# Sedai: Platform Full content of 43 pages from https://sedai.io/platform. Each entry starts with the page title and its source URL. Every full-content file is listed in https://sedai.io/llms.txt. --- # Amazon RDS Source: https://sedai.io/platform/amazon-rds-optimization Sedai continuously analyzes Amazon RDS and autonomously rightsizes instances through maintenance windows — safe enough for production databases. ## RDS Rightsizing That's Actually Safe to Act On Most teams avoid resizing production databases — the risk feels too high and the analysis too complex. Sedai continuously analyzes your RDS fleet and applies instance rightsizing changes autonomously, using maintenance windows and incremental adjustments to keep risk minimal. Sedai is an autonomous cloud optimization platform that continuously analyzes Amazon RDS fleets and autonomously rightsizes instances, optimizes storage configurations, and identifies unused resources. Unlike recommendation-only tools, Sedai applies changes through maintenance windows with incremental adjustments — making it safe enough for production databases. It supports RDS, Aurora, Multi-AZ deployments, and multi-account environments. ## Nobody Touches a Production Database That's Working. RDS handles the operational overhead of running a managed database, but the resource decisions that drive cost and performance still belong to your team. Instance sizing, storage configuration, and replica strategy are all difficult to revisit safely once a database is carrying real production traffic. ### Instances get provisioned for peak load and stay there. Rightsizing a production database means accepting regression risk on a system with high blast radius and slow recovery. Most teams make the rational choice to leave it alone — and pay for unused headroom indefinitely. ### Storage configuration involves tradeoffs that aren't obvious at provisioning time. Allocated storage can grow but can’t shrink in place, so every overestimate becomes permanent spend. Volume type and provisioned IOPS add tradeoffs, and getting them wrong means overpaying for throughput or hitting I/O limits. ### Idle infrastructure compounds quietly in the background. Dev and staging instances running outside business hours, read replicas with minimal traffic, databases for deprioritized projects — these accumulate across accounts without triggering alerts. The cost shows up in the bill; the cause rarely surfaces without a dedicated audit. ## How We Help ### Instance Rightsizing Sedai analyzes utilization at the 95th percentile to identify the optimal instance type and applies changes autonomously during scheduled maintenance windows to minimize production risk. ### Storage Optimization Sedai evaluates volume type, provisioned IOPS, and throughput against actual usage, and recommends the storage configuration that meets performance requirements at the lowest cost. That includes gp2-to-gp3 migrations and trimming over-provisioned IOPS. ### Unused Resource Cleanup Sedai identifies inactive RDS instances and read replicas with no traffic, flagging them for decommissioning before they quietly accumulate months of unnecessary spend. ## Sedai vs AWS Compute Optimizer | AWS Compute Optimizer | Sedai | | --- | --- | | Recommends instance changes; you execute manually | Analyzes, decides, and applies changes autonomously through maintenance windows | | Analyzes CPU and memory only; no storage or IOPS optimization | Optimizes instance type, storage configuration, and provisioned IOPS together | | Point-in-time recommendations that go stale as workloads shift | Continuous analysis with 95th-percentile modeling that adapts to traffic changes | | No awareness of maintenance windows or deployment topology | Schedules changes during maintenance windows; handles Multi-AZ failover timing | | No action on idle resources | Identifies inactive instances and read replicas with cost impact and connection history | ## Production Databases Deserve Better Than Manual Guesswork. ## Frequently Asked Questions ### How does Sedai avoid causing performance issues when rightsizing a production database? Sedai analyzes utilization at the 95th percentile rather than average usage, ensuring recommendations preserve enough headroom for peak load. Changes are applied incrementally and scheduled during maintenance windows by default, minimizing the risk of impacting live traffic. ### Does Sedai support both standard RDS and Aurora? Yes. Sedai supports both RDS and Aurora deployments, including storage-specific optimizations for Aurora such as recommendations between Standard and I/O-Optimized storage configurations. ### How does Sedai handle Multi-AZ deployments? Sedai's rightsizing analysis accounts for Multi-AZ configurations. Instance type changes in Multi-AZ deployments involve a failover event, which Sedai schedules during the maintenance window to minimize disruption. ### Will Sedai delete unused RDS instances automatically? No. Decommissioning recommendations for inactive instances and read replicas require explicit approval. Sedai surfaces the opportunity with supporting context — connection history, cost impact — but deletion stays in human hands. ### Does Sedai support RDS across multiple AWS accounts and regions? Yes. Sedai analyzes and optimizes RDS deployments across multi-account and multi-region AWS environments, with consistent rightsizing and storage optimization applied across your full fleet. --- # GPU Optimization Source: https://sedai.io/platform/gpu GPUs are scarce. Sedai finds the capacity locked in idle allocations and underused GPUs and safely puts it to work, so you get more from what you already have. ## Do More With the GPUs You Have GPUs are scarce and buying more takes months. Sedai finds the capacity locked inside your fleet, from idle allocations to workloads holding full GPUs they only need a slice of, and safely puts it to work. More workloads per GPU. No disruption in prod. ## Before You Buy Another GPU, Get More From the Ones You Have Teams hold idle GPUs on purpose, because giving them back can mean waiting months to get them again. Sedai doesn’t ask you to let go. It finds the capacity standard metrics miss, then right-sizes and packs workloads so the GPUs you’re holding take the next job in the queue. ## GPU Workload, Node & Cluster Optimization Static GPU allocations lead to massive waste. Sedai's proprietary utilization model continuously adapts to real workload behavior, keeping GPU usage optimized even as your AI infrastructure evolves. ### Fit More Workloads per GPU Most inference and dev workloads don’t need a whole GPU. Sedai shares GPUs through MIG or time-slicing, picks the right configuration, and packs more workloads onto each device. ### Reclaim Idle GPUs Find GPUs that are allocated but doing nothing. Sedai releases them for other workloads, with the impact shown before and after every change. ### Consolidate GPU Node Pools Sedai packs scattered workloads onto fewer nodes, raising utilization and cutting node spend. Every scale-down is yours to approve. ## 100% Utilization Doesn’t Mean 100% Used Most tools rely on standard utilization metrics, such as those reported by NVIDIA System Management Interface (nvidia-smi). However, those metrics only tell you whether a GPU is doing *something*, not whether it's doing something *useful*. A GPU can show 100% utilization while performing zero productive computation. Sedai approaches this differently: - Proprietary utilization model infers true GPU usage from multiple telemetry signals - Models real workload behavior across compute, memory, and throughput dimensions - Provides a first-class utilization score that drives every optimization decision - Identifies waste that surface-level metrics consistently miss ## GPU Capacity & Cost Intelligence Most tools only show you where GPU spend goes. Sedai knows why it's happening and reduces it for you. ### Free Capacity You Already Own Before procuring new GPUs, reclaim the ones you have. Sedai continuously identifies underutilized devices and frees them for use, reducing procurement delays and queue times for AI teams. ### See Where Your GPUs Actually Go See exactly where GPU spend lives across workloads, node pools, and clusters. Sedai turns cost drivers into actions for measurable, ongoing savings. ### Waste Detection at Every Layer Find inefficiencies across workloads, nodes, and clusters. Sedai surfaces idle and over-allocated GPU capacity and removes it, safely and autonomously. “By having Sedai in place, we’re not just saving money. We’re preventing would-be customer problems, before they become an issue.” — Matt Duren, VP of Engineering // KnowBe4 ## How Sedai Optimizes GPU Infrastructure Safely Get safe, outcome-driven GPU optimization at scale, designed to act on real workload behavior, with safeguards built into every decision. ### Learns GPU Workload Behavior ### Decides With Context ### Acts Safely ## Autonomy That Delivers Powered by real app behavior. GPU capacity reclaimed Increase in effective utilization Less manual optimization work ## Optimize Your Entire GPU Stack Sedai makes your GPU infrastructure smarter and safer. ### Kubernetes Platforms ### Optimize GPU workloads across any Kubernetes distribution ### GPU Right-Sizing ### Slice and pack GPUs to eliminate waste ### Monitoring Integrations ### Plug into your existing observability stack ## Other Tools Automate. Sedai Acts With Real Context. | Other Solutions | Sedai | | --- | --- | | Rely on surface-level GPU utilization metrics | Models true GPU utilization from multiple telemetry signals | | Stop at dashboards and recommendations | Autonomously executes changes with built-in guardrails | | Lack workload-level GPU intelligence | Optimizes at the workload, node, and cluster level | | CPU-focused; GPU optimization is an afterthought | Purpose-built for GPU and AI infrastructure | | No path from recommendations to safe autonomous action | Datapilot → Copilot → Autopilot progression for safe autonomy | | Treat GPU efficiency as a cost line item | Turns underused GPUs into capacity for more workloads | ## Get More GPU Capacity Without the Procurement Wait ## FAQs ### How does Sedai determine true GPU utilization? Sedai uses a proprietary model that infers real GPU usage from multiple telemetry signals, going beyond the standard nvidia-smi utilization metric, which measures only whether a GPU is active, not whether it's doing productive work. ### Is it safe to let Sedai make autonomous GPU changes? Yes. All changes are governed by the same safety framework Sedai applies across the platform — incremental execution, continuous validation, and automatic rollback on drift. You can start with Datapilot recommendations and progress toward autonomy at your own pace. ### Which GPU capabilities support autonomous execution today? GPU Workload Deallocation and MIG Enablement & Packing support both Datapilot and Copilot modes at launch. GPU Node Pool Optimization is available in Datapilot mode, with Copilot support on the roadmap. ### Which Kubernetes platforms are supported? Sedai GPU Optimization supports any Kubernetes platform and distribution, including EKS, GKE, AKS, OpenShift, Rancher, VMware Tanzu, and more. ### Will GPU optimizations affect my AI workload performance? Sedai only acts where it has high confidence that changes are safe. Every optimization is validated against workload behavior and performance signals before and after execution. ### Can Sedai help us delay or avoid buying more GPUs? Yes. Most GPU fleets have significant capacity locked in idle allocations and workloads holding full GPUs they only need a slice of. Sedai finds that capacity and frees it for new workloads. That shortens queue times now and lets you put off new GPU purchases until you’ve gotten full value from the fleet you already own. ### How much GPU capacity can Sedai reclaim? It depends on your workloads and how your GPUs are allocated today. Sedai has driven up to 3x effective GPU utilization, meaning up to three times the productive work from the same hardware. ### If Sedai frees a GPU, do we lose it? No. In Kubernetes, freeing a GPU usually means right-sizing requests, bin-packing workloads, or sharing GPUs through MIG or time-slicing. The node stays in your pool, and the scheduler places your next pending workload on it. If your GPUs come through reserved capacity, cloud commitments, neocloud contracts, or your own clusters, that capacity stays yours either way. Node scale-downs are separate recommendations, and you decide whether to approve them. ### How quickly can I see results? Most customers begin seeing GPU cost savings and utilization improvements within the first few weeks of enabling the capability. ### How does GPU Optimization fit into the broader Sedai platform? GPU Optimization is built on the same observe → learn → act engine that powers Sedai's CPU, memory, and cluster optimization. Customers already using Sedai can enable GPU Optimization within their existing environment. --- # Amazon EKS Source: https://sedai.io/platform/amazon-eks-optimization Sedai continuously right-sizes Amazon EKS pods, nodes, and purchasing — eliminating waste while protecting availability, with cluster compaction built in. ## Optimize Amazon EKS Without the Guesswork Most EKS teams set resource requests once, based on worst-case estimates, and never revisit them, leaving clusters chronically overprovisioned and expensive to run. Sedai continuously right-sizes pods, nodes, and purchasing to eliminate waste while protecting availability. Sedai is an autonomous cloud optimization platform that continuously right-sizes Amazon EKS pods, nodes, and purchasing based on real workload behavior — not static rules or manual estimates. It works alongside Karpenter or Cluster Autoscaler, improving the signal quality your autoscaler relies on while eliminating the overprovisioning that drives up costs and corrupts scaling decisions. Sedai writes every change back to your Terraform, Helm, or Kustomize configs as a PR, so optimizations persist across deployments instead of drifting back. ## EKS Optimization Isn't a One-Time Task. It's a Compounding Problem. EKS removes control plane management — but node sizing, pod resource requests, and scaling configuration still fall on your team. Most clusters get provisioned for worst-case estimates and never revisited, creating downstream consequences that compound over time. ### Overprovisioned pods corrupt your autoscaler's signals. When resource requests are too high, Cluster Autoscaler adds nodes based on phantom demand — so oversized workloads drive an oversized cluster, and the two problems reinforce each other. ### Manual optimization at cluster scale isn't sustainable. Rightsizing pods across dozens of services requires ongoing analysis of actual behavior, not a one-time pass. By the time you finish, the workloads you started with have already drifted. ### Getting it wrong has direct reliability consequences. Too-low requests risk CPU throttling and OOM kills. Too-tight limits cause latency spikes under burst traffic. There's no safe direction to guess. ## How We Help ### Workload Right-Sizing Sedai autonomously tunes pod CPU and memory requests based on real consumption patterns — P95/P99, not worst-case estimates. Changes are applied during safe maintenance windows with rollback if error rates spike. No VPA required. ### Node & Cluster Optimization Selects optimal instance types, redistributes workloads across nodes, and applies cluster compaction to eliminate underutilized nodes. Works with both Cluster Autoscaler and Karpenter — improving their decisions by feeding accurate pod requests upstream. ### Cost Visibility & Purchasing Maps workload-level cost attribution across compute, GPU, storage, and network. Recommends the right mix of on-demand, Savings Plans, Reserved Instances, and Spot based on actual usage stability — not blanket commitments. ### IaC-Native Changes Every optimization is written back to Terraform, Helm, or Kustomize as a pull request. Changes persist across deployments. No config drift. No "optimization that disappears on the next rollout." ## Sedai vs Karpenter/EKS Auto Mode | Karpenter/EKS Auto Mode | Sedai | | --- | --- | | Provisions nodes based on pod requests. If those requests are wrong (and they usually are), Karpenter scales the wrong infrastructure — efficiently. It doesn't analyze actual workload behavior, adjust pod resources, or optimize purchasing. Node-level provisioning without pod-level intelligence compounds overprovisioning. | Continuously right-sizes pod CPU and memory based on real consumption, then feeds accurate requests to Karpenter so it provisions the right nodes. Adds cluster compaction, purchasing optimization (Spot, Savings Plans, Reserved), and writes every change back to IaC as a PR. Karpenter handles where pods run. Sedai handles what they actually need — and keeps it current. | ## Stop Setting It and Forgetting It. ## Frequently Asked Questions ### Does Sedai support both managed node groups and self-managed nodes? Yes. Sedai works with EKS managed node groups, self-managed EC2 node groups, and clusters using Karpenter for node provisioning. Optimization recommendations and autonomous actions adapt based on how your cluster is configured. Note: this refers to Sedai's core optimization capabilities, which adapt to your node provisioning setup. The Karpenter Migration Assistant, which automates migrating existing node groups to Karpenter, currently supports EKS Managed Node Groups only — self-managed node groups and Fargate aren't yet supported. ### How does Sedai interact with Cluster Autoscaler or Karpenter? Sedai complements your autoscaler rather than replacing it. By ensuring pod resource requests accurately reflect actual consumption, Sedai improves the signal quality that Cluster Autoscaler and Karpenter use to make scaling decisions — reducing over-provisioning at the node level as a result. For EKS teams still on Cluster Autoscaler and Managed Node Groups, the Karpenter Migration Assistant can also automate the move to Karpenter itself — handling compatibility checks and a safe, phased cutover so you don't have to do it manually. ### Will Sedai's changes affect running workloads or cause pod restarts? Resource request and limit changes in Kubernetes do require pods to be restarted to take effect. Sedai schedules these changes to minimize disruption — respecting PodDisruptionBudgets, replica counts, and traffic patterns — and monitors stability after each change. If a restart causes instability, Sedai rolls back automatically. ### Do you support Fargate profiles on EKS? Yes. Sedai supports EKS clusters using Fargate profiles alongside EC2 node groups. Optimization behavior adapts to the Fargate execution model, where node-level configuration isn't applicable and right-sizing focuses on pod-level resource allocation. ### How long does it take before Sedai starts making changes? Sedai observes your cluster before acting. The profiling period typically runs 2–4 weeks, during which Sedai builds behavioral baselines for your workloads. Recommendations become visible within the first week; autonomous actions begin once confidence thresholds are met. --- # Kubernetes Autoscalers Source: https://sedai.io/platform/kubernetes-autoscalers Sedai right-sizes pods and tunes HPA targets based on real workload behavior — filling the gaps native autoscalers leave behind, without conflict. ## The Intelligence Layer Your Autoscalers Are Missing HPA scales pods, Karpenter provisions nodes, but neither knows whether your workloads are actually sized correctly. Sedai continuously right-sizes pod requests and tunes scaling targets based on real workload behavior, so your autoscalers are always working from accurate inputs. ## Autoscalers Scale Load. They Don't Fix Sizing. Autoscalers like HPA and Karpenter are reactive by design. The inputs they scale from are usually wrong before scaling even begins. ### Resource requests are set once and inherited blindly. Estimates go stale, but HPA still scales from whatever they say. ### VPA fights with HPA in live clusters. Vertical and horizontal autoscaling conflict at runtime, so most teams disable VPA in production or limit it to off-hours, leaving right-sizing unaddressed. ### Node provisioning inherits pod-level inefficiency. Karpenter bins pods onto nodes based on declared requests, so inflated requests mean larger nodes, lower density, and higher spend regardless of actual utilization. Sedai is an autonomous Kubernetes optimization platform that continuously right-sizes pod resource requests and tunes HPA scaling targets based on real workload behavior and SLOs. Unlike native autoscalers that scale from static or stale inputs, Sedai learns from live production data to eliminate resource waste, resolve HPA-VPA conflicts, and improve Karpenter bin-packing — without manual tuning or pod restarts. ## Key Capabilities Sedai adds an intelligence layer at both the pod and node level, filling the gaps that leave native autoscalers guessing. ### Set the Right HPA Target, Not Just a Guess Sedai analyzes workload behavior and SLOs to autonomously set the optimal HPA target, balancing pod size against replica count for the most cost-effective configuration. ### Get Vertical and Horizontal Scaling at the Same Time Kubernetes blocks HPA and VPA from running together. Sedai's vertical scaling works alongside HPA, safely adjusting pod requests and limits in small increments — conflict-free. ### Help Karpenter Pack Nodes More Efficiently Oversized pods waste node capacity. Sedai right-sizes pods first so Karpenter bin-packs onto fewer, cheaper nodes, and recommends instance types based on usage and pricing. ### Consolidate, With or Without Migrating to Karpenter Whether you stay on Cluster Autoscaler or move to Karpenter, Smart Scheduler delivers the consolidation benefits either way — and when you're ready to migrate, the Migration Assistant handles the cutover safely. ### Move to Karpenter Without the Risk Already decided Karpenter's the right call? The Migration Assistant automates the cutover from EKS Managed Node Groups, node pool by node pool — with a pre-flight compatibility check and automatic rollback built in. “We’ve gone from what used to be automated, deterministic workflows to autonomous, with Sedai. The human element is indispensable, and it always will be. But more and more engineering toil is being done by AI, so as humans, we can move up the value chain. That’s how we can deliver what our customers expect of us.” — Suresh Sangiah, SVP of Engineering // Palo Alto Networks ## Works With Your Stack ### Horizontal Pod Autoscaler (HPA) ### Vertical Pod Autoscaler (VPA) ### Karpenter ### Cluster Autoscaler ### KEDA ## The Sedai Difference | Without Sedai | Sedai | | --- | --- | | HPA utilization targets are set manually and rarely revisited | Sedai continuously determines and tunes the optimal HPA target based on live workload behavior | | Kubernetes prevents using HPA and VPA on the same workload | Sedai's vertical scaling works alongside HPA simultaneously, without the native conflict | | Oversized pods force Karpenter to provision larger, more expensive nodes | Right-sized pods let Karpenter pack workloads onto fewer nodes at lower cost | | Node autoscalers pick from the full instance catalog without guidance | Sedai recommends a targeted instance type list so autoscalers make more cost-effective selections | | Scaling decisions are based on infrastructure metrics alone | Sedai factors in application performance and SLOs, ensuring scaling doesn't compromise reliability | | Node consolidation without Karpenter requires manual tuning — and migrating to Karpenter yourself means navigating config translation and cutover risk on your own. | Smart Scheduler delivers Karpenter-style consolidation whether or not you migrate — and when you're ready to move, the Migration Assistant automates the cutover safely. | ## Resources ## Smarter Inputs. Better Scaling. Lower Costs. ## Frequently Asked Questions ### What does Sedai do for Kubernetes autoscalers? Sedai continuously right-sizes pod resource requests and tunes HPA scaling targets based on real workload behavior. It works alongside your existing HPA, VPA, Karpenter, Cluster Autoscaler, and KEDA configurations without replacing them. ### Can Sedai run VPA and HPA at the same time? Yes. Native Kubernetes blocks HPA and VPA from running together. Sedai's vertical scaling works alongside HPA, adjusting pod requests and limits in small increments without the native conflict. ### How does Sedai improve Karpenter? Sedai right-sizes pods so Karpenter bin-packs more efficiently, and for EKS teams still on Cluster Autoscaler, the Karpenter Migration Assistant automates a safe move to Karpenter itself. ### Does Sedai require pod restarts? No. Sedai applies right-sizing changes in small, safe increments during normal pod lifecycle events. It does not force restarts or evictions. ### Which Kubernetes environments does Sedai support? Sedai supports EKS, AKS, GKE, OpenShift, Rancher, and self-managed clusters. It works with any combination of HPA, VPA, Karpenter, Cluster Autoscaler, and KEDA. --- # IaC & CI/CD Source: https://sedai.io/platform/iac-cicd Sedai writes optimizations back to Terraform, Helm, or Kustomize as PRs — eliminating config drift and protecting gains across every deployment. ## Fix IaC Drift at the Source Sedai fits your IaC and GitOps workflow — writing optimizations back to Git as reviewable PRs, or, on Kubernetes, applying them without ever touching your deployment blueprint. ## Your IaC Workflow Wasn't Designed for Autonomous Systems Git-as-source-of-truth works well when humans are the only ones making changes. Autonomous systems change that. ### Live changes create invisible debt. Adjustments made directly to a running cluster exist outside your declared state — and get reverted on the next reconciliation. ### Manual promotion doesn't scale. Translating live changes back into IaC by hand is slow, error-prone, and rarely happens consistently. ### Policy lives outside your review process. Guardrails configured outside code have no version history, no peer review, and no audit trail. Sedai is an autonomous cloud optimization platform that writes every infrastructure change back to your IaC — Terraform, Helm, or Kustomize — as a pull request. Unlike drift detection tools that only alert on discrepancies, Sedai generates the optimization, applies it safely with validation and rollback, then syncs the result to Git so your declared state never falls behind your live environment. ## Optimization That Lives in Your Repository Sedai integrates directly with your IaC and GitOps workflows, so every change it makes is reviewable, auditable, and permanent. ### Drift Prevention After every optimization, Sedai opens a pull request with the exact code changes needed — Terraform variables, Helm values, Kustomize patches. Your declared state stays current autonomously. ### Deployment Resilience Sedai's CI/CD overwrite protection detects when a new deployment would revert an optimization and re-applies the correct configuration. Your gains don't disappear at the next push. ### Policy in Version Control Define the guardrails for autonomous optimization in Git. Sedai reads and enforces those boundaries from your repository — reviewed, versioned, and managed like any other policy file. ### No Manual Translation Sedai generates the exact code changes required. Engineers review a PR, not a recommendation. Sedai has already written the optimization; it just needs an approval. ## Set the Rules Once. Enforce Them Everywhere. Sedai lets you define CPU, memory, replica, & cost boundaries directly in Git as policy files, with no portal configuration required. Guardrails are versioned alongside your application code, and they're subject to the same review & approval process. ## Every Optimization Becomes a Pull Request After Sedai applies a change to your live environment, it autonomously opens a PR with the corresponding IaC changes. This can take the form of Terraform .tfvars, Helm values.yaml, or Kustomize patches. The PRs include a summary of what changed, the "why" behind it, and the projected impacted, reviewable by any engineer without Sedai access. ## Optimizations That Survive Every Deployment Sedai monitors for deployment events that would overwrite a previous optimization. When Sedai detects a conflict, it re-applies the correct configuration after the deployment completes — without manual intervention. For GitOps environments running ArgoCD or Flux, the Live Sync Controller intercepts reconciliation conflicts before they cause drift — on Kubernetes, this covers the direct-edit model; Pod Interceptor avoids the conflict at its source instead. ## For Kubernetes, Optimize Without Ever Touching Your Blueprint On Kubernetes, Sedai can apply optimizations directly to the pod at creation — without touching the deployment spec your GitOps tool watches, so there's nothing to detect as drift. Guardrails as Code and optional IaC loopback both still apply. Pod Interceptor is Sedai's default Kubernetes execution model; the direct-edit model above remains available. ## Know Exactly What Code Controls What Infrastructure Sedai's IaC Mapping Agent autonomously traces live cluster resources back to their originating IaC source: Terraform module, Helm release, or Kustomize overlap Sedai maintains mapping continuously as your stack evolves, without manual annotation or tagging. “What I found with Sedai is that it's very focused on safety. I don't really have to worry about it. It just delivers the optimization, in a very gradual and safe way.” — Robert Berger, Chief Architect // Informed ## Works With Your Stack ### Terraform ### Helm ### Kustomize ### GitHub ### GitLab ### Bitbucket ### ArgoCD ### Flux ## The Sedai Difference | Without Sedai | Sedai | | --- | --- | | Optimizations applied to live infra are overwritten by the next deployment | Sedai writes changes back to IaC as PRs, so optimizations survive every deploy | | Engineers manually translate recommendations into Terraform or Helm code | Sedai generates the exact code changes required, no manual translation | | Configuration drift accumulates silently between code and live environment | Code and live environment stay in sync automatically after every optimization | | Changes bypass code review, leaving no audit trail | Every optimization follows the same PR-based approval process as any other change | | GitOps tools like ArgoCD revert Sedai's changes on reconciliation | On Kubernetes, Pod Interceptor applies changes without ever triggering GitOps reconciliation; Live Sync Controller handles it everywhere else | ## Resources ## Optimization That Lives in Your Code, Not Your Cluster. ## Frequently Asked Questions ### What is IaC drift and why does it matter? IaC drift is when your live infrastructure diverges from the state defined in your Terraform, Helm, or Kustomize files. It means your code is no longer the source of truth — deployments become unpredictable, and optimizations get silently reverted. ### How does Sedai prevent IaC drift? After every optimization, Sedai opens a pull request with the exact IaC changes — Terraform variables, Helm values, or Kustomize patches. Your declared state stays current after every change, not just after manual updates. ### What happens when a deployment overwrites a Sedai optimization? Sedai's CI/CD Overwrite Protection detects deployment events that would revert a previous optimization and re-applies the correct configuration after the deployment completes. For ArgoCD or Flux environments, the Live Sync Controller intercepts reconciliation conflicts before they cause drift. ### How is Sedai different from drift detection tools like Spacelift or env0? Drift detection tools alert when infrastructure diverges from code and revert to the last-known state. Sedai goes further: it generates optimizations, applies them safely, and writes the results back to your repository as PRs — so your IaC improves over time instead of just staying frozen. ### Does Sedai work with GitOps workflows? Yes. Sedai integrates with ArgoCD and Flux through its Live Sync Controller, which intercepts reconciliation conflicts. It also works with GitHub, GitLab, and Bitbucket for PR-based IaC updates across Terraform, Helm, and Kustomize. --- # Kubernetes Optimization Source: https://sedai.io/platform/kubernetes Kubernetes optimization to reduce costs, improve performance, and protect SLOs. Continuously tune clusters without manual scaling or effort. ## Kubernetes Optimization You Can Trust in Prod Sedai doesn’t just recommend or automate Kubernetes optimizations. It makes safe, continuous changes for you based on real workload behavior. All without breaking prod. ## Sedai Ranks #1 in Key Features for Kubernetes Management! ## Optimize Kubernetes with Superintelligence Sedai's AI continuously learns your workload patterns, applying intelligent optimizations in real time — no human intervention required. It thinks ahead so your infrastructure runs smarter, not just leaner. ## Kubernetes Workload, Node, & Cluster Optimization Rule-based automation only works until workloads change. Sedai’s ML constantly adapts, keeping Kubernetes optimized even when behavior evolves. ### Autonomous Workload Rightsizing Continuously tunes pod CPU & memory based on real workload behavior. No static requests, limits or thresholds. ### Smarter Node Utilization Smart Scheduler continuously packs workloads onto the nodes that fit them best, based on real utilization patterns — reducing waste while keeping services stable. ### Cluster-Level Optimization Smart Scheduler evaluates your cluster continuously, not just at the pod or node level — consolidating workloads onto fewer nodes automatically, with no manual step required. ## Autoscalers Tuned by AI. Not Guesswork. Sedai doesn’t replace your Kubernetes autoscalers. It makes them more effective. It tunes policies from real behavior and SLOs, not static thresholds or manual guesswork. Sedai keeps your autoscalers in line: - HPA, VPA, KEDA and Cluster Autoscaler policy tuning - Eliminates thrash and overreaction - Coordinates autoscaling with autonomous rightsizing actions - Applies safe, SLO-based guardrails ## Kubernetes Cost & Capacity Intelligence Most tools only show you where Kubernetes spend goes. Sedai actually knows why it’s happening and reduces spend for you. ### Actionable Kubernetes Cost Visibility See exactly where Kubernetes spend lives, and how optimizations reduce cost over time. Sedai turns cost drivers into actions for measurable, on-going savings. ### Right Capacity, Right Commitment Base capacity and purchasing on real workload behavior, not fixed estimates. Sedai matches what you buy to the right resources at the right time. ### Waste Detection at Every Layer Find inefficiencies across pods, nodes, and clusters. Sedai shows idle and over-provisioned capacity and removes it autonomously. “Sedai has helped us save millions by optimizing and managing our own back-end services. But most importantly, Sedai has allowed us to respond in real time when anomalies are detected.” — Suresh Sangiah, SVP of Engineering // Palo Alto Networks ## How Sedai Optimizes Kubernetes Safely Get safe, outcome-driven optimization at scale. Designed to act on real application behavior, with safeguards built into every decision. ### Learns Application Behavior ### Decides With Context ### Acts Safely ## Autonomy That Delivers Powered by real app behavior. Cloud Savings Performance Gain Failed Customer Interaction Reduction ## Optimize Your Entire Stack Sedai makes your stack smarter and safer. ### Node Optimization ### Supported Kubernetes platforms & container runtimes. ### Workload Optimization ### Supported workload-level autoscaling & orchestration technologies. ## Other Tools Automate. Sedai Acts With Real Context. | Other Solutions | Sedai | | --- | --- | | Metric-driven optimization, not behavior-aware | Behavior- and SLO-based optimization for accuracy | | Recommend changes instead of executing | Autonomously rightsizes and scales workloads | | Rule-based automation risks regressions & outages | Verifies impact continuously and rolls back on drift | | Tune Kubernetes in isolation, creates bottlenecks | Optimizes Kubernetes in full cloud context | | Limited controls for enterprise workflows | Operates within IaC, ITSM, and policy guardrails | ## Resources ## Optimize Kubernetes On Autopilot ## FAQs ### How safe is the Kubernetes optimization process? Sedai is built for production safety. Its patented optimization engine learns normal workload behavior and only applies incremental changes within cluster constraints. It then validates every action against live signals, rolling back automatically if performance drifts. Sedai has safely executed millions of optimizations across some of the world’s largest enterprises. It’s proven reliability at massive scale. ### How does Sedai ensure optimizations won’t impact performance or availability? Sedai builds a baseline of each workload’s normal behavior and only applies changes that don’t affect performance and availability. It continues monitoring afterward and can roll back if it detects early degradation. ### How does optimization work at the workload and infrastructure level? Sedai adjusts container CPU and memory based on real behavior. On supported clusters, Sedai can make these updates without restarting the pods. At the infrastructure level, Smart Scheduler continuously reviews node capacity, utilization, and placement to consolidate workloads and remove unnecessary nodes automatically. ### What specific configuration changes does Sedai make? Sedai automatically tunes Kubernetes resources based on real workload behavior. It adjusts vertical and horizontal scaling, including CPU, memory, replica counts, & scaling policies at the pod & container level. There's no need for manual tuning. At the infrastructure layer, Sedai selects the right instance types based on application-level latency requirements. With Smart Scheduler, Sedai goes beyond traditional autoscalers by continuously and automatically redistributing workloads and safely decommissioning unused nodes. You can unlock additional savings without compromising performance or reliability. ### What metrics does Sedai monitor? Sedai monitors CPU and memory usage with service-level signals like latency, traffic, errors, and saturation. ### Can I see optimization recommendations before they’re applied? Yes. Sedai offers multiple operating modes so you stay in full control. In Datapilot mode, Sedai shows recommendations without taking action. In Copilot mode, you review and approve optimizations before they’re applied. In Autopilot mode, Sedai applies safe, validated optimizations automatically. You can use different modes for different workloads or environments, letting you run Autopilot in production while keeping review-only modes in dev or staging. ### Can I control which resources Sedai optimizes? Yes. You can choose which clusters or workloads Sedai optimizes and which stay in recommendation-only mode. Sedai only makes changes where you’ve explicitly allowed it. ### How does Sedai pricing work for Kubernetes environments? Sedai’s pricing is based on the number of vCPUs under management. You only pay for the Kubernetes resources Sedai actively optimizes, not dashboards, seats, or passive monitoring. This vCPU-aligned model gives you predictable costs as your environments scale, with flexible tiers for dev, staging, and production. ### How does Sedai integrate with my Kubernetes environment? Sedai connects through the Sedai Smart Agent using standard Kubernetes APIs. It requires no deployment changes and only acts within your set permissions. ### Do I need to change our existing monitoring setup? No. Sedai works with the metrics and signals already exposed by your Kubernetes cluster and services. Sedai runs with your existing monitoring tools. ### How long does implementation take? We make setting up Sedai fast and simple. Onboarding usually takes a few hours once permissions are set. Even for environments with 20+ clusters. After implementation, Sedai needs about 2–4 weeks to learn your workloads and traffic patterns. Talk to our cloud experts for a personalized estimate of Sedai's time to value. ### Can Sedai optimize large-scale Kubernetes environments? Yes. Sedai can optimize large and distributed Kubernetes environments. It uses the same metrics and safety checks at any scale. No need to restructure your clusters. ### How much can I actually save on Kubernetes costs? Sedai dramatically reduces your Kubernetes costs. Our customers typically achieve a 30–40% cost reduction, plus improvements in performance & availability. Talk to our cloud experts for a personalized estimate of how much Sedai can save you. --- # MCP Integration Source: https://sedai.io/platform/mcp Sedai's MCP integration brings the same capabilities available in Sed into your own agents — Claude Code, Copilot, or internal tooling — with no custom integration. ## Bring Sedai Into the Agents You Already Use Sedai's MCP (Model Context Protocol) integration puts the same capabilities available in Sed — Sedai's AI assistant — directly into your own agents. No custom integration, no separate login: just add Sedai as a tool your agent already knows how to use. ## If Your Agents Can't Reach Sedai, They're Missing Part of the Picture Knowledge about your cloud and AI infrastructure is scattered — cost data in one tool, optimization opportunities in another, resource configuration somewhere else. Increasingly, teams are pulling that picture together with their own agents. Sedai should be one of the tools those agents can reach. ### Custom integrations don't scale. Wiring your own agent or internal tooling into another platform's API usually means building and maintaining a bespoke integration for each one. ### Agent-to-agent workflows need a common interface. As more of the day-to-day work moves through agents talking to other agents and systems, a platform that's only reachable through its own UI becomes a dead end in that workflow. ### A platform's capabilities shouldn't lag behind its own product. Some integrations are narrow, bolt-on developer utilities — separate from, and slower to update than, the vendor's own in-product experience. ## Key Capabilities Sedai's MCP server exposes the same tools and skills that power Sed, so your own agents get the same capabilities Sedai's assistant has — nothing held back. ### Pull Information From Sedai Ask your agent to check optimization status, pull savings data, or surface opportunities from Sedai — no dashboard required. ### Take Action Through Sedai Ask your agent to kick off the same actions available in Sed — adjusting settings, managing resource groups — as part of a larger workflow. ### Plug Into What You Already Run Add Sedai as an MCP tool in Claude Code, Copilot, or your own internal agent tooling, alongside whatever else is already in your stack. ### Grow Automatically With Sed Sed and MCP share one underlying capability layer. As Sed gains new skills, they become available through MCP too, with no separate roadmap or lag. ## One Capability Layer, Two Surfaces Everything available through Sedai's MCP integration is also available in Sed, Sedai's AI assistant — and vice versa. There's no separate roadmap and no feature gap between the two; they're built from the same underlying tools and skills. ## Works With Your Tools Sedai's MCP integration works with any MCP-compliant client, including: ### Claude Desktop ### Claude Code ### Cursor ### Windsurf ### VS Code (GitHub Copilot) ### ChatGPT / Codex ### OpenCode ### Cline ## See Sedai's MCP Integration in Your Own Workflow --- # Release Intelligence Source: https://sedai.io/platform/release-intelligence Sedai auto-scores every deployment against a pre-release baseline — attributing cost, latency, and error impact to the exact change that caused it. ## Know the Impact of Every Release Teams deploy fast, but visibility into what each release does to cost, performance, and reliability lags far behind. Sedai automatically scores every deployment against a pre-release baseline, attributing impact to the exact change that caused it. Ship with confidence and stop guessing when something goes wrong. ## Automatic Feedback on Every Deployment Sedai detects every release the moment it happens, measures its real-world impact, and scores it, without any manual instrumentation or configuration. ### Detect Every Change Sedai automatically identifies new releases when a container image or workload configuration changes — no tagging or manual triggers required. ### Measure Against Baseline Sedai captures pre-release performance, cost, and error metrics, then monitors the new version for up to 48 hours to measure meaningful deviation. ### Score and Attribute Every release receives a scorecard rating across cost, latency, and errors, so teams know immediately whether a deployment helped, hurt, or was neutral. “The biggest change I've seen with Sedai is that my teams are able to work on a lot more valuable projects." — Matt Duren, VP of Engineering // KnowBe4 ## Key Capabilities Release Intelligence goes beyond deployment logs, giving teams the attribution, trends, and workflow integration needed to continuously improve release quality. ### Release Scorecards Clear, opinionated grades for every deployment — good, neutral, or bad — so teams can act fast without digging through metrics manually. ### Quality Trend Tracking Monitor how release quality evolves for each service over time, making it easy to spot regressions in engineering practices before they compound. ### Release Frequency Insights Track deployment velocity alongside quality to understand whether shipping faster is coming at the cost of stability. ### Native CI/CD Integration Plugs directly into your existing deployment pipelines and change management tools to enrich release context and trigger alerts automatically. ## Real Outcomes ## Resources ## Every Release Tells a Story. Sedai Reads It For You. --- # Safety By Design Source: https://sedai.io/platform/safety-by-design Sedai wraps every autonomous action in layered guardrails — validating, executing, and reversing changes safely. Zero production incidents, ever. ## Sedai is Patented to Optimize Safety Most cloud teams avoid automation in production because the risk outweighs the reward. Sedai's safety-by-design architecture wraps every autonomous action in layered guardrails built from deep application learning. Optimize continuously, without ever putting production at risk. ## Automation Without Guardrails Just Fails Faster Most teams don't resist automation because they prefer doing things manually. They resist it because they've seen what happens when an automated change goes wrong in production — an outage, a degraded SLA, an incident that takes hours to trace back to its cause. ### Static automation doesn't adapt or understand context. Rules-based automation executes if/then logic regardless of traffic shifts, deployment stability, or dependency load — it wasn't built to know when a change is risky. ### Some tools stay recommendation-only because they haven't earned autonomy. Several AIOps platforms limit themselves to suggestions — not because autonomy isn't valuable, but because the safety mechanisms aren't there yet. ### Manual operations don't scale either. Human oversight is safe but slow, creating delayed response, inconsistent execution, and alert fatigue in environments that change continuously. “What I found with Sedai is that it's very focused on safety. I don't really have to worry about it. It just delivers the optimization, in a very gradual and safe way.” — Robert Berger, Chief Architect // Informed ## Safety Built Into Every Action Sedai validates, executes, and monitors every change through a multi-layered safety process — before, during, and after every action. ### Learn Before You Act Sedai profiles your applications for weeks before acting — mapping dependencies and establishing what "healthy" looks like for each service. ### Validate Before You Execute Before every change, Sedai runs a multi-point safety check across impact, timing, dependencies, and conflicts. If any check fails, it waits. ### Reverse Before Harm Occurs If post-change SRE signals move in the wrong direction, Sedai automatically rolls back — no ticket, no page, no human required. ## Built for Production Trust ## Key Capabilities Sedai designs every layer of its safety architecture to make autonomous action more reliable than manual intervention. ### Topology-Aware Dependency Mapping Understands which services are upstream or downstream of every change, preventing actions that are locally safe but systemically risky. ### Incremental Change Execution Applies changes in small steps, validating stability after each one before proceeding. ### Conflict Detection Checks for in-flight changes across your environment before acting, preventing race conditions and compounding changes. ### Configurable Autonomy Start in Copilot mode with one-click approvals. Graduate to full Autopilot when you're ready. ## Resources ## The Safest Environmental Change Is One Sedai Makes. --- # Proactive Incident Prevention Source: https://sedai.io/platform/proactive-incident-prevention Sedai detects resource risks before thresholds breach and autonomously remediates — no pages, no tickets. Shift from firefighting to prevention. ## Fix Problems Before Users Find Them Static alerting thresholds fire too late, too often, or both, leaving your team in permanent reaction mode. Sedai analyzes real-time resource trends to identify what's about to go wrong, and autonomously intervenes before it becomes an incident. Shift from firefighting to prevention without adding headcount. ## From Reactive Alerts to Autonomous Prevention Sedai doesn't wait for thresholds to breach. It monitors the trajectory of your workloads in real time and acts before a trend becomes an outage. ### Establish What Normal Looks Like Sedai monitors your workload metrics over time to build a precise baseline of normal behavior, so it knows the difference between high utilization and a genuine risk. ### Predict What's About to Go Wrong Regression models analyze the direction of resource usage, not just the current level. Sedai flags a memory trend climbing from 40% to 80% in minutes, even if it hasn't crossed a threshold yet. ### Intervene Before Users Are Impacted When Sedai confirms a risk, it autonomously remediates — increasing memory limits, expanding CPU headroom, or scaling capacity — without waiting for a human to respond. “By having Sedai in place, we’re not just saving money, we’re preventing would-be customer problems before they become an issue.” — Matt Duren, VP of Engineering // KnowBe4 ## Key Capabilities Sedai covers the full spectrum of availability risks — from resource exhaustion to application-level failures — with autonomous remediation at every layer. ### OOM Prevention Detects upward memory trends in Kubernetes pods and Lambda functions and increases allocation before an out-of-memory event occurs. ### CPU Throttling Prevention Identifies containers approaching their CPU limits and expands headroom before performance degrades. ### Memory Leak Mitigation Provides autonomous temporary relief for memory leaks — keeping services available while your team diagnoses the root cause, without the pressure of an active incident. ### Trend-Based Alerting Replaces noisy static thresholds with trajectory-aware signals — fewer false positives, and early warning on the risks that actually matter. ## The Case for Autonomous Action ## Resources ## Stop Responding to Incidents. Start Preventing Them. --- # Rancher Source: https://sedai.io/platform/rancher-optimization Sedai continuously rightsizes workloads across every cluster Rancher manages — extending Rancher's centralized control to autonomous, cluster-wide optimization. ## Multi-Cluster Control Without Multi-Cluster Guesswork Rancher centralizes multi-cluster management, but not resource tuning. Sedai extends that same centralized control to continuous, autonomous rightsizing across every cluster Rancher manages. ## On-Prem Kubernetes Wasn't Built to Tune Itself Rightsizing self-managed clusters still relies on static estimates and manual effort — with no cloud elasticity to absorb the guesswork. ### Static Sizing Wastes Capacity Workloads run on worst-case CPU and memory requests that are set once and rarely revisited. ### Manual Tuning Doesn't Scale Rightsizing by hand is slow, guesswork-driven, and risky at the pace modern clusters change. ### Toolchains Don't Optimize Themselves HPA and GitOps controllers execute configs but can't tune themselves for cost or performance. ## How We Help ### Autonomous Workload Optimization Sedai continuously analyzes utilization and performance to safely adjust pod and container resource allocations — using reinforcement learning to move workloads toward their optimal state. ### GPU Optimization For AI, ML, and HPC workloads, Sedai identifies idle GPU capacity and right-sizes allocations to match real demand, cutting a major cost driver. ### Works With Your Existing Toolchain Sedai doesn't replace HPA or your GitOps controller — it refines their configurations continuously, connecting via a lightweight agent or GitOps integration to fit your security needs. ## Optimize On-Prem Kubernetes Like It's Cloud-Native. ## Frequently Asked Questions ### Does Sedai replace our HPA or GitOps controller? No. Sedai integrates with your existing HPA and GitOps tooling (like ArgoCD or Flux) and continuously refines their configurations based on real workload behavior — it doesn't replace your operational stack. ### How does Sedai connect to a self-managed or air-gapped cluster? You can deploy a lightweight in-cluster agent or use a GitOps integration, where Sedai generates configuration files for your GitOps controller to apply — no direct external access required. ### What level of control do we have over changes Sedai makes? Sedai supports three modes: Datapilot for read-only recommendations, Copilot for human-approved changes, and Autopilot for full autonomous execution — so you choose how much control to hand over, per environment. ### Can Sedai be deployed fully within our own environment, including air-gapped? Yes. Sedai's self-hosted model deploys the entire platform inside your own Kubernetes cluster — public cloud, private cloud, or on-prem — so no data ever leaves your network, and it can run fully air-gapped. Capabilities are identical to the SaaS model; your team manages infrastructure and applies updates that Sedai packages for you. ### Does Sedai support FedRAMP-regulated environments? Yes. While Sedai's SaaS offering is not FedRAMP certified, the self-hosted model can be deployed entirely within your own FedRAMP-certified environment, with full optimization capabilities and no data leaving your network. --- # OpenShift Source: https://sedai.io/platform/openshift-optimization Sedai brings autonomous optimization to OpenShift with self-hosted, FedRAMP-compatible deployment — so tuning never outpaces your governance requirements. ## Optimization That Doesn't Outpace Compliance OpenShift is built for compliance-heavy industries. Sedai brings autonomous optimization to that same environment — with self-hosted, FedRAMP-compatible deployment, so tuning never outpaces your governance requirements. ## On-Prem Kubernetes Wasn't Built to Tune Itself Rightsizing self-managed clusters still relies on static estimates and manual effort — with no cloud elasticity to absorb the guesswork. ### Static Sizing Wastes Capacity Workloads run on worst-case CPU and memory requests that are set once and rarely revisited. ### Manual Tuning Doesn't Scale Rightsizing by hand is slow, guesswork-driven, and risky at the pace modern clusters change. ### Toolchains Don't Optimize Themselves HPA and GitOps controllers execute configs but can't tune themselves for cost or performance. ## How We Help ### Autonomous Workload Optimization Sedai continuously analyzes utilization and performance to safely adjust pod and container resource allocations — using reinforcement learning to move workloads toward their optimal state. ### GPU Optimization For AI, ML, and HPC workloads, Sedai identifies idle GPU capacity and right-sizes allocations to match real demand, cutting a major cost driver. ### Works With Your Existing Toolchain Sedai doesn't replace HPA or your GitOps controller — it refines their configurations continuously, connecting via a lightweight agent or GitOps integration to fit your security needs. ## Optimize On-Prem Kubernetes Like It's Cloud-Native. ## Frequently Asked Questions ### Does Sedai replace our HPA or GitOps controller? No. Sedai integrates with your existing HPA and GitOps tooling (like ArgoCD or Flux) and continuously refines their configurations based on real workload behavior — it doesn't replace your operational stack. ### How does Sedai connect to a self-managed or air-gapped cluster? You can deploy a lightweight in-cluster agent or use a GitOps integration, where Sedai generates configuration files for your GitOps controller to apply — no direct external access required. ### What level of control do we have over changes Sedai makes? Sedai supports three modes: Datapilot for read-only recommendations, Copilot for human-approved changes, and Autopilot for full autonomous execution — so you choose how much control to hand over, per environment. ### Can Sedai be deployed fully within our own environment, including air-gapped? Yes. Sedai's self-hosted model deploys the entire platform inside your own Kubernetes cluster — public cloud, private cloud, or on-prem — so no data ever leaves your network, and it can run fully air-gapped. Capabilities are identical to the SaaS model; your team manages infrastructure and applies updates that Sedai packages for you. ### Does Sedai support FedRAMP-regulated environments? Yes. While Sedai's SaaS offering is not FedRAMP certified, the self-hosted model can be deployed entirely within your own FedRAMP-certified environment, with full optimization capabilities and no data leaving your network. --- # Platform9 Source: https://sedai.io/platform/platform9-optimization Sedai extends Platform9's managed Kubernetes simplicity to optimization — autonomously right-sizing workloads without adding operational overhead. ## Managed Kubernetes Simplicity, Extended to Optimization Platform9 delivers managed Kubernetes without a dedicated platform team. Sedai extends that same simplicity to optimization — autonomously right-sizing workloads without adding operational overhead. ## On-Prem Kubernetes Wasn't Built to Tune Itself Rightsizing self-managed clusters still relies on static estimates and manual effort — with no cloud elasticity to absorb the guesswork. ### Static Sizing Wastes Capacity Workloads run on worst-case CPU and memory requests that are set once and rarely revisited. ### Manual Tuning Doesn't Scale Rightsizing by hand is slow, guesswork-driven, and risky at the pace modern clusters change. ### Toolchains Don't Optimize Themselves HPA and GitOps controllers execute configs but can't tune themselves for cost or performance. ## How We Help ### Autonomous Workload Optimization Sedai continuously analyzes utilization and performance to safely adjust pod and container resource allocations — using reinforcement learning to move workloads toward their optimal state. ### GPU Optimization For AI, ML, and HPC workloads, Sedai identifies idle GPU capacity and right-sizes allocations to match real demand, cutting a major cost driver. ### Works With Your Existing Toolchain Sedai doesn't replace HPA or your GitOps controller — it refines their configurations continuously, connecting via a lightweight agent or GitOps integration to fit your security needs. ## Optimize On-Prem Kubernetes Like It's Cloud-Native. ## Frequently Asked Questions ### Does Sedai replace our HPA or GitOps controller? No. Sedai integrates with your existing HPA and GitOps tooling (like ArgoCD or Flux) and continuously refines their configurations based on real workload behavior — it doesn't replace your operational stack. ### How does Sedai connect to a self-managed or air-gapped cluster? You can deploy a lightweight in-cluster agent or use a GitOps integration, where Sedai generates configuration files for your GitOps controller to apply — no direct external access required. ### What level of control do we have over changes Sedai makes? Sedai supports three modes: Datapilot for read-only recommendations, Copilot for human-approved changes, and Autopilot for full autonomous execution — so you choose how much control to hand over, per environment. ### Can Sedai be deployed fully within our own environment, including air-gapped? Yes. Sedai's self-hosted model deploys the entire platform inside your own Kubernetes cluster — public cloud, private cloud, or on-prem — so no data ever leaves your network, and it can run fully air-gapped. Capabilities are identical to the SaaS model; your team manages infrastructure and applies updates that Sedai packages for you. ### Does Sedai support FedRAMP-regulated environments? Yes. While Sedai's SaaS offering is not FedRAMP certified, the self-hosted model can be deployed entirely within your own FedRAMP-certified environment, with full optimization capabilities and no data leaving your network. --- # Kubernetes (self-managed) Source: https://sedai.io/platform/kubernetes-self-managed-optimization Sedai adds autonomous rightsizing to self-managed Kubernetes — full stack control stays yours, with identical capabilities in fully air-gapped deployments. ## Full Control. Now With Autonomous Optimization. Self-managed Kubernetes gives you full control of the stack — and full responsibility for optimizing it. Sedai adds autonomous rightsizing without asking you to give up that control. ## On-Prem Kubernetes Wasn't Built to Tune Itself Rightsizing self-managed clusters still relies on static estimates and manual effort — with no cloud elasticity to absorb the guesswork. ### Static Sizing Wastes Capacity Workloads run on worst-case CPU and memory requests that are set once and rarely revisited. ### Manual Tuning Doesn't Scale Rightsizing by hand is slow, guesswork-driven, and risky at the pace modern clusters change. ### Toolchains Don't Optimize Themselves HPA and GitOps controllers execute configs but can't tune themselves for cost or performance. ## How We Help ### Autonomous Workload Optimization Sedai continuously analyzes utilization and performance to safely adjust pod and container resource allocations — using reinforcement learning to move workloads toward their optimal state. ### GPU Optimization For AI, ML, and HPC workloads, Sedai identifies idle GPU capacity and right-sizes allocations to match real demand, cutting a major cost driver. ### Works With Your Existing Toolchain Sedai doesn't replace HPA or your GitOps controller — it refines their configurations continuously, connecting via a lightweight agent or GitOps integration to fit your security needs. ## Optimize On-Prem Kubernetes Like It's Cloud-Native. ## Frequently Asked Questions ### Does Sedai replace our HPA or GitOps controller? No. Sedai integrates with your existing HPA and GitOps tooling (like ArgoCD or Flux) and continuously refines their configurations based on real workload behavior — it doesn't replace your operational stack. ### How does Sedai connect to a self-managed or air-gapped cluster? You can deploy a lightweight in-cluster agent or use a GitOps integration, where Sedai generates configuration files for your GitOps controller to apply — no direct external access required. ### What level of control do we have over changes Sedai makes? Sedai supports three modes: Datapilot for read-only recommendations, Copilot for human-approved changes, and Autopilot for full autonomous execution — so you choose how much control to hand over, per environment. ### Can Sedai be deployed fully within our own environment, including air-gapped? Yes. Sedai's self-hosted model deploys the entire platform inside your own Kubernetes cluster — public cloud, private cloud, or on-prem — so no data ever leaves your network, and it can run fully air-gapped. Capabilities are identical to the SaaS model; your team manages infrastructure and applies updates that Sedai packages for you. ### Does Sedai support FedRAMP-regulated environments? Yes. While Sedai's SaaS offering is not FedRAMP certified, the self-hosted model can be deployed entirely within your own FedRAMP-certified environment, with full optimization capabilities and no data leaving your network. --- # VMware Tanzu Source: https://sedai.io/platform/vmware-tanzu-optimization Sedai optimizes both container requests and the underlying vSphere VMs serving as your Tanzu Kubernetes nodes — no manual re-tuning required. ## Optimize Kubernetes Nodes Running on vSphere Tanzu runs Kubernetes on top of vSphere infrastructure. Sedai optimizes both container-level requests and the underlying nodes — right-sizing the VMs serving as your Kubernetes nodes. ## On-Prem Kubernetes Wasn't Built to Tune Itself Rightsizing self-managed clusters still relies on static estimates and manual effort — with no cloud elasticity to absorb the guesswork. ### Static Sizing Wastes Capacity Workloads run on worst-case CPU and memory requests that are set once and rarely revisited. ### Manual Tuning Doesn't Scale Rightsizing by hand is slow, guesswork-driven, and risky at the pace modern clusters change. ### Toolchains Don't Optimize Themselves HPA and GitOps controllers execute configs but can't tune themselves for cost or performance. ## How We Help ### Autonomous Workload Optimization Sedai continuously analyzes utilization and performance to safely adjust pod and container resource allocations — using reinforcement learning to move workloads toward their optimal state. ### GPU Optimization For AI, ML, and HPC workloads, Sedai identifies idle GPU capacity and right-sizes allocations to match real demand, cutting a major cost driver. ### Works With Your Existing Toolchain Sedai doesn't replace HPA or your GitOps controller — it refines their configurations continuously, connecting via a lightweight agent or GitOps integration to fit your security needs. ## Optimize On-Prem Kubernetes Like It's Cloud-Native. ## Frequently Asked Questions ### Does Sedai replace our HPA or GitOps controller? No. Sedai integrates with your existing HPA and GitOps tooling (like ArgoCD or Flux) and continuously refines their configurations based on real workload behavior — it doesn't replace your operational stack. ### How does Sedai connect to a self-managed or air-gapped cluster? You can deploy a lightweight in-cluster agent or use a GitOps integration, where Sedai generates configuration files for your GitOps controller to apply — no direct external access required. ### What level of control do we have over changes Sedai makes? Sedai supports three modes: Datapilot for read-only recommendations, Copilot for human-approved changes, and Autopilot for full autonomous execution — so you choose how much control to hand over, per environment. ### Can Sedai be deployed fully within our own environment, including air-gapped? Yes. Sedai's self-hosted model deploys the entire platform inside your own Kubernetes cluster — public cloud, private cloud, or on-prem — so no data ever leaves your network, and it can run fully air-gapped. Capabilities are identical to the SaaS model; your team manages infrastructure and applies updates that Sedai packages for you. ### Does Sedai support FedRAMP-regulated environments? Yes. While Sedai's SaaS offering is not FedRAMP certified, the self-hosted model can be deployed entirely within your own FedRAMP-certified environment, with full optimization capabilities and no data leaving your network. --- # Monitoring & APM Source: https://sedai.io/platform/monitoring-apm Sedai correlates infra metrics with latency, errors, and traffic from your existing APM tools — no new agents, just smarter and safer optimization. ## Optimize Without Risking Performance Your observability tools already capture how your services behave under load. Sedai considers that signal with every optimization decision, so infrastructure changes don't trade efficiency for reliability. ## Your Monitoring Stack Sees Everything. Optimization Doesn't. APM and observability tools generate the right signal. It just never reaches the systems making resource decisions. ### Metrics live in silos. CPU and memory sit in your cloud console. Latency and error rates live in Datadog or Dynatrace. Nothing connects them. ### Low utilization isn't the same as safe to reduce. A service at 30% CPU might be absorbing bursts or sitting upstream of a latency-sensitive path. ### Alerts fire after the damage is done. Tracing a regression back to the resource change that caused it means manual log digs and timeline comparisons. ## Key Capabilities By connecting to your APM tools, Sedai moves beyond utilization metrics to understand how resource changes actually affect your applications. ### Correlate Infrastructure Metrics With Application Behavior Sedai combines CPU and memory data with latency, error rates, and traffic signals, building a complete model of each workload before taking any action. ### Catch the Optimizations That Would Have Caused Problems A service with low CPU utilization might still be latency-sensitive. Sedai identifies these cases and avoids changes that would reduce efficiency without improving safety. ### Connect Multiple Providers Into a Single Model For instance, use CloudWatch for infrastructure metrics and Datadog for APM data simultaneously. Sedai correlates signals across all connected sources into one unified view of each workload. "Sedai has helped us save millions of dollars by optimizing and managing our own back-end services. But most importantly, what Sedai has done very well is allow us to respond in real time when anomalies are detected." — Suresh Sangiah, SVP of Engineering // Palo Alto Networks ## Works With Your Stack ### AppDynamics ### Chronosphere ### Datadog ### Dynatrace ### Elasticsearch ### Prometheus ### Cloud Monitoring ### Grafana ### Mimir ### Netdata ### New Relic ### Splunk ### VictoriaMetrics ### Wavefront ## The Sedai Difference | Without Sedai | Sedai | | --- | --- | | Optimization decisions based on CPU and memory alone | Every decision accounts for latency, error rates, and traffic alongside infra metrics | | APM tools and optimization tooling are disconnected workflows | Sedai connects directly to existing monitoring providers — no new agents required | | Low utilization is treated as a safe signal to reduce resources | Sedai checks application behavior before acting — low CPU doesn't always mean safe to cut | | A single monitoring source limits the accuracy of the model | Sedai connects multiple providers simultaneously for a unified workload view | | SLO violations are discovered after the optimization is applied | Application performance signals are part of the safety check before every action | ## Resources ## Infrastructure Efficiency That Doesn't Come at the Cost of Application Performance --- # Workflow & Alerts Source: https://sedai.io/platform/workflow-alerts Sedai logs every action in ServiceNow or Jira and pushes alerts to Slack or PagerDuty — full autonomy where allowed, gated approvals where not. ## Autonomous Optimization That Respects Your Change Process Sedai integrates with your ITSM and alerting tools so every action is logged, traceable, and subject to the same governance policies as any other change. ## Change Management Wasn't Designed for Autonomous Decisions ITSM workflows and approval gates are built around human-paced change requests. Autonomous optimization runs on a different clock — and most governance tooling hasn't caught up yet. ### Autonomous changes are invisible to the change record. If they don't generate tickets, they don't exist as far as ITSM is concerned — no audit trail, no traceability, no compliance coverage. ### Approval workflows assume a human is proposing the change. Most ITSM processes have no path for machine-initiated actions, so autonomous optimization either bypasses governance or gets blocked by it. ### Alerting can't tell optimization from incidents, and there's no rollback signal. On-call teams can't distinguish an expected adjustment from a real problem, and reversing a change means manual intervention. ## Key Capabilities Sedai's ITSM and alerting integrations ensure autonomous optimization is never a black box. Every action is controlled, visible, and aligned with your enterprise workflows. ### Enforce Change Control Automatically Sedai creates a formal change request in ServiceNow or Jira for every optimization, ensuring all autonomous actions are logged within your existing ITSM platform. ### Require Approval Before Sedai Acts For sensitive environments, configure Sedai to pause before executing a change and wait for an authorized approver in your ITSM tool. Production stays gated; dev can run freely ### Keep Teams Informed Without Extra Dashboards Sedai pushes notifications for executed optimizations, new opportunities, and system health events directly to Slack, Teams, or PagerDuty — no manual monitoring required. “We've had an incredible journey with Sedai so far. It's been very, very easy for us to implement. If you’re considering autonomous optimization, just do it!” — Matt Duren, VP of Engineering // KnowBe4 ## Works With Your Stack ### ServiceNow ### Jira ### Slack ### Microsoft Teams ### PagerDuty ### Opsgenie ### Webhook ### Rundeck ### Stackstorm ### Asana ### Email ## The Sedai Difference | Without Sedai | Sedai | | --- | --- | | Autonomous actions bypass change control, creating compliance risk | Every Sedai action generates a formal change record in your existing ITSM platform | | No audit trail connecting optimization actions to their outcomes | Complete traceability: every action is logged, linked, and reviewable | | Approval workflows require engineers to check a separate tool | Sedai pauses and waits for approval within ServiceNow or Jira before acting | | The same approval overhead applies to every environment | Per-environment configuration: gated approvals for production, autonomous execution for dev | | Teams learn about optimization activity after the fact, if at all | Sedai proactively pushes notifications to Slack, Teams, or PagerDuty as events occur | ## Resources ## Autonomous. Auditable. Aligned With How Your Organization Works. --- # Amazon EC2 Source: https://sedai.io/platform/amazon-ec2-optimization Sedai continuously analyzes Amazon EC2 utilization and autonomously right-sizes instances — application-aware, unlike generic provider recommendations. ## Stop Paying for EC2 Capacity You Don't Use Most EC2 instances are sized for peak load, not typical load — and they stay that way. Sedai continuously analyzes utilization and autonomously right-sizes instances without risking performance or availability. ## Rightsizing Advice Only Helps If You Can Safely Act on It. AWS provides utilization-based rightsizing recommendations, but infrastructure metrics alone don't tell you whether acting on them is safe. For latency-sensitive workloads, that gap between "looks underutilized" and "safe to resize" is exactly where teams get burned — so they stop acting on recommendations at all. ### Utilization data without application context is incomplete. CPU and memory averages look fine until you resize an instance and discover the workload had burst requirements or latency sensitivities the metrics didn't surface. Technically accurate recommendation. Still a degraded service. ### At fleet scale, manual analysis is always out of date. By the time you've audited a set of instances, the first ones have drifted again. Static analysis against a moving target isn't a strategy — it's a way to stay busy while underutilization compounds. ### Risk aversion is rational, and it's expensive. When teams can't confidently assess whether a change is safe, leaving it alone is the rational call. The result is persistent overspend that everyone knows about and no one touches. ## How We Help ### Continuous Rightsizing Sedai profiles each instance's actual utilization patterns and autonomously adjusts sizing — no manual audits, no stale recommendations sitting in a backlog. ### Application-Aware Optimization Unlike generic cloud provider tools, Sedai incorporates application performance signals before acting, so rightsizing decisions don't come at the cost of latency or availability. ### Purchasing Optimization Sedai recommends and manages the lowest-cost purchasing mix across on-demand, reserved instances, and savings plans, continuously updated as your workload evolves. ## Your Instances Sized Themselves. Finally. ## Frequently Asked Questions ### How is this different from AWS Compute Optimizer recommendations? AWS Compute Optimizer uses utilization metrics but lacks application-level context. Sedai incorporates performance signals like latency, error rates, and availability before making changes, reducing the risk that a recommendation causes degradation. ### Does Sedai work with both Linux and Windows EC2 instances? Yes. Sedai supports both Linux and Windows workloads across EC2 instance families. ### Will rightsizing cause downtime? Instance type changes require a stop/start cycle, which Sedai schedules during low-traffic windows. For applications requiring zero downtime, Sedai can coordinate changes alongside Auto Scaling groups to maintain availability throughout. ### How does Sedai handle instances that are part of an Auto Scaling group? Sedai treats ASG-managed instances as a unit, optimizing launch template configurations and instance type selection at the group level rather than instance by instance. ### How does purchasing optimization work? Does Sedai make purchases on our behalf? Sedai provides recommendations across on-demand, savings plans, and reserved instance options. Purchasing actions require explicit approval; Sedai doesn't commit spend autonomously. --- # AWS Fargate Source: https://sedai.io/platform/aws-fargate-optimization Sedai continuously tunes AWS Fargate task CPU, memory, and Spot eligibility — configuring the optimal Spot/On-Demand blend without sacrificing availability. ## Right-Size Fargate Tasks Without the Guesswork With Fargate, you pay for exactly what you request, which means over-provisioned task definitions cost you directly, every hour. Sedai continuously tunes CPU and memory allocations, Spot eligibility, and auto scaling to cut waste without sacrificing availability. ## Fargate Removes Infrastructure Management. It Doesn't Remove Inefficiency. Serverless compute shifts the operational burden off your team, but the decisions that drive cost don't disappear. Task CPU and memory are still yours to configure, Spot still requires judgment to use safely, and scaling minimums set at launch tend to stay wherever they were set. ### Task configuration is set at deployment and rarely revisited. CPU and memory get defined based on launch-time estimates that may not reflect actual behavior. Unlike EC2, there's no gradual drift signal — you're either correctly sized or you're not, and the bill doesn't tell you which. ### Spot adoption requires per-service judgment that most teams skip. Fargate Spot can cut compute costs significantly, but whether a service can tolerate interruptions depends on architecture, criticality, and traffic patterns. Assessing that across every service consistently gets deprioritized. ### Conservative scaling minimums run as idle cost indefinitely. Minimum task counts get set to safe-feeling numbers at launch and rarely come back down — even when the services they were protecting no longer need them. ## How We Help ### Task & Container Rightsizing Sedai observes actual utilization and incrementally adjusts CPU and memory allocations — accounting for Fargate's specific resource combinations — to eliminate over-provisioning at the task level. ### Intelligent Fargate Spot Sedai evaluates restart tolerance, traffic patterns, and environment type to identify Spot candidates, then configures an optimal Spot/On-Demand blend automatically. ### Auto Scaling Optimization Sedai analyzes load patterns to right-size task minimums and scaling policies, so you stop paying for idle capacity during low-demand periods. ## Stop Funding Worst-Case Scenarios. ## Frequently Asked Questions ### Does Sedai support Fargate Spot? Yes. Sedai analyzes each service's characteristics, including restart time, traffic patterns, and whether it's production or non-production, to determine Spot suitability and configure the right Spot/On-Demand mix automatically. ### How does Sedai handle Fargate's fixed CPU/memory combinations? Fargate requires CPU and memory to be selected from a specific set of valid combinations. Sedai's optimization model understands these constraints and only recommends configurations that are valid for Fargate — it won't suggest arbitrary values that can't be applied. ### Will task rightsizing cause service interruptions? Applying new CPU and memory allocations requires tasks to be replaced. Sedai manages this process incrementally and respects your service's minimum healthy task count to maintain availability throughout. ### Does Sedai work with both ECS on Fargate and EKS on Fargate? Yes. Sedai supports Fargate as a launch type for both ECS services and EKS pods, with optimization logic adapted to each orchestration model. ### How is auto scaling handled if we already have scaling policies configured? Sedai evaluates your existing policies and only recommends changes when it identifies a more efficient configuration. It won't override a policy simply to make a change — only when the data supports an improvement. --- # AWS Lambda Source: https://sedai.io/platform/aws-lambda-optimization Sedai continuously tunes AWS Lambda memory and concurrency per function — eliminating cold starts and overprovisioning without manual configuration. ## Lambda Optimized Continuously, Not Just Once Memory, concurrency, and cold starts require constant tuning as traffic and code change — but most teams set them once and move on. Sedai autonomously finds the optimal configuration for every function, continuously. ## Lambda's Simplicity Is Real. Its Cost Model Is Genuinely Hard to Optimize. Memory allocation controls CPU, and CPU controls execution time, which controls cost. These relationships make Lambda difficult to tune manually — especially across hundreds of functions with different traffic profiles and runtime behaviors. ### Memory is the wrong knob when what you're tuning is CPU. Lambda allocates CPU proportionally to memory. Teams that optimize on memory alone — without accounting for execution time tradeoffs — routinely end up paying for unused RAM or creating latency problems by under-allocating CPU. ### Static provisioned concurrency wastes money by design. Provisioned concurrency eliminates cold starts but is billed continuously regardless of actual traffic. A fixed setting that made sense at peak will overspend during every off-peak hour until someone adjusts it. ### Reserved concurrency across hundreds of functions doesn't manage itself. Maintaining appropriate concurrency limits across a large function fleet — as traffic patterns and deployment frequencies change — is ongoing work that SRE teams consistently deprioritize. "Pretty much everything that we're doing is using Lambdas and Step Functions. It was a whole area that I thought I was going to have to spend a lot of resources on. But [now] the optimization of the memory and the CPU of our lambdas is something that I just have Sedai worry about. And we just see the results." — Robert Berger, Chief Architect // Informed ## How We Help ### Memory & Performance Optimization Sedai continuously adjusts each function's memory allocation — measuring impact on duration and cost — to find the configuration that delivers best performance at the lowest price. ### Provisioned Concurrency Management Sedai analyzes traffic patterns to dynamically adjust provisioned concurrency throughout the day, eliminating cold starts without the cost of a static configuration. ### Reserved Concurrency Visibility Sedai surfaces how account-level concurrency is distributed across all functions, making it practical to protect critical functions from being throttled by high-volume, non-critical ones. ## Every Function. Optimal Config. All the Time. ## Frequently Asked Questions ### How does Sedai determine the right memory setting for a function? Sedai uses a reinforcement learning model that makes incremental adjustments to memory in the live environment, measures the resulting impact on execution duration and cost, and converges on the configuration that delivers the best performance-to-cost ratio for that specific function's behavior. ### Does Sedai's memory optimization account for the cost of execution time, not just allocation? Yes. Because Lambda pricing is based on both memory allocated and execution duration, increasing memory can sometimes lower total cost by reducing how long a function runs. Sedai's model accounts for this relationship and optimizes for total cost, not just allocated memory. ### Will dynamic provisioned concurrency adjustments cause cold starts during transitions? Sedai builds a predictive model from your function's traffic patterns and seasonality, adjusting concurrency ahead of anticipated demand rather than reactively. This approach is designed to maintain warm instances through traffic ramps rather than responding after cold starts occur. ### How does Sedai handle functions with unpredictable or spiky traffic? Sedai's model accounts for traffic variability when building concurrency schedules. For highly unpredictable functions, recommendations are more conservative — maintaining sufficient baseline capacity while avoiding the cost of over-provisioning across the full day. ### Does Sedai support Lambda functions across multiple AWS accounts or regions? Yes. Sedai supports multi-account and multi-region Lambda deployments, with concurrency visibility and optimization applied across the full account structure. --- # Amazon EBS Source: https://sedai.io/platform/amazon-ebs-optimization Sedai continuously monitors your Amazon EBS estate, autonomously right-sizing volumes and flagging waste — safety checks and change tickets included. ## Cut EBS Waste Without the Manual Audit Most EBS environments accumulate waste quietly — oversized volumes, wrong storage classes, and unattached disks left running after instances terminate. Sedai continuously monitors and optimizes your entire EBS estate automatically. ## EBS Costs Accumulate Quietly and Are Hard to Audit at Scale. Storage costs don't spike — they compound. Volumes get provisioned for peak estimates and never revisited. Instances get terminated but their volumes keep running. The wrong volume type means overpaying for performance you don't need or hitting bottlenecks you didn't anticipate. None of this surfaces without tooling built to look for it. ### Volumes get provisioned for peak and never reclaimed. Initial sizing decisions are made under uncertainty and optimized for headroom. Unlike compute, underutilized storage doesn't trigger alerts — it just bills. ### Orphaned volumes are predictable and rarely managed. When instances are terminated, attached EBS volumes frequently aren't. They sit unattached, accruing cost. At thousands of volumes across multiple accounts, that notice often never comes. ### The right volume type changes as workloads evolve. io1, gp2, gp3, st1, sc1 — the correct choice depends on I/O profile and usage level, both of which change over time. That analysis rarely gets repeated after initial provisioning. ## How We Help ### Storage Class Optimization Sedai analyzes IOPS, throughput, and utilization to autonomously select the right volume type for each workload, meeting performance requirements at the lowest cost. ### Unused Volume Detection Sedai identifies unattached and idle volumes across your EBS environment and surfaces decommissioning recommendations to eliminate recurring spend on unused resources. ### Automated Remediation Sedai executes optimization changes automatically — with safety checks, change management ticketing, and post-change verification built into every action. ## Your EBS Estate Is Larger Than You Think. ## Frequently Asked Questions ### How does Sedai decide which EBS volume type to recommend? Sedai analyzes actual IOPS consumption, throughput utilization, and volume size over time to understand what each workload genuinely requires. It then maps that demand to the lowest-cost volume type that meets it, whether that's gp3, io2, or a lower-cost HDD option. ### How does Sedai identify unattached volumes? Sedai continuously monitors the attachment state and usage patterns of every volume in your environment. Volumes that have been detached or show no meaningful I/O activity are flagged for review, with recommendations for safe decommissioning. ### Does Sedai delete volumes automatically? No. Decommissioning recommendations require explicit approval. Sedai surfaces the opportunity and provides the context needed to make a confident decision — the action itself stays in human hands. ### What safety checks are included when Sedai changes a volume type? Before executing a volume type change, Sedai acquires a lock on the volume to prevent concurrent modifications, creates a change management ticket if your ITSM is connected, applies the change, and verifies the outcome. If verification fails, the issue is flagged for review. ### Does Sedai support EBS volumes across multiple AWS accounts? Yes. Sedai supports multi-account AWS environments, with EBS optimization and visibility applied consistently across your full account structure. --- # Amazon S3 Source: https://sedai.io/platform/amazon-s3-optimization Sedai autonomously shifts S3 data into Intelligent-Tiering by access pattern, then recommends Archive moves for persistently cold data. ## Stop Paying S3 Standard Rates for Cold Data Most teams store everything in S3 Standard by default and never revisit it. Sedai analyzes access patterns across your buckets and autonomously moves data to the right storage class — continuously. ## S3 Costs Accumulate Quietly Because Nothing Forces You to Look at Them. S3 has no performance degradation to alert on. Data lands in Standard and stays there. Lifecycle policies are complex enough to deprioritize. Incomplete multipart uploads accumulate invisibly. The result is spend that grows consistently and rarely surfaces as a priority. ### Lifecycle policy complexity is a real reason teams don't implement them. Moving data to Infrequent Access, Glacier, or Intelligent-Tiering requires understanding access patterns and evaluating cost crossover points. It's ongoing maintenance that consistently loses to higher-visibility work. ### Intelligent-Tiering is valuable but not universally appropriate. The per-object monitoring charge means it only pencils out above certain object size and access frequency thresholds. Applying it blindly — or not applying it when it would help — both cost money. ### Unmanaged resources add to the bill in ways that never surface without dedicated tooling. Incomplete multipart uploads, stale object versions, and objects left in Standard past their useful access window are predictable outputs of normal S3 usage — and they accumulate until something specifically audits for them. ## How We Help ### Autonomous Intelligent-Tiering Sedai analyzes access patterns and performs a cost-benefit analysis before moving eligible objects to Intelligent-Tiering — so data shifts between tiers automatically based on real usage. ### Archive Tier Recommendations For persistently cold data, Sedai recommends transition to Archive or Deep Archive tiers — surfacing the savings opportunity without acting unilaterally on long-term storage. ### Cost & Usage Visibility Sedai surfaces S3 cost breakdowns and hidden waste — including incomplete multipart uploads — so savings opportunities that would otherwise go unnoticed don't. ## Your Coldest Data Shouldn't Cost Standard Rates. ## Frequently Asked Questions ### How does Sedai decide whether Intelligent-Tiering is worth it for a given bucket? Intelligent-Tiering has its own per-object monitoring cost, which means it isn't always cost-justified for small objects or buckets with unpredictable access patterns. Sedai performs a cost-benefit analysis before making any transition, only moving objects where the savings outweigh the overhead. ### Does Sedai move objects to Archive tiers automatically? No. Transitions to Archive Access and Deep Archive are surfaced as recommendations rather than autonomous actions. These tiers introduce retrieval latency that may not be acceptable for all workloads, so Sedai leaves that decision with your team. ### What happens to data availability when objects move to Intelligent-Tiering? Intelligent-Tiering maintains the same availability and durability as S3 Standard. Objects in the frequent access tier are immediately available; objects that have transitioned to infrequent access are still accessible without retrieval fees, just with slightly different performance characteristics. ### How does Sedai handle incomplete multipart uploads? Sedai identifies incomplete multipart uploads that are consuming storage without serving any purpose and surfaces them as cleanup candidates. As with decommissioning recommendations elsewhere, cleanup actions require explicit approval. ### Does Sedai work across multiple S3 buckets and AWS accounts? Yes. Sedai analyzes and optimizes S3 storage across all buckets in your environment, with support for multi-account AWS deployments. --- # Amazon DynamoDB Source: https://sedai.io/platform/amazon-dynamodb-optimization Sedai continuously analyzes DynamoDB capacity, GSI usage, and table class fit, then recommends right-sizing, cleanup, and storage changes to make. ## Stop Over-Provisioning DynamoDB Capacity You Don't Use RCUs and WCUs provisioned for peak load sit idle most of the time, and unused GSIs and wrong table classes quietly add to the bill. Sedai analyzes your DynamoDB environment and surfaces the right optimizations to act on. ## DynamoDB Capacity Is Easy to Set, Hard to Right-Size. DynamoDB's managed model removes significant operational burden — but capacity planning still requires ongoing attention. RCU and WCU settings get made for peak load and rarely revisited as usage evolves. Table-level decisions that affect cost — storage class, GSI retention, billing mode — don't optimize themselves. ### Capacity units get set for peak and drift from reality over time. Traffic patterns shift, application usage changes, and the original estimates stop reflecting actual behavior — but revising them requires analysis that competes with higher-visibility engineering work. ### GSIs accumulate long after applications stop querying them. An index created for a feature that was later retired continues generating storage and write costs indefinitely. Auditing GSI usage across a large table inventory is rarely done on a regular cadence. ### Billing mode and storage class decisions rarely get revisited. A table in Provisioned mode might be cheaper in On-Demand as access patterns become spikier. A Standard table might qualify for Standard-IA. These are tractable optimizations — but only if someone is continuously analyzing the data. ## How We Help ### Provisioned Capacity Rightsizing Sedai analyzes historical read and write capacity consumption to identify over-provisioned tables and recommends RCU and WCU adjustments that eliminate waste without compromising performance. ### Unused Index Detection Sedai monitors GSI access patterns and flags indexes with no sustained query activity, surfacing deletion candidates before they continue accumulating storage and throughput costs indefinitely. ### Table Class Optimization Sedai analyzes table access frequency to identify candidates for Standard-IA, recommending the switch where the cost structure better matches actual usage patterns. ## Your DynamoDB Bill Has a Few Things to Say. ## Frequently Asked Questions ### Does Sedai support both provisioned and on-demand capacity modes? Sedai's capacity optimization analysis focuses on provisioned capacity tables, where RCU and WCU allocations are directly controllable. For tables in on-demand mode, Sedai provides visibility into consumption patterns to inform decisions about whether switching to provisioned capacity would reduce costs. ### How does Sedai identify unused GSIs — could it flag an index used only for quarterly reports? Sedai flags GSIs that show no query activity over a sustained monitoring period. We recommend reviewing flagged indexes carefully before deletion — an index that appears idle may still serve infrequent but critical workflows like quarterly reporting. Sedai surfaces the recommendation; your team makes the call. ### Does Sedai apply DynamoDB changes autonomously? Not currently. DynamoDB optimizations are delivered as recommendations rather than autonomous actions. Sedai provides the analysis and identifies the opportunity — execution remains in your hands. ### How does Sedai determine whether Standard-IA is the right table class? Sedai analyzes each table's access frequency against the cost structure of Standard vs. Standard-IA. Standard-IA carries lower storage costs but higher read/write costs, so the recommendation only applies where the access profile makes the tradeoff genuinely favorable. ### Does Sedai support DynamoDB across multiple AWS accounts? Yes. Sedai connects to multi-account AWS environments and provides DynamoDB recommendations consistently across your full account structure. --- # Azure AKS Source: https://sedai.io/platform/azure-aks-optimization Sedai continuously rightsizes AKS pods and node pools together, then attributes cost by team — all through small, validated changes with no disruption. ## From Pods to Node Pools, Optimized Together AKS workloads are provisioned once against worst-case estimates, and the node pools underneath inherit that waste — running oversized, mismatched, or underutilized. Sedai continuously rightsizes pods and containers, then optimizes node type, count, and segregation to match, while attributing cost back to the teams responsible. ## Rightsizing Pods Doesn't Fix a Cluster on Its Own Kubernetes workloads and the infrastructure underneath them are two separate optimization problems, and most teams only have the bandwidth to guess at one — usually with static, worst-case requests that ripple all the way down to the node pool. ### Static resource requests set the ceiling for everything above and below them. Teams size pods for worst-case load and rarely revisit it, and manually rightsizing dozens of clusters on an ongoing basis is guesswork that carries risk of causing performance issues or outages if done too aggressively. ### Node pools inherit whatever inefficiency the workloads create — and add their own. VM instance types that don't match the workload profile, underutilized pools that never get consolidated, and mixed workloads sharing infrastructure they shouldn't all compound the waste from oversized pods. ### Shared clusters obscure who's actually spending what. Without workload-level attribution, the costs of compute, storage, GPU, and network usage in a shared cluster are difficult to trace back to the team or business unit responsible for them. ## How We Help ### Autonomous Workload Optimization Sedai analyzes real-time CPU and memory data and continuously adjusts pod scaling through small, validated changes, moving workloads to their optimal state without disruption. ### Node & Cluster Optimization After rightsizing workloads, Sedai recommends better-matched instance types, resizes or consolidates node pools, and suggests splitting mixed workloads onto more specialized infrastructure. ### Cost Attribution & Application-Aware Tuning Get workload-level cost attribution across compute, storage, and network, with trade-offs tuned to whether a workload is latency-sensitive or cost-sensitive. ## Stop Guessing at Every Layer of Your Cluster. ## Frequently Asked Questions ### Does Sedai optimize node pools, or just the pods running on them? Both, in sequence. Sedai first rightsizes pod-level CPU and memory requests, then analyzes the collective needs of each node pool to recommend better-matched instance types, resizing, consolidation, or splitting mixed workloads onto specialized pools. ### How does Sedai avoid causing performance issues while making changes? Sedai applies a series of small, validated changes rather than large, aggressive shifts, moving workloads toward their optimal configuration gradually and monitoring for issues along the way. ### Can Sedai tell the difference between a latency-sensitive service and a batch job? Yes. Sedai's application-aware intelligence factors in what kind of workload it's optimizing, making different cost-versus-performance trade-offs for latency-sensitive services than it would for cost-sensitive batch workloads. ### We run shared clusters across multiple teams — can Sedai help with cost attribution? Yes. Sedai attributes compute, GPU, storage, and network costs down to the individual workload, so teams and business units can see the true cost of what they're running, even in a shared cluster. ### Does Sedai work alongside Azure's Cluster Autoscaler? Yes. Sedai doesn't replace the autoscaler — it acts as an intelligence layer that makes it more efficient. Sedai analyzes your workload profile and refines the set of choices the autoscaler has to work with, such as narrowing a range of 15 eligible VM instance types down to the five with the best cost-performance fit, without disrupting your existing operational setup. --- # Azure Virtual Machines Source: https://sedai.io/platform/azure-virtual-machines-optimization Sedai continuously analyzes Azure VM utilization and autonomously right-sizes instances and purchasing — safety checks and audit trails included. ## Right-Size Every Azure VM, Autonomously Most Azure VM fleets are provisioned for peak load and never revisited, leaving teams paying for capacity they rarely use. Sedai continuously analyzes utilization patterns and right-sizes VMs (in-family, cross-family, and burstable) while optimizing purchasing, so your fleet matches actual demand. ## VM Optimization Isn't a One-Time Sizing Decision. Azure gives you dozens of VM families and sizes, but choosing right the first time isn't the same as staying right. Workloads drift, teams estimate for the worst case, and manual rightsizing can't keep pace across a growing fleet. ### Peak-load estimates become the safe default, and the safe default becomes permanent. Downsizing without deep analysis risks throttling or availability issues, so teams round up instead, and the gap between allocated and actual usage compounds every month. ### Rightsizing doesn't scale with the fleet. Tracking utilization and adjusting instance types across hundreds or thousands of VMs by hand can't keep up with how fast workload patterns shift. ### Azure's VM catalog adds another layer of difficulty. Azure's VM catalog adds another layer of difficulty. With general-purpose, compute-optimized, memory-optimized, and burstable families all in play, matching the right family — not just the right size — to a workload's actual profile is its own analysis problem. ## How We Help ### Continuous VM Right-Sizing Sedai analyzes utilization data to right-size VMs — in-family for consistency, cross-family for true mismatches, or burstable B-series where usage patterns fit. ### Safe, Autonomous Execution Sedai applies changes with built-in safety checks, audit trails, and health monitoring — or via Copilot mode for teams who want to approve first. ### Purchasing Optimization Get recommendations across on-demand, reserved instances, and savings plans, layered on top of rightsizing to minimize total compute spend. ## Stop Sizing for Peak. Start Paying for Actual. ## Frequently Asked Questions ### Does Sedai support all Azure VM families, including B-series burstable instances? Yes. Sedai works across Azure's VM families — general-purpose, compute-optimized, memory-optimized, and burstable B-series — and factors family-specific behavior, like CPU credit accumulation for B-series, into its recommendations. ### Will resizing a VM cause downtime? Changing a VM's size in Azure requires a restart to take effect. Sedai schedules these changes to minimize disruption and monitors application health afterward, rolling back automatically if instability follows. ### What's the difference between in-family and cross-family recommendations? In-family changes keep a workload on the same VM family (e.g., D4s\_v5 to D2s\_v5) for architectural consistency. Cross-family changes move a workload to a fundamentally better-suited family, such as when a CPU-bound workload is running on memory-optimized hardware. ### Can I review changes before Sedai applies them? Yes. Copilot mode surfaces every recommendation for approval before execution, for teams that want oversight before autonomous action. ### Does Sedai only rightsize instances, or does it also help with purchasing? Both. Sedai optimizes vertical scaling (instance size) and horizontal scaling (instance count), and separately recommends the most cost-effective purchasing option — on-demand, reserved instances, or savings plans — for your workload. --- # Azure Managed Disks Source: https://sedai.io/platform/azure-managed-disks-optimization Sedai continuously rightsizes Azure Managed Disk tiers, reclaims unattached disks, and migrates to newer types — safety checks and snapshots included. ## Every Disk, Priced for What It Actually Does Managed Disks are provisioned once and rarely revisited, leaving teams overpaying for performance tiers they don't need, exposed to bottlenecks when they under-provision, or paying for orphaned disks no one owns. Sedai continuously rightsizes tiers, reclaims unattached disks, and migrates to newer, more cost-effective disk types. ## Disk Costs Drift in Both Directions — and Nobody's Watching Choosing a performance tier isn't a one-time decision, and getting it wrong doesn't fail loudly. Tier mismatches, deleted VMs that leave their disks behind, and disk types that go stale all quietly add up, with no natural trigger to catch them. ### Performance tiers get chosen once and assumed correct forever. Premium SSDs are the default safety net for uncertain I/O needs, but that same instinct also produces the opposite failure: a tier too slow for the workload, causing latency and availability problems on the attached VM. ### Deleting a VM doesn't delete its disks. Data disks routinely survive the VM they were attached to, continuing to accrue storage costs indefinitely with no compute workload — and often no clear owner — to flag them. ### Disk technology keeps improving, but migrations don't happen on their own. Newer disk types like Premium SSD v2 frequently offer better performance at a lower price than their predecessors, but nothing prompts a team to revisit disks that were provisioned years earlier. ## How We Help ### Performance Tier Rightsizing Sedai analyzes historical IOPS and throughput to move underused disks to lower-cost tiers, upgrade bottlenecked disks, and migrate to newer types like Premium SSD v2. ### Unattached Disk Cleanup Sedai identifies orphaned disks with no attached VM and recommends deletion, with automatic snapshotting available first to protect against data loss. ### Safe, Automated Execution Changes to disks on running VMs follow a safe sequence — safety checks, stop, apply, restart — handled automatically without manual intervention. ## Stop Paying for Disks You Don't Need — or Can't Use. ## Frequently Asked Questions ### How does Sedai decide whether a disk needs a higher or lower performance tier? Sedai analyzes historical IOPS and throughput against the disk's provisioned limits. Disks that consistently run well under capacity are candidates for a lower-cost tier; disks that repeatedly hit their limits are flagged for an upgrade to eliminate bottlenecks. ### What happens to an unattached disk before Sedai recommends deleting it? Sedai identifies disks with no attached VM and surfaces them for cleanup. You can configure Sedai to automatically snapshot a disk before termination, so the data remains recoverable even after the disk itself is removed. ### Does optimizing a disk require taking down the VM it's attached to? Yes, for disks attached to a running VM. Sedai runs safety checks, stops the VM, applies the configuration change, and restarts it, handling the full sequence automatically so no manual intervention is required. ### What does Sedai mean by "modernization" recommendations? When a newer disk type offers better performance at a lower cost than what's currently provisioned, such as Premium SSD v2 versus v1, Sedai identifies the opportunity and recommends migrating. ### Can I review disk changes before they're applied? Yes. As with other Sedai optimizations, Copilot mode is available for teams that want to approve recommendations before any change is executed. --- # Azure Blob Storage Source: https://sedai.io/platform/azure-blob-storage-optimization Sedai continuously analyzes Azure Blob Storage access and autonomously shifts cold data to cheaper tiers, weighing retrieval costs with safety checks built in. ## Every Byte, Priced for How Often It's Used Blob Storage defaults to the Hot tier regardless of access frequency, and nobody revisits that decision as data ages. Sedai continuously analyzes access patterns, manages lifecycle policies, and moves data to cooler tiers — accounting for retrieval costs — so every byte lands where it actually saves money. ## Storage Tiers Only Save Money If Something Moves the Data Hot is the default tier for a reason: nobody wants a performance problem, But that default rarely gets revisited. As data cools, the gap between what you're paying and what you could be paying only grows. ### Data lands in the Hot tier by default and stays there. Most data doesn't need Hot-tier performance for its entire lifecycle, but without an active process to reassess it, infrequently accessed data keeps paying premium storage rates indefinitely. ### Tiering decisions don't stay accurate on their own. Access patterns shift as data ages, and manually reviewing usage and moving blobs between Hot, Cool, and Archive tiers across a large environment isn't something a team can sustain continuously. ### The cost of inaction compounds quietly. Backup and archival data left in the Hot tier can cost twice what the same data would in Archive — savings that simply accumulate as waste until someone actively intervenes. ## How We Help ### Data-Driven Tiering Analysis Sedai analyzes read/write patterns via Azure Monitor to identify cold data, weighing storage savings against retrieval costs so every move results in net savings. ### Automated Lifecycle Policy Management Sedai creates lifecycle policies where none exist, or refines existing ones, to automatically transition blobs to cooler tiers based on age or last access. ### Intelligent Tiering Enablement Sedai can enable Azure's Intelligent Tiering feature directly, running safety checks first to confirm the storage account is in a valid state. ## Stop Paying Hot-Tier Prices for Cold Data. ## Frequently Asked Questions ### How does Sedai decide when moving data to a cooler tier makes sense? Sedai analyzes access patterns from Azure Monitor to identify infrequently accessed data, then weighs the storage savings of a cooler tier against that tier's retrieval costs. Sedai only recommends a move when the net result is savings, not just a lower storage rate. ### What if my storage account doesn't have any lifecycle policies set up? Sedai creates one. It configures a lifecycle policy that automatically transitions blobs to cooler tiers based on age or last access time, so tiering happens continuously without manual review. ### What if I already have lifecycle policies in place? Sedai analyzes your existing policies and recommends modifications to improve their cost savings, rather than replacing them outright. ### What is Intelligent Tiering, and can Sedai turn it on for me? Intelligent Tiering is an Azure feature that automatically moves data between tiers based on usage. Sedai can enable it on your behalf, running safety checks first to confirm the storage account is in a valid state before making the change. ### What do I need to set up to get started? Connect your Azure account to Sedai, select Blob as a resource type, and connect a monitoring data source like Azure Monitor. --- # GKE Source: https://sedai.io/platform/google-kubernetes-engine-optimization Sedai continuously analyzes GKE usage and autonomously rightsizes pods and nodes across Standard and Autopilot, plus GPU and disk optimization. ## Right-Size Every GKE Workload, Autonomously GKE removes the operational burden of running Kubernetes, but resource decisions like pod requests, node packing, and GPU allocation still drive your bill. Sedai continuously analyzes actual usage and safely adjusts resources in both Standard and Autopilot clusters, so you stop paying for capacity you don't use. ## GKE Makes Kubernetes Easier to Run. Not Easier to Right-Size. Google manages the control plane, but the resource decisions that drive cost are still yours to get right, and most teams don't have the bandwidth to do it continuously. ### Workloads are over-provisioned by default. CPU and memory requests get set from worst-case estimates and rarely revisited, wasting resources regardless of GKE mode. ### That waste hits your bill differently depending on mode. Standard leaves you paying for underutilized nodes; Autopilot bills you for requested resources whether you use them or not. ### Fixing it manually doesn't scale, and specialized resources make it worse. GPUs and high-performance disks are frequently over-provisioned for peak loads that rarely occur. ## How We Help ### Autonomous Workload Rightsizing Sedai analyzes historical CPU and memory usage for every workload and continuously adjusts resource requests to match real demand, in both Standard and Autopilot clusters. ### Smarter Node Infrastructure (Standard) Sedai consolidates pods onto fewer, better-utilized nodes, helps the cluster autoscaler terminate idle capacity faster, and recommends more cost-effective instance types for your node pools. ### GPU & Persistent Disk Optimization Sedai identifies idle GPU capacity, right-sizes workloads to the most cost-effective GPU types, and flags overprovisioned or unattached Persistent Disks based on real I/O patterns. ## Stop Paying GKE Prices for Idle Capacity. ## Frequently Asked Questions ### Does Sedai work with both GKE Standard and Autopilot? Yes. Sedai rightsizes workload requests in both modes, and additionally optimizes node infrastructure — consolidation, autoscaling, instance selection — in Standard clusters where you manage the nodes. ### How does Sedai make changes safely? Sedai makes small, incremental adjustments and validates each one against application health metrics like latency and error rates before proceeding, rather than applying large changes all at once. ### Why does workload rightsizing matter so much in Autopilot specifically? In Autopilot, you're billed directly for what your workloads request, not what they use, so rightsizing requests is the primary lever for reducing cost in that mode. ### Does Sedai work alongside the GKE cluster autoscaler? Yes. By consolidating workloads and increasing node utilization, Sedai helps the cluster autoscaler identify and terminate idle nodes more effectively, rather than replacing its logic. ### Can Sedai optimize GPU workloads? Yes. Sedai identifies idle GPU capacity, right-sizes workloads to more cost-effective GPU types, and can partition physical GPUs across multiple smaller workloads to increase density. --- # Google Compute Engine Source: https://sedai.io/platform/google-compute-engine-optimization Sedai continuously analyzes Google Compute Engine usage and safely resizes instances and machine families — with validation and rollback protection. ## Right-Size Every GCE Instance, Safely Compute is usually the largest line item on a GCP bill, yet most VMs are sized for peak load and never revisited. Sedai continuously analyzes real usage across your fleet and safely resizes instances, switches machine families, and cleans up wasted storage — without manual effort or outage risk. ## Compute Is the Biggest Line on Your Bill. It's Also the Hardest to Safely Trim. GCE instances are provisioned for the load they might see, not the load they actually carry — and revisiting that at scale is where teams get stuck. ### Instances are provisioned for peak, not typical, load. This is especially wasteful for idle dev environments and latency-sensitive apps sized with heavy safety margin. ### Manual rightsizing carries real risk. A single bad resize can cause an outage, so teams avoid touching thousands of VMs even when they know they're oversized. ### Waste doesn't stop at the VM. Orphaned disks left behind after decommissioning, oversized storage tiers, and underutilized GPUs all keep costing money long after they stop being useful. ## How We Help ### VM Rightsizing & Family Optimization Sedai analyzes real usage and recommends smaller instances, cross-family moves (like N2 to E2), or burstable types for workloads with low average but spiky CPU demand. ### Persistent Disk Optimization Sedai matches disk types to actual I/O patterns to eliminate overpaying for unused performance, and automatically identifies orphaned disks left behind after VM deletion. ### Safe, Validated Execution Every change runs through defined maintenance windows and is validated against system health afterward, with automatic rollback if anything looks wrong — no manual babysitting required. ## Your Biggest Bill Item Shouldn't Be Your Biggest Blind Spot. ## Frequently Asked Questions ### How does Sedai decide when to recommend a different machine family, versus just a smaller size in the same family? Sedai first checks whether a smaller instance in the same family fits your usage. If a different family (like E2 instead of N2) offers better cost-performance for that workload's profile, Sedai recommends the cross-family move instead. ### What happens if a workload has occasional CPU spikes? Won't rightsizing hurt performance during those spikes? Sedai looks for exactly this pattern and often recommends burstable instance types, which keep baseline costs low while still providing burst capacity when the workload actually needs it. ### How does Sedai avoid causing an outage when it resizes a live instance? Changes run through defined maintenance windows and are validated against system health immediately after execution. If anything looks off, Sedai automatically rolls back the change. ### Does Sedai clean up orphaned disks automatically, or just flag them? Sedai identifies orphaned Persistent Disks left behind after VM deletion and helps you remove them — eliminating a common, easy-to-miss source of ongoing waste. ### Does this work for VMs behind a load balancer? Yes. Sedai's optimization applies to individual VMs, including those running as part of a load balancer's backend service. --- # Google Cloud Dataflow Source: https://sedai.io/platform/google-cloud-dataflow-optimization Sedai continuously analyzes Dataflow job performance and recommends the right instance types and worker counts — savings reports, reviewed before acting. ## Right-Size Every Dataflow Job's Workers Dataflow streaming jobs are easy to over-provision. The wrong instance type or worker count quietly inflates cost with no performance benefit. Sedai analyzes historical job performance and recommends the instance types and worker counts that fit actual demand, with projected savings you can review before acting. ## The Right Dataflow Configuration Isn't a Guess. It's a Moving Target. Dataflow doesn't reward over-provisioning the way other services do — it punishes the wrong configuration, and that configuration changes as job behavior evolves. ### Instance type and worker count are easy to get wrong. Both are typically set once, without a clear view into how the job actually performs over time. ### Streaming jobs don't behave consistently. CPU, memory, and worker needs shift as data volume and processing patterns change, so a config that fit last quarter may not fit now. ### Revisiting configuration requires ongoing analysis, not a one-time fix. Without continuous visibility into historical usage, teams have no reliable signal for when a job's setup is due for a change. ## How We Help ### Automated Job Analysis Sedai runs scheduled background analysis across eligible Dataflow jobs, continuously collecting CPU, memory, and worker utilization data — no manual monitoring required. ### Instance & Worker Recommendations Based on historical usage patterns, Sedai recommends more cost-effective instance types and optimal worker counts tailored to each job's actual behavior. ### Savings Reports Before You Act Every recommendation is saved to a report with projected cost savings, so you can review the impact and make an informed decision before changing anything. ## Stop Guessing at Dataflow Worker Counts. ## Frequently Asked Questions ### Does Sedai automatically apply these changes to my Dataflow jobs? Not currently. Sedai analyzes your jobs and generates recommendations with projected cost savings, which you review and apply — it doesn't execute changes autonomously for Dataflow. ### How often does Sedai analyze my Dataflow jobs? Sedai runs a scheduled analysis in the background on all eligible GCP accounts where this optimization is enabled, so recommendations stay current as job behavior changes. ### What data does Sedai use to generate a recommendation? Sedai collects historical performance metrics for each job, including average and maximum CPU and memory utilization, and the average number of workers used. ### What do I get from a recommendation report? Each report includes the recommended instance type and worker count along with projected cost savings, giving you a clear view of the impact before you make any changes. --- # Google Cloud Storage Source: https://sedai.io/platform/google-cloud-storage-optimization Sedai continuously analyzes Google Cloud Storage access patterns and recommends the lowest-cost class for every object, plus lifecycle-rule insights. ## Stop Paying Standard Rates for Cold Data Most GCS data lands in the Standard storage class by default, regardless of how often it's actually accessed — an expensive fit for logs, backups, and historical data. Sedai continuously analyzes object-level access patterns and recommends the lowest-cost storage class that still meets your needs. ## Storage Class Shouldn't Be an Accident. GCS defaults nearly everything to Standard, the class built for frequently accessed data — which makes it the most expensive place for data that isn't. ### Default settings don't account for access frequency. Default settings don't account for access frequency. Logs, backups, and historical analytics data often sit in Standard long after anyone actually needs fast access to them. ### The right tier depends on a pattern, not a one-time decision. Nearline, Coldline, and Archive each fit a different access frequency, and that frequency changes as data ages. ### Tracking access patterns object-by-object isn't practical manually. Without continuous visibility, dormant data stays in an expensive tier well past the point it should have moved. ## How We Help ### Continuous Access Pattern Analysis Sedai monitors access frequency across every object in your buckets as an automated background process — no manual auditing of storage patterns required. ### Storage Class Transition Recommendations Based on observed access patterns, Sedai recommends the right tier (Standard, Nearline, Coldline, or Archive) so each object sits in the lowest-cost class that still fits its use. ### Lifecycle Rule & Bucket-Level Insights Sedai flags existing lifecycle rules that could transition dormant data sooner, and identifies entire buckets where a different default storage class would save money. ## Cold Data Doesn't Belong in a Hot Storage Tier. ## Frequently Asked Questions ### Does Sedai automatically move data between storage classes? Not currently. Sedai analyzes access patterns and generates storage class recommendations, which are made available in the platform for your review — it doesn't execute transitions autonomously. ### How does Sedai decide which storage class fits an object? Sedai continuously monitors how often each object is accessed and matches that pattern to the appropriate class — for example, recommending Nearline for data untouched for over 30 days. ### What's the difference between Nearline, Coldline, and Archive recommendations? Nearline fits data accessed less than once a month, Coldline less than once a quarter, and Archive is for long-term data you rarely, if ever, need to retrieve. ### Can Sedai help with lifecycle rules I've already set up? Yes. Sedai reviews existing lifecycle rules and flags ones that could be tightened to move long-dormant data to cheaper tiers sooner than they currently do. ### Does this only work at the object level, or can it evaluate entire buckets? Both. Sedai analyzes individual objects for transition opportunities, and can also flag buckets where the overall access pattern suggests a different default storage class would be more cost-effective. --- # Google Persistent Disk Source: https://sedai.io/platform/google-persistent-disk-optimization Sedai optimizes Google Persistent Disk autonomously, matching disk type to real I/O and clearing unattached disks — with validated, safe execution. ## Match Every Disk to Its Actual I/O Persistent Disks are often provisioned for peak performance and left that way indefinitely — and unattached disks keep costing money long after the VM they served is gone. Sedai continuously analyzes I/O patterns and attachment state, and can recommend, assist, or autonomously apply the right fix, depending on the mode you choose. ## Disk Performance Is Provisioned Once. Usage Changes Constantly. Two patterns quietly waste money on Persistent Disks: paying for I/O capacity you don't use, and paying for disks nothing is even using. ### High-performance disks are provisioned for peak, then forgotten. A pd-ssd sized for worst-case I/O keeps costing the same even after usage settles into a normal, lower pattern. ### The mismatch is between disk type and actual behavior. Provisioned performance and observed IOPS or throughput drift apart over time, and nothing flags that gap on its own. ### Unattached disks keep billing after the VM is gone. Disks decommissioned alongside a VM are easy to forget and continue incurring cost indefinitely. ## How We Help ### Disk Performance Profiling Sedai collects IOPS, throughput, and configuration data for every disk, using machine learning to build a performance profile based on how it's actually used. ### Recommendations or Autonomous Action, Your Choice Over-provisioned disks and unattached disks are surfaced as recommendations in Datapilot and Copilot modes, or resolved automatically in Autopilot — the level of control is yours. ### Safe, Validated Execution in Autopilot When running autonomously, Sedai applies changes through maintenance windows, validates system health afterward, and automatically rolls back if any issues are detected. ## Don't Pay Peak Prices for Average I/O. ## Frequently Asked Questions ### Does Sedai automatically change my disk type or delete unattached disks? It depends on the mode you choose. In Datapilot, Sedai only recommends changes for your review. In Copilot, you get the same recommendations with more built-in support to act on them. In Autopilot, Sedai applies changes autonomously. ### What's the difference between Datapilot, Copilot, and Autopilot for Persistent Disk? Datapilot surfaces recommendations only. Copilot builds on that with more guided support. Autopilot goes further and executes changes autonomously, validating system health afterward and rolling back automatically if issues arise. ### What data does Sedai use to evaluate a disk? Sedai collects performance metrics like IOPS and throughput from Cloud Monitoring, along with the disk's configuration — type, size, and attachment state — to build a usage profile over time. ### What kind of disk type change might Sedai recommend or apply? A common example is a pd-ssd disk with consistently low IOPS and throughput being changed to pd-balanced or pd-standard, reducing cost without affecting the application. ### How does Autopilot mode avoid risking application performance? In Autopilot, Sedai executes changes through maintenance windows and validates system health immediately afterward, automatically rolling back if anything looks wrong — so the safety net doesn't depend on a human reviewing it first. --- # Data Platforms Optimization Source: https://sedai.io/platform/data-platforms Data platform optimization to reduce costs, improve performance, and boost efficiency. Continuously tune resources without manual effort. ## Data Platform Optimization You Can Trust in Prod Sedai doesn’t just recommend data optimizations. It makes them. Safely. ## Optimize Data Platforms with Superintelligence Sedai isn’t another recommendation tool. It optimizes your data platforms for you. By deeply understanding your infra, Sedai rightsizes compute, reduces termination timeouts, and provides better cost breakdowns. ### Autonomous Rightsizing Autonomously rightsizes driver & executor nodes based on your real CPU & memory utilization. No manual tuning needed. ### Release-Aware Optimization & Regression Detection Understands how releases impact performance & cost. Sedai only optimizes when behavior is consistent & regression-free. ### Performance & Availability Optimization Detects saturation & latency early. It adjusts resources based on real workload signals to keep data platforms fast & stable. “Sedai has helped us save millions by optimizing and managing our own back-end services. But most importantly, Sedai has allowed us to respond in real time when anomalies are detected.” — Suresh Sangiah, SVP of Engineering // Palo Alto Networks ## What Makes Sedai Different? Get safe, outcome-driven optimization at scale. Designed to act on real application behavior, with safeguards built into every decision. ### Safe Autonomy ### Full-Stack Cloud Coverage ### Enterprise-Grade Governance ## Autonomy That Delivers Powered by real app behavior. Cloud Savings Performance Gain Failed Customer Interaction Reduction ## Optimize Your Entire Stack Sedai makes your stack smarter and safer. ### Databricks ### GCP ### Storage ### test ## Optimize Data Platforms On Autopilot ## FAQs ### Does Sedai optimize queries, schemas, or code? No. Sedai doesn’t modify queries, schemas, or code. It optimizes by rightsizing compute, storage, & performance settings based on real workload behavior. ### How does Sedai reduce database compute costs? Sedai identifies oversized compute based on real CPU & memory usage. It rightsizes resources to remove unused capacity, which directly lowers compute cost. ### What data platforms does Sedai support? Today, Sedai supports Databricks.integrates with major cloud data platforms, including AWS EMR, GCP Dataflow, GCP BigQuery, and Databricks. ### Who owns platform optimization decisions? You do. You choose the level of autonomy Sedai operates under: recommendations only, approval-based actions, or fully autonomous optimization. ### How does Sedai ensure performance doesn't degrade? Sedai learns your normal performance and only applies changes when key signals like CPU, memory, & latency are stable. It keeps monitoring and can roll back if performance drops. --- # Database Optimization Source: https://sedai.io/platform/database Database optimization to reduce costs, improve query performance, and ensure reliability. Continuously tune databases without manual effort. ## Database Optimization You Can Trust in Prod Sedai doesn’t just recommend database optimizations. It makes them. Safely. ## Optimize Databases with Superintelligence Sedai optimizes databases as part of the application, not in isolation. It balances cost efficiency with performance and reliability. ### Autonomous Database Rightsizing Continuously tune instance class, CPU, memory, & I/O capacity to meet latency/ throughput targets at the lowest cost. ### Intelligent Storage & Backup Optimization Optimize storage class, provisioned IOPS, autoscaling thresholds, & backup retention to reduce waste without compromising durability or recovery. ### Connection & Concurrency Management Prevent saturation by tuning max connections, pool sizes, & throttling policies in line with application demand. ## What Makes Sedai’s Database Optimization Safe Database optimization is critical for app performance. And Sedai treats it that way. With every optimization made with full app-behavior awareness & within your guardrails, Sedai never optimizes unless it won’t break prod. How Sedai does it: - Checks optimizations against latency, availability, & error thresholds - Keeps changes within SLOs & error budgets - Continuously optimizes as demand changes - Provides full impact visibility of each optimization “Sedai has helped us save millions by optimizing and managing our own back-end services. But most importantly, Sedai has allowed us to respond in real time when anomalies are detected.” — Suresh Sangiah, SVP of Engineering // Palo Alto Networks ## What Makes Sedai Different? Get safe, outcome-driven optimization at scale. Designed to act on real application behavior, with safeguards built into every decision. ### Application-Aware Intelligence ### Safe Autonomy ### Full-Stack Cloud Coverage ## Autonomy That Delivers Powered by real app behavior. Cloud Savings Performance Gain Failed Customer Interaction Reduction ## Optimize Your Entire Stack Sedai makes your stack smarter and safer. ### AWS ### Storage ### test ## Optimize Databases On Autopilot ## FAQs ### What is Sedai's database optimization platform? Sedai is an autonomous database optimization platform that continuously tunes instance class, CPU, memory, I/O, storage, and backup settings to balance cost, performance, and reliability. Unlike recommendation-only tools, it makes safe changes directly in production, validated against latency, availability, and error thresholds. ### Which databases does Sedai support? Sedai optimizes Amazon RDS and Amazon DynamoDB, plus databases running across AWS, Azure, GCP, and Kubernetes environments. Optimization is application-aware, so database changes are coordinated with the rest of your stack rather than made in isolation. ### How does Sedai keep database optimization safe in production? Every change stays within your SLOs and error budgets, applied incrementally with real-time validation and automatic rollback if any risk is detected. No optimization is made unless Sedai has confirmed it won't break production. ### How much can Sedai reduce database costs? Sedai customers see up to 50% cloud savings, 75% performance gains, and 70% fewer failed customer interactions. For example, KnowBe4 cut average response time from 18.5 seconds to 80 milliseconds and saved $1.2M in AWS costs. ### How long does it take to set up Sedai for databases? Initial onboarding typically takes about 15 minutes for Sedai to start reading metrics from your environment, with extra time for integrations depending on complexity. A free tier and 30-day trial are available, with volume-based pricing on the resources optimized. --- # On-Prem Optimization Source: https://sedai.io/platform/on-prem On-prem optimization to improve utilization, reduce waste, and align capacity with demand. Continuously adapt workloads without manual tuning. ## On-Prem Isn’t Built for Change. Sedai Is. On-prem hardware is fixed. Workloads aren’t. Sedai continuously & safely tunes Kubernetes, GPU, & data platform resources to stay within capacity. Even as demand shifts. ## Supported Technology for On-Prem ### Compute Optimization ### Sedai keeps on-prem Kubernetes & GPU workloads tuned to real demand. They stay within your hardware’s capacity limits without constant manual adjustment. ### Data Optimization ### Sedai optimizes Databricks workloads running in your AWS or Azure environment by aligning compute resources with actual workload behavior. Right size clusters and track true job costs. ### test ### test ## Your On-Prem Runs Better on Sedai Sedai doesn’t risk your prod when it optimizes on-prem environments. It learns your real workload behavior so every adjustment stays incremental, SLO-aware, & reversible. Keep your fixed hardware aligned with changing demand. ### Application-Aware Intelligence ### Outcome-Driven Optimization ### Safe Autonomy ### Full-Stack Cloud Coverage ### Enterprise-Grade Governance ## This Is What Intelligent Optimization Looks Like Powered by real app behavior. Cloud Savings Performance Gain Failed Customer Interaction Reduction ## See Sedai in Action ## FAQs ### What is Sedai's on-premises optimization platform? Sedai autonomously and safely tunes Kubernetes, GPU, and data platform workloads within your fixed on-prem hardware capacity, adapting as demand shifts. It keeps environments efficient without constant manual intervention. ### How does Sedai work within fixed on-prem hardware limits? Sedai uses machine learning to monitor real workload behavior and incrementally adjust resources so workloads stay within your hardware's capacity, even as demand changes. ### Is Sedai's on-prem deployment agentless? Yes. Sedai connects securely to your on-prem environment using IAM, with no complex installations or additional agents required. ### How does Sedai keep on-prem optimizations safe? As the only platform patented for safe autonomous optimization in production, Sedai makes slow, validated, SLO-aware changes and rolls back if risk is detected. ### What results can Sedai deliver on-prem? Sedai delivers up to 50% cost savings, 75% performance gains, and 70% fewer failed customer interactions by continuously optimizing on-prem workloads. --- # GCP Optimization Source: https://sedai.io/platform/gcp GCP optimization with AI to cut costs, boost performance, and protect SLOs across GKE and VMs. See how Sedai automates safe tuning. ## Sedai Makes GCP Optimization Production Safe GCP is powerful, but a misconfigured GKE or GCE VM setting can break prod. Sedai safely optimizes based on real workload behavior to cut costs and maintain reliability. ## Supported Technology for GCP ### Compute Optimization ### Sedai right-sizes GKE and Google Compute Engine (GCE) based on actual demand patterns. Keep scaling stable as workloads evolve. ### Storage Optimization ### Sedai adjusts Cloud Storage tiers & Persistent Disk types as usage changes. Autonomously keep storage efficient. ### Data Optimization ### Sedai tunes Dataflow, BigQuery, & Cloud SQL to real query & pipeline behavior. Keep jobs fast and queries reliable. ### test ### test ## GCP Runs Better on Sedai Sedai doesn’t risk your prod when it optimizes GCP. It learns your real workload behavior so every change stays incremental, SLO-aware, and reversible. Get continuous optimization that adapts to shifting demand without manual tuning. ### Application-Aware Intelligence ### Outcome-Driven Optimization ### Safe Autonomy ### Full-Stack Cloud Coverage ### Enterprise-Grade Governance ## This Is What Intelligent Optimization Looks Like Powered by real app behavior. Cloud Savings Performance Gain Failed Customer Interaction Reduction ## See Sedai in Action ## FAQs ### What is Sedai's GCP optimization platform? Sedai safely optimizes Google Cloud by right-sizing GKE and Google Compute Engine (GCE) based on real workload behavior, cutting costs while maintaining reliability. Every change stays incremental, SLO-aware, and reversible. ### Which GCP technologies does Sedai support? Sedai right-sizes GKE and Google Compute Engine (GCE) based on actual demand patterns, and covers compute, storage, data, and AI workloads from one platform. ### How does Sedai optimize GCP without breaking production? Sedai understands application behavior - golden signals, dependencies, and seasonality - not just CPU or memory, and applies gradual changes with continuous safety checks. If a change risks stability, it reverses it. ### How does Sedai govern GCP changes? Sedai integrates with IaC, ITSM, and security workflows, so every change to production is tracked, governed, and compliant. ### How much can Sedai save on GCP? Sedai delivers up to 50% cloud savings, 75% performance gains, and 70% fewer failed customer interactions on GCP. --- # Azure Optimization Source: https://sedai.io/platform/azure Azure optimization for AKS and VMs to reduce costs, improve performance, and protect SLOs. Continuously tune workloads without manual effort. ## Sedai Makes Azure Optimization Production Safe Azure is powerful, but one wrong VM or AKS change can break prod. Sedai prevents that with safe, autonomous optimization that cuts cost & keeps Azure reliable. ## Supported Technology for Azure ### Compute Optimization ### Sedai automatically rightsizes AKS workloads & Azure VMs as demand shifts. Keep capacity aligned and prevent bottlenecks before they surface. ### Storage Optimization ### Sedai automatically picks the right Blob tiers & Managed Disk types as usage patterns change. Keep storage efficient while protecting throughput & latency. ### Data Optimization ### Sedai tunes Azure SQL by adjusting compute & storage around actual query behavior. Keeps databases responsive and stable as demand shifts. ### test ### test ## Azure Runs Better on Sedai Sedai optimizes Azure by learning real workload behavior and making incremental, SLO-aware, reversible changes. You get continuous optimization that reduces waste and maintains performance. All without manual tuning. ### Application-Aware Intelligence ### Outcome-Driven Optimization ### Safe Autonomy ### Full-Stack Cloud Coverage ### Enterprise-Grade Governance ## This Is What Intelligent Optimization Looks Like Powered by real app behavior. Cloud Savings Performance Gain Failed Customer Interaction Reduction ## See Sedai in Action ## FAQs ### What is Sedai's Azure optimization platform? Sedai is a patented, autonomous platform that safely optimizes Azure - including AKS and VMs - by making gradual, SLO-aware, reversible changes. It reduces cost and prevents outages without manual intervention. ### Which Azure technologies does Sedai support? Sedai optimizes Azure Kubernetes Service (AKS) and Azure VMs, and covers compute, storage, and data workloads. It rightsizes capacity as demand shifts to prevent bottlenecks before they surface. ### How does Sedai ensure safety during Azure optimization? As the only platform patented for safe autonomous optimization in production, Sedai makes incremental changes with continuous validation and never applies risky all-at-once changes. If a change risks an SLO, it's rolled back. ### How does Sedai handle governance for Azure changes? Sedai integrates with Infrastructure as Code (IaC), ITSM, and security workflows, so every production change is tracked, governed, and compliant. ### How much can Sedai save on Azure? Sedai delivers up to 50% cloud savings, 75% performance gains, and 70% fewer failed customer interactions in Azure environments. --- # AWS Optimization Source: https://sedai.io/platform/aws AWS optimization with AI to cut costs, boost performance, and protect SLOs. Automate safe tuning across EC2, ECS, and more. ## The Only Safe Way To Optimize AWS Your AWS bill is too high. But optimization is risky. Sedai autonomously cuts waste & tunes performance. All without breaking prod. ## Supported Technology for AWS ### Compute Optimization ### Sedai tunes EKS, ECS, EC2/Fargate, & Lambda in real time to match demand. Eliminate overprovisioning while keeping services responsive. ### Storage Optimization ### Sedai continuously optimizes S3 & EBS by adjusting storage types, tiers, & performance. Each change tracks real workload usage so storage stays efficient. ### Data Optimization ### Sedai optimizes RDS & EMR by aligning compute & storage to real behavior. This ensures queries & jobs have the right resources at the right time. ### test ### test ## AWS Runs Better on Sedai Sedai doesn’t risk your prod when it optimizes AWS. It learns your real workload behavior so every change stays SLO-aware & reversible. Get continuous optimization that adapts as your AWS environments evolve. All without manual tuning. ### Application-Aware Intelligence ### Outcome-Driven Optimization ### Safe Autonomy ### Full-Stack Cloud Coverage ### Enterprise-Grade Governance ## This Is What Intelligent Optimization Looks Like Powered by real app behavior. Cloud Savings Performance Gain Failed Customer Interaction Reduction ## See Sedai in Action ## FAQs ### What is Sedai's AWS optimization platform? Sedai autonomously tunes AWS environments - including EKS, ECS, EC2/Fargate, and Lambda - to cut costs and improve performance without risking production. It learns real workload behavior and makes SLO-aware, reversible changes as your environment evolves. ### Which AWS services does Sedai optimize? Sedai supports Amazon EKS, ECS, EC2, Fargate, and Lambda, tuning them in real time to match demand and remove overprovisioning. It also integrates with AWS CloudWatch and tools like Datadog, Dynatrace, New Relic, and Prometheus. ### How does Sedai keep AWS changes safe? Sedai uses safe autonomy - gradual, reversible changes with continuous safety checks, all kept SLO-aware to minimize outage risk. Every change is tracked and governed through IaC and ITSM workflows. ### How is Sedai different from other AWS optimization tools? Sedai is fully autonomous, executing optimizations in production in real time rather than only recommending them. It adapts to evolving workloads and integrates with governance and compliance workflows. ### How much can Sedai save on AWS? Sedai delivers up to 50% cloud savings, 75% performance gains, and 70% fewer failed customer interactions across AWS environments. --- # Storage Optimization Source: https://sedai.io/platform/storage Storage optimization for S3 and EBS to cut costs and improve performance. Continuously optimize storage tiers and usage without manual effort. ## Storage Optimization You Can Trust in Prod Sedai doesn’t just recommend storage optimizations. It makes them. Safely. ## Optimize Storage with Superintelligence Sedai isn’t another recommendation tool. It optimizes your storage for you. By deeply understanding your infra, Sedai tunes volumes for performance & cost. Always based on your goals. ### Autonomous Storage Rightsizing Automatically adjusts storage tier, and performance settings based on real usage patterns. No manual tuning needed. ### Usage & Cost-Aware Optimization Understands how applications consume storage over time and applies changes that reduce waste & lower storage costs. ### Performance & Availability Optimization Detects latency, IOPS pressure, & saturation early, adjusting storage settings to keep systems fast & reliable. “Sedai has helped us save millions by optimizing and managing our own back-end services. But most importantly, Sedai has allowed us to respond in real time when anomalies are detected.” — Suresh Sangiah, SVP of Engineering // Palo Alto Networks ## What Makes Sedai Different? Get safe, outcome-driven optimization at scale. Designed to act on real application behavior, with safeguards built into every decision. ### Application-Aware Intelligence ### Safe Autonomy ### Full-Stack Cloud Coverage ## Autonomy That Delivers Powered by real app behavior. Cloud Savings Performance Gain Failed Customer Interaction Reduction ## Optimize Your Entire Stack Sedai makes your stack smarter and safer. ### AWS ### GCP ### Azure Coming Soon ### Storage ### test ## Optimize Storage On Autopilot ## FAQs ### What is Sedai's approach to cloud storage optimization? Sedai is a patented, autonomous platform that optimizes storage cost and performance directly in production, adjusting tier and performance settings based on real usage. Unlike recommendation-only tools, it safely makes the changes itself, validating each one to avoid incidents or SLO breaches. ### Which storage types does Sedai support? Sedai optimizes AWS EBS, AWS S3, plus storage across GCP, Azure, and Kubernetes environments from a single platform. It coordinates storage changes with the rest of your stack rather than treating storage in isolation. ### How does Sedai reduce storage costs without hurting performance? Sedai learns how applications consume storage over time and right-sizes tier, capacity, and performance settings to cut waste. It detects latency, IOPS pressure, and saturation early, adjusting before performance degrades. ### How is Sedai different from storage recommendation tools? Recommendation tools only suggest changes; Sedai safely executes them in production. Each change is validated, gradual, and reversible, so savings are realized without manual follow-up. ### What results can Sedai deliver for storage? Sedai delivers up to 50% cloud savings, 75% performance gains, and 70% fewer failed customer interactions by autonomously optimizing storage in production. --- # Serverless Optimization Source: https://sedai.io/platform/serverless Serverless optimization to reduce costs, improve efficiency, and protect SLOs. Continuously tune functions without manual intervention. ## Serverless Optimization You Can Trust in Prod Sedai doesn’t just recommend serverless optimizations. It makes them. Safely. ## Serverless Optimization with Superintelligence Sedai isn’t another recommendation tool. It autonomously optimizes your serverless functions for you. By deeply understanding your infra, Sedai rightsizes memory, concurrency, and performance settings. Always based on your goals. ### Autonomous Function Rightsizing Automatically adjusts memory, timeouts, & throttles based on real function behavior. Cut costs without sacrificing performance. ### Concurrency Management Manages provisioned and reserved concurrency based on functions actually run. No more constant tweaks. No more guesswork. ### Performance & Availability Optimization Detects saturation, throttling, & slowdowns early. It adjusts settings based on app behavior to keep functions fast and reliable. “Sedai has helped us save millions by optimizing and managing our own back-end services. But most importantly, Sedai has allowed us to respond in real time when anomalies are detected.” — Suresh Sangiah, SVP of Engineering // Palo Alto Networks ## What Makes Sedai Different? Get safe, outcome-driven optimization at scale. Designed to act on real application behavior, with safeguards built into every decision. ### Application-Aware Intelligence ### Safe Autonomy ### Full-Stack Cloud Coverage ## Autonomy That Delivers Powered by real app behavior. Cloud Savings Performance Gain Failed Customer Interaction Reduction ## Optimize Your Entire Stack Sedai makes your stack smarter and safer. ### AWS ### Storage ### test ## Serverless Optimization On Autopilot ## FAQs ### What is Sedai's serverless optimization platform? Sedai autonomously rightsizes and manages serverless functions in production, tuning memory, concurrency, timeouts, and throttles based on real function behavior. It's the only patented platform that makes these changes safely without causing incidents or breaching SLOs. ### Does Sedai optimize AWS Lambda? Yes. Sedai provides deep AWS Lambda optimization, including autonomous rightsizing, provisioned and reserved concurrency management, and performance tuning - all with safety-first guardrails. ### How does Sedai manage concurrency for serverless functions? Sedai manages provisioned and reserved concurrency based on actual usage, removing the need for constant manual tweaks while keeping functions fast and cost-efficient. It detects saturation and throttling early and adjusts before users are affected. ### What results can Sedai deliver for serverless workloads? For serverless, Sedai delivers up to 25% cloud savings, 38% performance gains, and 35% fewer failed customer interactions. Belcorp, for example, reduced AWS Lambda latency by 77% with Sedai. ### Is Sedai safe to run on serverless in production? Yes. Every optimization is constrained, continuously validated, and reversible, with automatic rollback if risk is detected - a patented approach designed for zero incidents and no SLO breaches. --- # ECS Optimization Source: https://sedai.io/platform/ecs ECS optimization with AI to cut costs, boost performance, and maintain SLOs. Automate safe tuning for AWS ECS, no manual effort needed. ## ECS Optimization You Can Trust in Prod Sedai doesn’t just recommend ECS optimizations. It makes them. Safely. ## Optimize ECS with Superintelligence Sedai isn’t another recommendation tool. It optimizes ECS for you. By deeply understanding your infra, Sedai rightsizes services & tasks. Always based on your goals. ### Autonomous Task & Service Rightsizing Autonomously adjusts CPU, memory, & task size based on real service behavior. No manual tuning needed. ### Release-Aware Optimization & Regression Detection Tracks each deployment & detects service-level regressions, using releases to understand behavior changes. And it only optimizes when a release is stable. ### Performance & Availability Optimization Optimizes performance & service capacity based on workload behavior. Keep ECS fast, stable, & predictable across EC2 or Fargate. “Since going live with Sedai, we've achieved remarkable cost savings: up to 50% in production & 87% in development for ECS and up to 99.3% in Lambda—all within just five months without any incidents." — Matt Duren, VP of Engineering // KnowBe4 ## What Makes Sedai Different? Get safe, outcome-driven optimization at scale. Designed to act on real application behavior, with safeguards built into every decision. ### Application-Aware Intelligence ### Safe Autonomy ### Full-Stack Cloud Coverage ## Autonomy That Delivers Powered by real app behavior. Cloud Savings Performance Gain Failed Customer Interaction Reduction ## Optimize ECS On Autopilot ## FAQs ### What is Sedai's ECS optimization? Sedai autonomously rightsizes Amazon ECS by adjusting CPU, memory, and task sizes based on real service behavior, then safely implements those changes in production. It goes beyond recommendations, acting only when a change is proven safe for performance. ### Does Sedai support both ECS on EC2 and Fargate? Yes. Sedai optimizes ECS workloads running on both EC2 and Fargate, keeping performance fast and predictable across your entire ECS environment. ### How does Sedai avoid breaking ECS workloads when it optimizes? Sedai is release-aware - it tracks each deployment, detects regressions, and only optimizes when a release is stable. Changes are gradual and reversible, so production stays protected. ### How much can Sedai save on ECS? Customers have achieved up to 50% cost savings in production and 87% in development on ECS, with up to 75% performance gains - KnowBe4 reached these results within five months and with zero incidents. ### How quickly can I get started with Sedai for ECS? General setup takes about 5 minutes, with ECS-specific use cases typically ready in around 15 minutes, and most teams see first value within 14 days. Live onboarding, documentation, and a Slack community are available. --- # AI Agent Optimization Source: https://sedai.io/platform/ai-agent-optimization Sedai brings governance, observability, and intelligent model routing to every LLM call — one import, no code rewrites. ## Take Control of Your AI Spend Sedai for AI Agent Optimization brings governance, observability, and intelligent routing to every LLM call, without a single line of rewritten code. ## One Platform. Optimize Everything. Most tools address one layer — infrastructure, observability, or routing. Sedai covers the full stack. Cloud optimization, agent governance, and intelligent model routing, unified in a single platform. ## Four Capabilities. One SDK. Managing LLM usage across teams and providers is its own operational challenge. Sedai closes the gap. ### Observability Real-time cost, token, and latency visibility consolidated across every provider, project, and model. ### Governance Org and project-level model access policies with automatic fallback routing, enforced without developer self-governance. ### Reliability Automatic retries, cross-provider fallbacks, and load balancing. Built in. Configured once. ### Smart Routing AI-powered routing trained on your actual production traffic, not public benchmarks. Continuously adapts as models change. ## The Right Model for Every Prompt, Automatically New models ship every week. A choice that was optimal last quarter may cost twice as much, or score lower on accuracy, today. Sedai's Smart Routing is built on your actual queries, not someone else's dataset. **Traffic-Aware Routing Groups**. Sedai auto-clusters production prompts into distinct groups by domain and task type. Each group gets its own model set and optimization preference. **Pareto-Optimal Model Selection**. Every candidate model is tested on every routing group for accuracy, cost, and latency. You choose the trade-off that fits your product. **Manual and Auto Modes**. Manual gives you full control over groups, models, and evaluators. Auto handles everything end-to-end. No code changes in either mode. ## AI Agent Cost & Efficiency Intelligence Most tools show you where AI spend goes. Sedai knows why it's happening and fixes it for you. ### Actionable LLM Cost Visibility See exactly where LLM spend lives across every provider, team, and project, and how optimization reduces cost over time. Sedai turns fragmented usage data into actions for measurable, ongoing savings. ### The Right Model at the Right Cost Stop paying for more model than you need. Sedai continuously matches each agent to the optimal model for its actual workload, balancing accuracy, latency, and cost without manual benchmarking. ### Waste Detection Across Every Agent Find inefficiencies hiding across your agent fleet — stale model choices, over-provisioned calls, unattributed spend. Sedai surfaces them and routes around them automatically. “By having Sedai in place, we’re not just saving money, we’re preventing would-be customer problems before they become an issue.” — Matt Duren, VP of Engineering // KnowBe4 ## How Sedai Optimizes AI Agents Built on real production behavior, with safeguards at every step. ### Learns Your Traffic ### Decides With Context ### Acts Safely Without Code Changes ## AI Agent Optimization That Delivers LLM Spend Reduction Response Accuracy Improvement Reduced Time Spent on Model Evaluation ## Supported LLM Providers ### OpenAI ### AWS Bedrock ### Vertex AI ### Azure Foundry ## Other Tools Stop at Visibility. Sedai Adds Control. | Other Solutions | Sedai | | --- | --- | | Observability without governance or routing | Governance, observability, reliability, and routing in one platform | | Cost-focused routing based on static rules | Accuracy- and cost-aware routing trained on your production traffic | | Gateway-based routing adds 20–40ms per call | SDK-based middleware with sub-millisecond overhead | | Separate tools for tracing, governance, and routing | One import line covers all four pillars | | Code rewrites required to change providers | Zero code changes — developers keep working as before | ## One Import Line. No code rewrites. Two to three week onboarding. Full Control Over Every LLM Call. ## FAQs ### What is Sedai for AI Agent Optimization? A middleware layer that brings governance, observability, reliability, and intelligent model routing to every LLM call — without changing how your agents work. ### Does it require changes to my existing agent code? No. Sedai intercepts LLM calls transparently through the SDK. No interfaces change, no retry logic needs to be rewritten. ### Which LLM providers does Sedai support? Currently Sedai supports OpenAI, AWS Bedrock, Vertex AI, and Azure Foundry, with additional providers planned. ### What is Smart Routing and how is it different from other routing tools? Smart Routing builds custom routers trained on your actual production traffic — not public benchmarks. It clusters prompts into routing groups, tests candidate models across accuracy, cost, and latency, and surfaces the Pareto-optimal set per group. It adapts continuously as the model landscape evolves. ### Does Sedai replace my LLM observability tools like Langfuse or LangSmith? No — Sedai integrates with both as data sources for Smart Routing. They remain your tracing layer. Sedai adds governance, cost attribution, fallback routing, and model optimization on top. ### How long does implementation take? Typically two to three weeks from kickoff to live. ### Is this part of the Sedai platform I already use? Yes. AI Agent Optimization extends the same platform to cover your LLM spend — one vendor, one platform, covering the full stack. --- # VM Optimization Source: https://sedai.io/platform/vm VM optimization with AI that cuts costs, boosts performance, and eliminates manual tuning. See how Sedai automates safe optimization. ## VM Optimization You Can Trust in Prod Sedai doesn’t just recommend VM optimizations. It makes them. Safely. ## Optimize VMs with Superintelligence Sedai isn’t another recommendation tool. It autonomously optimizes your VMs for you. By deeply understanding your setup & usage, Sedai rightsizes instance type, vCPU, & memory. Always based on your goals. ### Autonomous Compute & Storage Rightsizing Autonomously adjusts VM instance type, vCPU, memory and storage performance settings based on real workload behavior. No manual tuning needed. ### Release-Aware Optimization Understands how deployments & workload changes impact performance & cost. It only optimizes when behavior is within safe, normal ranges. ### Performance & Availability Optimization Detects saturation, latency, & resource pressure early. It adjusts VM resources based on app behavior to keep them fast & reliable. “Sedai has helped us save millions by optimizing and managing our own back-end services. But most importantly, Sedai has allowed us to respond in real time when anomalies are detected.” — Suresh Sangiah, SVP of Engineering // Palo Alto Networks ## What Makes Sedai Different? Get safe, outcome-driven optimization at scale. Designed to act on real application behavior, with safeguards built into every decision. ### Application-Aware Intelligence ### Safe Autonomy ### Full-Stack Cloud Coverage ## Autonomy That Delivers Powered by real app behavior. Cloud Savings Utilization Gain Failed Customer Interaction Reduction ## Optimize Your Entire Stack Sedai makes your stack smarter and safer. ### AWS ### Azure ### GCP ### Storage ### test ## Optimize Virtual Machines with AI ## FAQs ### How does the VM optimization process work? Sedai observes how each VM runs. It chooses a better instance type based on CPU, memory, disk, & network behavior. Sedai then recommends or applies the change through a controlled workflow to avoid disruption. ### How does Sedai determine the right VM size? Sedai sizes VMs by watching how they actually run over time, including CPU, memory, disk, & network behavior. When the data shows a better instance type, it recommends or applies the change. ### Does Sedai optimize VMs with a gradual/incremental approach or immediate changes? Yes. Sedai resizes VMs in small, controlled steps. After each adjustment, it checks performance utilization to ensure the update won’t cause disruption. It continues if everything remains stable. ### Can Sedai scale out a VM? Yes. Sedai adjusts VM groups & scale sets by increasing or reducing instance counts based on real demand. For standalone VMs, Sedai right-sizes the instance type instead: horizontal for groups, vertical for one.