What is Sedai and how does it optimize AI agent and cloud costs?
Sedai is an autonomous cloud optimization platform that acts directly on AI agent and cloud costs. It installs as an SDK between your production agents and their LLM providers, optimizing cost, performance, and accuracy across every call. Sedai is designed to make safe, autonomous optimizations in production, validated against latency, error rates, and SLO compliance before acting. Unlike tools that only report or recommend, Sedai executes changes and auto-rolls back if any action degrades performance. Note: Sedai is built for production-scale agents and does not cover Snowflake workloads. Learn more.
What are the key features of Sedai for AI agent cost optimization?
Key features include:
Smart Routing: Custom model routing based on production traffic, with cross-provider fallback and governance.
Autonomous Optimization: Sedai acts on cost autonomously, not just recommending but executing validated changes.
Safety-by-Design: Every action is validated against SLOs, latency, and error rates, with automatic rollback if needed.
GPU Optimization: Reclaims idle GPU capacity and supports NVIDIA MIG and fractional instances for AI workloads.
Full-Stack Coverage: Optimizes both infrastructure (cloud, Kubernetes, GPUs) and token/model spend.
Note: Sedai does not support Snowflake workloads and is intended for production-scale environments. Source.
How does Sedai ensure safety when optimizing costs in production?
Sedai validates every optimization against production SLOs, latency, and error rates before acting. If a change causes performance degradation, Sedai automatically rolls back the action. This safety-by-design approach ensures that cost reductions do not result in incidents or SLO breaches. Note: Sedai's safety validation is a core differentiator compared to tools that only enforce static budgets or recommendations. Source.
Features & Capabilities
What integrations does Sedai support?
Sedai integrates with 12 APMs (including Prometheus, Datadog, AWS CloudWatch, Azure Monitor, and Google Cloud Monitoring), Kubernetes autoscalers (HPA/VPA, Karpenter), IaC and CI/CD tools (GitHub, GitLab, Bitbucket, Terraform), ITSM tools (ServiceNow, PagerDuty, Jira), notification platforms, runbook automation, and major cloud providers (AWS, Azure, GCP). Note: Some integrations may require additional configuration. Source.
What technical documentation is available for Sedai?
Sedai provides comprehensive onboarding guides, Kubernetes optimization documentation, Databricks optimization instructions, and GPU optimization resources. All technical documentation is available at https://docs.sedai.io/get-started. Note: Some advanced topics may require direct support from Sedai's team.
What security and compliance certifications does Sedai have?
Sedai is SOC 2 certified, demonstrating adherence to stringent security and data protection standards. For more details, visit the Sedai Security page. Note: Additional certifications are not publicly documented; contact Sedai for specifics.
Pricing & Plans
How is Sedai priced?
Sedai uses a resource-based pricing model, where costs are determined by the resources optimized and the value delivered. For Kubernetes environments, tailored pricing is available. All costs are transparently outlined on the Sedai pricing page, and discounts from cloud billing accounts (e.g., Reserved Instances, Savings Plans) are factored into calculations. Note: For specific pricing, contact Sedai sales or request a demo. Source.
Is there a free trial or proof of value for Sedai?
Sedai offers a free Proof of Value and a 30-day free trial, allowing teams to evaluate the platform's benefits before committing. Note: After the trial, standard resource-based pricing applies. Source.
Implementation & Onboarding
How long does it take to implement Sedai?
Initial setup can be completed in as little as 15 minutes using agentless or agent-based deployment. For AI Agent Optimization, implementation typically takes two to three weeks. Databricks environments can be set up in under 15 minutes. Note: Complex enterprise environments may require additional integration time. Source.
What support is available during onboarding and ongoing use?
Sedai provides personalized onboarding sessions, extensive documentation, and a community Slack channel for real-time assistance. Customers also benefit from comprehensive support resources and a straightforward onboarding process. Note: Some advanced support may require a service agreement. Source.
Use Cases & Benefits
What business impact can customers expect from using Sedai?
Customers can achieve up to 50% reduction in cloud costs, reduce latency by up to 75%, and decrease failed customer interactions by up to 70%. Engineering teams may see up to 6X productivity gains due to automation of repetitive tasks. These outcomes are based on real-world deployments and case studies. Note: Actual results may vary depending on environment and usage. Source.
Who are Sedai's customers and what industries do they represent?
Sedai's customers include KnowBe4 (security awareness training), Palo Alto Networks (cybersecurity), Belcorp (beauty and personal care), Campspot (travel and hospitality), Inflection (background check services), and Freshworks (customer engagement software). Industries represented include cybersecurity, SaaS, beauty, travel, and background checks. See case studies. Note: Not all industries are covered; contact Sedai for more sector-specific examples.
Can you share specific customer success stories with Sedai?
Yes.
KnowBe4 achieved up to 50% cost savings and reduced average response time from 18.5 seconds to 80 milliseconds (99.5% reduction) on AWS Lambda. Case study.
Palo Alto Networks saved $3.5 million through Sedai's optimization. Video case study.
Belcorp reduced AWS Lambda latency by 77%.
Campspot achieved a 34% reduction in AWS Lambda latency.
Note: Results are specific to each customer environment. More stories.
Who can benefit from using Sedai?
Sedai is best suited for platform engineering, ML platform, FinOps, technology leadership, SRE, and cloud operations teams in organizations running production-scale AI agents and cloud workloads. It is ideal for teams that need continuous, autonomous cost optimization with safety validation. Note: Teams with only single-app or non-production workloads may not realize the full value of Sedai. Source.
Pain Points & Problem Solving
What problems does Sedai solve for engineering and cloud teams?
Sedai addresses runaway cloud costs (up to 50% savings), performance bottlenecks (up to 75% latency reduction), operational toil (up to 6X productivity gains), and complexity in multi-cloud/hybrid environments. It also bridges misaligned priorities between engineering and finance by aligning cost efficiency with performance objectives. Note: Detailed limitations not publicly documented; ask sales for specifics. Source.
How does Sedai solve pain points differently than other tools?
Sedai uses autonomous optimization (not just recommendations), application-aware intelligence (optimizing for outcomes, not just metrics), and safety-by-design (continuous validation and auto-rollback). Unlike tools that require manual intervention, Sedai acts continuously and safely. Note: Sedai is not intended for single-app or non-production use cases. Source.
Competition & Comparison
How does Sedai compare to TrueFoundry for AI agent cost optimization?
TrueFoundry enforces cost at the gateway with policy-driven controls, supporting latency-based routing, quotas, and rate limits. However, it does not validate changes against SLOs or act autonomously. Sedai, in contrast, acts autonomously and validates every change against SLOs, latency, and error rates, with auto-rollback for safety. Choose Sedai if you need autonomous, SLO-safe optimization; choose TrueFoundry if you need policy-driven governance without autonomous action. Note: TrueFoundry's controls are not SLO-gated. Source.
How does Sedai compare to Portkey for AI agent cost optimization?
Portkey reduces spend through caching and routing across a large model catalog, with budget limits and observability. However, it does not gate actions on production SLOs and only enforces budgets. Sedai acts autonomously, validates every change against SLOs, and provides auto-rollback. Choose Sedai for autonomous, SLO-safe optimization; choose Portkey for broad model access and caching. Note: Portkey's caching is most effective for repeated traffic. Source.
How does Sedai compare to Cast AI for infrastructure optimization?
Cast AI optimizes Kubernetes infrastructure, including GPU workloads, with real-time rightsizing and spot management. However, it does not optimize Lambda, ECS, VMs, or token/model spend. Sedai optimizes both infrastructure and token/model spend, with SLO validation and autonomous action. Choose Sedai for full-stack, autonomous optimization; choose Cast AI for Kubernetes infrastructure focus. Note: Cast AI's optimization stops at the infrastructure layer. Source.
How does Sedai compare to observability tools like Helicone or Langfuse?
Helicone and Langfuse provide observability and cost attribution, surfacing where spend occurs but not acting on it. Sedai acts autonomously to reduce spend, validated by SLOs and with auto-rollback. Choose Sedai if you need autonomous cost reduction; choose Helicone or Langfuse for observability and debugging. Note: Observability tools require manual intervention to realize savings. Source, Source.
AI agent cost optimization tools do one of four things to your spend: show you where spend goes, route and cache requests to reduce spend, trim cloud and compute costs underneath, or act on costs directly. Which you need depends on where your cost lives.
Key Takeaways
One agent task triggers many billed model calls because each step re-sends the growing context, so costs compound.
The FinOps Foundation estimates a 30x to 200x cost difference between an unoptimized setup and one engineered for cost.
Prompt caching and model routing save the most; Anthropic prices cache reads at a 90% discount.
Most tools only report or route cost. Only one tool acts on costs autonomously and safely.
How AI Agent Costs Differ From Normal LLM Costs
AI agent costs are harder to control than single-call LLM costs because of what happens to the context on every call. With any LLM, context means everything you send the model in a request. That could be instructions, the conversation so far, or any data/tool results from earlier steps.
You pay for all of it with input tokens each time you call the model. A single-turn chatbot sends one prompt and gets one answer, so it pays for that context once.
An agent works differently. To finish a single task, it makes a series of calls, and on each call it re-sends everything that has happened so far so the model can continue where it left off. That re-sent history grows with every step, so the agent pays for the same accumulating context again and again. This is what makes agent bills compound and run higher than predicted.
Two teams can run the same agent workload, the same tasks at the same request volume, and still pay very different amounts. The FinOps Foundation estimates a 30x to 200x difference between an unoptimized setup on a premium vendor with no discounts and one engineered for cost. That range is why this category of tools exists.
Three cost drivers sit underneath that delta:
Input tokens: The system prompt, tool definitions, and prior conversation get re-sent on every step of the loop, so long-running agents pay for the same context repeatedly.
Output tokens: Generated text, tool calls, and reasoning are billed at a higher rate than input on most providers.
Runaway loops: An agent that retries, recurses, or fails to terminate keeps spending until something stops it.
The tools below handle these drivers in different ways. Some measure them, some cap and reroute them, and some act on them without a human in the loop.
We evaluated each tool on what it actually does to cost, not feature-count:
What it optimizes: Token and model spend, cloud and Kubernetes infrastructure, or both.
How it acts: Observe (show you the cost), route (change how requests are sent), or act (make the change for you).
Production-safety awareness: Whether it checks latency, errors, and SLO compliance before it changes anything.
Integration effort: Whether it needs a rewrite, an SDK import, a proxy, or read-only access.
Best-fit team: Platform engineering, FinOps, or finance.
Most lists skip the question that matters most: what does the tool actually do about the cost? A dashboard and an autonomous optimizer both say they optimize AI agent costs, but a dashboard only shows you where the money goes, while an autonomous optimizer changes it. We say which one each tool is.
Sedai acts on cost autonomously instead of simply providing recommendations or visibility through a dashboard. It installs as an SDK between your production agents and their LLM providers, then optimizes cost, performance, and accuracy across every call.
Where most tools stop at surfacing a finding, Sedai autonomously makes the change, validating each one against latency, error rates, and SLO compliance before it acts.
Key Features
Smart Routing sends each agent to the right model through a custom router trained on that agent's own production traffic, not generic benchmarks. Cross-provider fallback and reliability are built in, and org-level governance and credential management sit on top.
It supports OpenAI, Bedrock, Vertex AI, and Azure Foundry, and optimizes what the agent runs on as well as what it calls.
On the infrastructure side, that includes the GPUs behind AI workloads, where Sedai reclaims idle GPU capacity and packs more work onto each GPU with NVIDIA MIG and fractional instances, using the same validation and rollback it applies everywhere else.
What It Does Well
Sedai removes the human from the loop safely. Every action works within SLO guardrails and is reversed by auto-rollback if a change degrades performance, so cost cuts do not become incidents. Sedai estimates that at least 30% of LLM costs can be eliminated once agent observability is turned on.
Limitations
Sedai is built for production agents running at real scale, not for single-app usage. It does not cover Snowflake today.
Best For
Platform and ML-platform teams that want a system to fix cost continuously on its own and have SLOs they cannot afford to breach.
AI Gateways
TrueFoundry
TrueFoundry enforces cost at the gateway before spend happens through a single governed control plane. It sits in front of your models, tools, and agents and applies policy uniformly across all of them. Its strength is governance rather than autonomy.
Key Features
TrueFoundry supports:
Latency-based and intelligent routing to avoid unnecessary calls to expensive models
Cost-based and token-based quotas set with metadata filters
Rate limits per user, service, or endpoint
Team-level isolation runs on role-based access control with per-team API keys and usage tracking.
Limitations
Its controls are policy-driven, not SLO-gated. It enforces budgets and routes requests. However, it does not validate a change against latency or error rates and act on its own the way an autonomous optimizer does.
Portkey
Portkey reduces spend at the gateway through caching and routing across a wide model catalog. It gives access to more than 1,600 LLMs behind a unified API and adds cost controls on top. Its most distinctive lever is caching that returns stored responses instead of paying for repeated inference.
Key Features
It offers intelligent caching, routing strategies, and request batching, with real-time cost and performance monitoring and budget limits. Its observability surfaces traces, errors, and cache behavior so you can see which calls are expensive.
Limitations
Like other gateways, it enforces budgets but does not gate actions on production SLOs, and caching only helps to the degree your traffic repeats.
LiteLLM
LiteLLM is an open-source proxy that puts routing, budgets, and spend tracking in front of 100+ models. It calls them all through the OpenAI input and output format, which makes it a low-friction way to standardize access and add controls without committing to a vendor. The project is open source, with its code on GitHub.
Key Features
The proxy server provides a centralized API gateway with:
Authentication and authorization
Retry and fallback logic across deployments
Multi-tenant cost tracking per project and user
Budgets per project
Caching as a per-project option
Limitations
It is infrastructure you run and maintain, and its budgets and rate limits are static controls rather than SLO-aware, autonomous ones.
Observability and Cost Attribution
Helicone
Helicone shows you where LLM spend goes at the level of individual requests, users, and sessions. It positions itself as an AI gateway and observability platform that helps teams route, debug, and analyze their applications. It is open source with its code on GitHub.
Its role in a cost stack is to make spend legible before you decide what to change.
Key Features
Helicone's dashboard tracks activity by request, session, and user, so you can see what is driving spend. A built-in query language lets you slice that data, and a playground with prompt and dataset tools help you test changes.
Rate limits and alerts flag runaway usage.
Limitations
Observability shows the problem, it does not fix it. Every saving still depends on an engineer reading the data and making the change, so Helicone reduces spend only indirectly.
Langfuse
Langfuse traces what your agents actually do and attaches cost and quality to each step. It is an open-source platform for debugging and improving LLM applications. It captures every call an agent makes, including retrieval, embeddings, and other API calls, not just the model calls.
For agents, that matters because much of the cost hides in those non-model calls that most tools never track.
Key Features
Langfuse offers comprehensive tracing with:
Multi-turn and agent visualization
Cost and usage tracking per user
Evaluation tools including LLM-as-a-judge and human labeling
Prompt management with versioning.
It is fully open source with a public API.
Limitations
It is an observability and evaluation platform, so it surfaces cost but does not route around it or act on it.
CloudZero
CloudZero connects AI spend to the customers and features that drive it, so you can see what each one actually costs. It pulls cost and usage from your AI providers and cloud into one place, then traces a dollar of spend back to the customer or feature that caused it.
Its job is answering what something costs and for whom, not changing the bill directly.
Key Features
CloudZero can break a single dollar of AI cost down by customer, feature, or model. It reports unit economics like cost per customer, per feature, and per transaction, and streams spend in real time.
When cost jumps, its anomaly detection points to the team, model, or feature behind the spike.
Limitations
It is an attribution and reporting platform, so it identifies waste and anomalies but leaves the fix to your team.
Infrastructure and FinOps Tools
Cast AI
Cast AI cuts the compute cost underneath your agents when they run on Kubernetes. It is a Kubernetes cost optimization platform for FinOps and cloud engineering teams that rightsizes pods, automates spot instances, and bin-packs nodes in real time.
Key Features
It combines rules with predictive machine learning for real-time rightsizing, autoscaling, and spot management, including spot-interruption protection ahead of reclamation.
Limitations
Cast AI optimizes Kubernetes infrastructure only. It rightsizes and scales the compute your agents run on, including GPU workloads inside Kubernetes, but it does not touch Lambda, ECS, VMs, or the LLM and API token spend that drives most agent cost.
Its optimization stops at the infrastructure layer, not the models and calls your agents make.
PointFive
PointFive finds cloud waste across providers and, through its TokenShift product, compresses tokens for coding agents. It is a cloud cost visibility and FinOps platform that detects waste and recommends savings across AWS, Azure, and GCP across 500+ detections.
Its AI-specific play targets developer tooling rather than production agents.
Key Features
It surfaces waste and savings recommendations across clouds and reports through clean FinOps dashboards. TokenShift compresses context for coding agents, supporting tools like Claude Code, Cursor, Copilot, and Windsurf.
Limitations
PointFive can write a fix, either as a pull request or as infrastructure-as-code. But an engineer still has to review it and apply it. The work of changing production stays with your team, and findings can pile up in a backlog.
It also runs on static rules with no SLO awareness. That means it flags what looks wasteful, not what is safe to change.
TokenShift is also a separate, narrower product. It runs on developer machines against coding assistants, and never touches the cost of agents running in production.
Datadog Cloud Cost Management
Datadog Cloud Cost Management surfaces cost data and recommendations from the telemetry you already collect. It is a visibility platform that fires anomaly alerts and generates daily recommendations backed by Datadog observability data.
If you already run Datadog, it puts cost next to the metrics you watch every day.
Key Features
It combines observability data with cloud billing to flag orphaned, legacy, and over-provisioned resources. It refreshes recommendations daily, and covers EC2, RDS, Lambda, ECS, and more.
Limitations
Datadog Cloud Cost Management centers on visibility and recommendations. It can automate a limited set of actions, such as deleting unused EBS volumes or running scheduled remediations with optional Slack or Teams approval, but most cost decisions are surfaced for an engineer to act on.
It does not gate cost changes on latency or error SLOs, so it flags what looks wasteful, not what is safe to change.
Other AI Agent Cost Tools Worth Knowing
A few adjacent tools cover parts of this space:
Amnic gives you a single view of LLM, cloud, and Kubernetes spend together.
Mavvrik is a cost management platform focused on GPU hours and model costs across providers.
nOps and Usage.ai lean toward cloud commitment and infrastructure cost, with AI cost as a growing module.
FAQs
If your agents' costs are rising faster than your team can safely cut them, Sedai is the act layer that reduces spend autonomously without breaching your SLOs. See what Sedai finds in your own environment.
