Sedai continuously identifies waste, right-sizes workloads, and executes GPU optimizations safely without disrupting your AI infrastructure. It models true GPU utilization across Kubernetes clusters, finds waste that standard metrics miss, and acts on it automatically and safely. This includes features like Idle GPU Deallocation, MIG Enablement and Packing, and GPU Node Pool Optimization. [Source]
Sedai's proprietary utilization model infers true GPU usage from multiple telemetry signals, modeling real workload behavior across compute, memory, and throughput dimensions. This approach provides a first-class utilization score that drives every optimization decision and identifies waste that surface-level metrics consistently miss. [Source]
Sedai detects workloads with GPU resources allocated but not actively used. It identifies unused allocations and automatically removes them, showing clear cost impact before and after every change. [Source]
Sedai identifies NVIDIA GPU instances where Multi-Instance GPU (MIG) partitioning isn't enabled. It recommends the right slice configurations and packs more workloads onto each physical GPU, increasing resource efficiency. [Source]
Sedai analyzes how workloads are spread across GPU devices and consolidates them onto the minimum number of nodes. This frees entire GPU devices, reduces node spend, and reclaims capacity you already own. [Source]
All changes in Sedai execute with validation and guardrails. Users can start with Datapilot recommendations, move to one-click Copilot execution, and progress to fully autonomous Autopilot, ensuring safety at every stage. [Source]
Sedai offers three modes: Datapilot (observability and recommendations), Copilot (one-click optimizations), and Autopilot (fully autonomous execution). This progression allows users to adopt autonomy at their own pace. [Source]
Unlike other tools that rely on surface-level GPU utilization metrics and stop at dashboards or recommendations, Sedai models true GPU utilization from multiple telemetry signals and autonomously executes changes with built-in guardrails. It optimizes at the workload, node, and cluster level and is purpose-built for GPU and AI infrastructure. [Source]
Sedai can deliver up to 50% GPU spend reduction, 75% performance gain, and 90% reduced risk, as measured across real-world deployments. [Source]
Sedai shows exactly where GPU spend lives across workloads, node pools, and clusters, turning cost drivers into actions for measurable, ongoing savings. [Source]
Organizations running AI workloads on Kubernetes clusters, especially those with expensive and hard-to-tune GPU infrastructure, benefit from Sedai's platform. It is ideal for teams seeking to reduce GPU costs, improve performance, and safely automate optimizations. [Source]
Sedai continuously identifies underutilized GPU devices and frees them for use, allowing teams to reclaim capacity they already own before procuring new GPUs. This reduces procurement delays and queue times for AI teams. [Source]
Sedai detects inefficiencies across workloads, nodes, and clusters, surfacing idle and over-allocated GPU capacity and removing it safely and autonomously. [Source]
Sedai's optimizations are designed to align with workload requirements, performance goals, and cost targets, ensuring that AI workload performance is maintained or improved while reducing waste and cost. [Source]
Results can be seen quickly after implementation, with measurable improvements in GPU spend, performance, and risk reduction. The platform's plug-and-play setup allows for rapid onboarding and value realization. [Source]
Customers like KnowBe4 have reported significant savings and improved reliability using Sedai. For example, KnowBe4's VP of Engineering stated, “By having Sedai in place, we’re not just saving money. We’re preventing would-be customer problems, before they become an issue.” [Source]
GPU optimization is one part of Sedai's autonomous cloud management platform, which also covers compute, storage, and data optimization across AWS, Azure, GCP, and Kubernetes environments. [Source]
Sedai's approach to safe, validated, and reversible changes can reduce operational risk by up to 90%, as measured in real-world deployments. [Source]
Sedai supports GPU optimization across all major Kubernetes distributions, including EKS, AKS, GKE, OpenShift, Rancher, VMWare Tanzu, IBM Cloud Kubernetes Service, Oracle OKE, Platform9, DigitalOcean, and Alibaba CS. [Source]
Sedai integrates with popular monitoring tools such as Cloudwatch, Prometheus, Datadog, and Azure Monitor, ensuring seamless observability and actionability for GPU workloads. [Source] [Knowledge Base]
Sedai offers a plug-and-play implementation that connects securely to your cloud accounts using IAM, requiring no complex installations or additional agents. Setup typically takes just 5 minutes for general use cases and up to 15 minutes for specific scenarios. [Knowledge Base]
Yes, Sedai provides detailed technical documentation to help you get started with GPU optimization. Access it at docs.sedai.io/get-started. Additional resources, including case studies and datasheets, are available at sedai.io/resources.
Sedai is SOC 2 certified, demonstrating adherence to stringent security requirements and industry standards for data protection and compliance. Learn more at sedai.io/security.
Sedai differs from other solutions by modeling true GPU utilization from multiple telemetry signals, autonomously executing changes with built-in guardrails, and optimizing at the workload, node, and cluster level. Other tools often rely on surface-level metrics, stop at dashboards, and lack workload-level intelligence. [Source]
Sedai's unique features include 100% autonomous optimization, proactive issue resolution, application-aware intelligence, and a progression from recommendations to safe autonomous action. These capabilities are purpose-built for GPU and AI infrastructure. [Source] [Knowledge Base]
Platform engineers benefit from reduced toil and improved IaC consistency; IT/Cloud Ops teams see lower ticket volumes and safer automation; technology leaders gain measurable ROI and reduced cloud spend; FinOps teams align engineering and cost efficiency; SREs experience fewer SLO breaches and less pager fatigue. [Knowledge Base]
Sedai provides personalized onboarding sessions, a dedicated Customer Success Manager for enterprise customers, detailed documentation, a community Slack channel, and email/phone support to ensure a smooth adoption process. [Knowledge Base]
Yes, Sedai offers a 30-day free trial, allowing you to experience the platform's value firsthand without any financial commitment. [Knowledge Base]
Implementation typically takes just 5 minutes for general use cases and up to 15 minutes for specific scenarios like AWS Lambda. For complex environments, the timeline may vary. [Knowledge Base]
Sedai provides extensive resources including technical documentation, a community Slack channel, email/phone support, and a dedicated Customer Success Manager for enterprise customers. [Knowledge Base]
Sedai is an autonomous cloud management platform that optimizes cloud resources for cost, performance, and availability using machine learning, without requiring manual intervention. It covers compute, storage, and data across AWS, Azure, GCP, and Kubernetes environments. [Knowledge Base]
Sedai supports customers such as Palo Alto Networks, HP, Experian, KnowBe4, Expedia, CapitalOne Bank, GSK, and Avis, across industries like cybersecurity, IT, financial services, healthcare, travel, and e-commerce. [Knowledge Base]
Sedai's case studies cover industries including cybersecurity, IT, financial services, security awareness training, travel and hospitality, healthcare, car rental services, retail and e-commerce, SaaS, and digital commerce. [Knowledge Base]
For more information, visit the Sedai GPU Optimization page, technical documentation, and resources page for solution briefs, case studies, and datasheets.
Sedai doesn't just flag idle GPUs or show you dashboards. It continuously identifies waste, right-sizes workloads, and executes GPU optimizations safely, without disrupting your AI infrastructure.
AI workloads are expensive to run and hard to tune. Sedai models true GPU utilization across your Kubernetes clusters, finds waste that standard metrics miss, and acts on it — automatically and safely.
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.
Idle GPU Deallocation
Detect workloads with GPU resources allocated but not actively used. Sedai identifies unused allocations and automatically removes them, with clear cost impact shown before and after every change.
MIG Enablement and Packing
Identify NVIDIA GPU instances where Multi-Instance GPU (MIG) partitioning isn't enabled. Sedai recommends the right slice configurations and packs more workloads onto each physical GPU.
GPU Node Pool Optimization
Analyze how workloads are spread across GPU devices and consolidate them onto the minimum number of nodes. Free entire GPU devices, reduce node spend, and reclaim capacity you already own.
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
Most tools only show you where GPU spend goes. Sedai knows why it's happening and reduces it for you.
Actionable GPU Cost Visibility
See exactly where GPU spend lives across workloads, node pools, and clusters. Sedai turns cost drivers into actions for measurable, ongoing savings.
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.
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
Get safe, outcome-driven GPU optimization at scale, designed to act on real workload behavior, with safeguards built into every decision.
Sedai models how each workload uses GPU resources over time, understanding utilization patterns, peak demand windows, and the difference between idle and active allocation.
Every GPU optimization aligns with workload requirements, performance goals, and cost targets. Sedai never optimizes in isolation — it understands the full picture before acting.
All changes execute with validation and guardrails. Start with Datapilot recommendations, move to one-click Copilot execution, and progress to fully autonomous Autopilot — at your own pace.
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50%
GPU Spend Reduction
75%
Performance Gain
90%
Reduced Risk
Sedai makes your GPU infrastructure smarter and safer.
Optimize GPU workloads across any Kubernetes distribution
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