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.
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.
Autonomy That Delivers
Powered by real app behavior.
50%
GPU capacity reclaimed
3X
Increase in effective utilization
90%
Less manual optimization work
Optimize Your Entire GPU Stack
Sedai makes your GPU infrastructure smarter and safer.
Optimize GPU workloads across any Kubernetes distribution
EKS
AKS
GKE
OpenShift
Rancher
VMWare Tanzu
IBM Cloud Kubernetes Service
Oracle OKE
Platform9
DigitalOcean
Alibaba CS
Other Tools Automate. Sedai Acts With Real Context.
Other Solutions
- Rely on surface-level GPU utilization metrics
- Stop at dashboards and recommendations
- Lack workload-level GPU intelligence
- CPU-focused; GPU optimization is an afterthought
- No path from recommendations to safe autonomous action
- Treat GPU efficiency as a cost line item
- Models true GPU utilization from multiple telemetry signals
- Autonomously executes changes with built-in guardrails
- Optimizes at the workload, node, and cluster level
- Purpose-built for GPU and AI infrastructure
- Datapilot → Copilot → Autopilot progression for safe autonomy
- Turns underused GPUs into capacity for more workloads