Sedai GPU Optimization is an autonomous solution designed to reduce costs and optimize GPU usage for AI infrastructure, particularly in Kubernetes environments. It leverages a proprietary utilization model to identify and execute safe, meaningful optimizations automatically, addressing both cost and performance challenges for teams running AI workloads at scale. [Source]
Sedai GPU Optimization was created to address the growing challenge of spiraling GPU costs and underutilization in AI infrastructure. Many organizations struggle with poor visibility into GPU usage, leading to over-provisioning and wasted spend. Sedai's solution provides trustworthy utilization signals and autonomous execution to safely optimize GPU resources. [Source]
Unlike traditional tools that rely on basic activity metrics (such as nvidia-smi), Sedai GPU Optimization uses a proprietary model that infers true GPU usage from multiple telemetry signals. This approach provides a more accurate picture of actual workload activity, enabling more effective and safer optimizations. [Source]
The primary purpose of Sedai GPU Optimization is to autonomously reduce GPU costs and improve resource utilization for AI workloads, eliminating manual intervention and minimizing the risk of disrupting production workloads. [Source]
Sedai GPU Optimization offers three main features: GPU Deallocation (identifies and removes unused GPU allocations), Partitioning (enables multi-instance GPU and fractional GPU usage), and GPU Node Pool Optimization (recommends workload repacking to consolidate usage and free up hardware). [Source]
Sedai uses a proprietary utilization model that synthesizes multiple telemetry signals to infer actual GPU workload activity, rather than relying solely on standard activity metrics. This model provides a first-class utilization score that drives all optimization decisions. [Source]
GPU Deallocation identifies Kubernetes workloads with allocated but unused GPU resources. Sedai detects these cases and, after approval in Copilot mode, autonomously removes the unnecessary allocation with full safety checks, resulting in immediate cost savings. [Source]
Partitioning allows a single physical GPU to be divided into multiple instances using NVIDIA Multi-Instance GPU or AWS G6 fractional GPU instances. This enables multiple workloads to share a GPU, increasing utilization and reducing costs. [Source]
GPU Node Pool Optimization analyzes workload distribution across GPU devices and recommends repacking workloads to consolidate them onto fewer nodes. This can free up entire GPU devices for redeployment or deprovisioning, maximizing hardware efficiency. [Source]
Sedai GPU Optimization supports three modes: Datapilot (observability and recommendations), Copilot (one-click execution with user approval), and Autopilot (fully autonomous execution). This phased approach allows teams to build trust in the system before enabling full autonomy. [Source]
Yes, Sedai GPU Optimization is designed to work across various Kubernetes platforms and distributions, including EKS, GKE, AKS, OpenShift, and more, without requiring vendor-specific workarounds. [Source]
Sedai GPU Optimization is ideal for platform and infrastructure teams, FinOps and finance leaders, ML and AI engineering teams, and cloud architects who manage GPU resources in Kubernetes environments and seek to reduce costs, improve utilization, and ensure safe operations. [Source]
Sedai GPU Optimization addresses poor visibility into GPU usage, over-provisioning, wasted spend, and the risk of disrupting production workloads. It provides actionable insights and autonomous execution to optimize GPU resources safely and efficiently. [Source]
FinOps and finance leaders gain measurable, attributable savings on GPU costs, with clear before-and-after reporting at the workload and node pool level. Sedai helps control one of the fastest-growing cost categories in the cloud bill. [Source]
ML and AI engineering teams benefit from better GPU packing, which reduces delays waiting for available capacity and minimizes resource contention across the cluster, resulting in improved productivity and performance. [Source]
Cloud architects gain a consistent optimization approach that works across Kubernetes platforms and distributions, eliminating the need for vendor-specific workarounds and ensuring best practices for GPU resource management. [Source]
Yes, Sedai GPU Optimization is designed with safety and autonomy in mind, allowing teams to start with recommendations and progress to full autonomy as trust is built. This makes it suitable for production AI workloads where reliability is critical. [Source]
Sedai GPU Optimization is available now for existing Sedai customers. New users can learn more and book a demo to speak with a technical expert at sedai.io/demo. [Source]
Sedai offers a 30-day free trial for new users, allowing you to experience the platform's value firsthand. You can sign up for the trial at this link. [Source]
For general use cases, Sedai’s setup process takes just 5 minutes. For specific scenarios, such as AWS Lambda, it may take up to 15 minutes. More complex environments may require additional time. [Source]
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. [Source]
Technical documentation for Sedai is available at docs.sedai.io/get-started, including setup guides, feature explanations, and troubleshooting resources. [Source]
Sedai's autonomous optimization can reduce cloud costs by up to 50% by rightsizing workloads and eliminating waste, as demonstrated in customer case studies. [Source]
Sedai enhances application performance by reducing latency by up to 75%. For example, Belcorp achieved a 77% reduction in AWS Lambda latency, significantly improving user experience. [Source]
By automating routine tasks, Sedai delivers up to 6X productivity gains, allowing engineering teams to focus on high-value work instead of manual optimizations. [Source]
Yes. For example, Palo Alto Networks saved $3.5 million and reduced Kubernetes costs by 46% using Sedai's autonomous optimization. KnowBe4 achieved 50% cost savings in production. [Palo Alto Networks Case Study] [KnowBe4 Case Study]
Industries such as cybersecurity, IT, financial services, healthcare, travel, e-commerce, SaaS, and digital commerce have benefited from Sedai's optimization solutions. Notable customers include Palo Alto Networks, HP, Experian, KnowBe4, Expedia, CapitalOne Bank, GSK, Avis, Belcorp, Freshworks, and Campspot. [Source]
Sedai integrates with monitoring and APM tools (Cloudwatch, Prometheus, Datadog, Azure Monitor), Kubernetes autoscalers (HPA/VPA, Karpenter), IaC and CI/CD tools (GitLab, GitHub, Bitbucket, Terraform), ITSM tools (ServiceNow, Jira), notification tools (Slack, Microsoft Teams), and runbook automation platforms. [Source]
Yes, Sedai connects securely to cloud accounts using Identity and Access Management (IAM), eliminating the need for complex installations or additional agents. [Source]
Yes, Sedai is SOC 2 certified, demonstrating adherence to stringent security requirements and industry standards for data protection and compliance. [Source]
Sedai GPU Optimization stands out by providing autonomous optimization, a proprietary utilization model for accurate workload analysis, and safe, incremental autonomy (Datapilot, Copilot, Autopilot). Unlike many tools that only surface recommendations, Sedai executes optimizations safely and automatically. [Source]
Sedai GPU Optimization is unique for its 100% autonomous execution, application-aware intelligence, full-stack cloud coverage, and phased autonomy model. It addresses both signal quality and execution confidence, enabling safe, meaningful optimizations that deliver measurable results. [Source]
Ethan Andyshak
VP of Product
March 17, 2026
Featured
AI infrastructure spending is growing faster than most organizations can manage. At the center of that growth is GPU compute — powerful, essential, and extraordinarily expensive. And yet, despite the cost, a significant portion of GPU capacity sits largely unused at any given time.
We built Sedai GPU Optimization to fix that. Today, we're announcing its general availability for Kubernetes environments. I’d like to walk you through what we built, why we built it the way we did, and what it means for teams running AI workloads at scale.
Over the past year, we've had a consistent theme emerge in conversations with customers and prospects: GPU costs are spiraling, and nobody feels like they have a reliable handle on them.
The pattern is familiar. An AI team needs GPUs for a training job or inference workload. They request more than they need, because the consequences of under-provisioning are painful and the consequences of over-provisioning are, at worst, a bigger cloud bill. Visibility into actual usage is poor, so nobody really knows how much is being wasted. And even when teams suspect they're over-allocated, they're reluctant to make changes. One bad configuration change on a GPU workload can mean a failed training run, a degraded inference service, or an angry ML team.
So the default behavior is to follow the old standard: leave it running, just to be safe, and accept the painful price tag.
The result is that roughly one-third of all GPUs run at less than 15% utilization, while GPU instances can cost 40x more than standard compute based on published cloud pricing. The math on that wastage is brutal.
Before building anything, we spent time understanding why this problem persists despite a growing ecosystem of cloud cost and infrastructure tools.
The answer comes down to two things: signal quality and execution confidence.
Signal quality: The most widely used GPU utilization metric — the one reported by nvidia-smi and ingested by most monitoring and FinOps tools — measures whether a GPU is active, not whether it's doing productive work. In the most extreme case, a GPU can report 100% utilization while performing zero actual computation. That means teams are making decisions based on a metric that fundamentally misrepresents what their GPUs are doing.
Execution confidence: Even when teams identify potential savings, they struggle to act. GPU optimization is genuinely complex. Hardware configurations vary widely, ML frameworks add layers of abstraction, and the risk of disrupting a production AI workload is high. Most tools that surface GPU cost recommendations stop there, leaving teams to figure out how to safely implement changes on their own. Many teams never translate those recommendations to action and savings.
Sedai GPU Optimization is built around solving both problems simultaneously: synthesize a trustworthy utilization signal, then use it to execute safe, meaningful optimizations automatically.
Here's how it works.
The foundation of everything we do is a proprietary utilization model that infers true GPU usage from multiple telemetry signals. This is a massive step forward from just using the standard activity metric. Our model reflects what workloads are actually doing with the hardware, producing a first-class utilization score that drives every optimization decision we make.
Building this model was not easy. GPU metrics are fragmented, hardware differences vary widely, and the interaction between ML frameworks, Kubernetes scheduling, and GPU hardware creates a lot of complexity. But getting the signal right was non-negotiable — everything downstream depends on it.
With a reliable utilization model in place, we built three optimization capabilities at launch:
GPU Deallocation identifies Kubernetes workloads that have GPU resources allocated but aren't actively using them. These are quick (and significant) wins — workloads that requested a GPU, don't need it, and are silently burning through the budget with nothing to show for it. Sedai detects these unnecessary allocations, after getting the go-ahead in Copilot mode, executes the change autonomously with full safety checks.
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Partitioning targets NVIDIA GPUs that support Multi-Instance GPU and AWS G6 fractional GPU instances. These options slice a single physical GPU into smaller instances, meaning that one large GPU can serve multiple workloads rather than monopolizing a full GPU for each workload.
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GPU Node Pool Optimization analyzes how workloads are distributed across GPU devices and recommends repacking them to consolidate onto fewer nodes. This can free up entire GPU devices. It’s not just about reducing allocation, but reclaiming physical hardware that can be redeployed or deprovisioned.
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One of our core product principles at Sedai is that autonomy should be earned through trust, not assumed from day one. GPU Optimization follows the same Datapilot → Copilot → Autopilot model as the rest of our platform — letting teams start with guided recommendations, progress to one-click execution, and ultimately move toward fully hands-off autonomous execution at their own pace. Teams running production AI workloads need to trust that optimization changes won't break things before they're willing to hand over the wheel. We designed the product to build that trust incrementally.
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GPU Optimization is relevant across several personas, each of whom may have different priorities.
Platform and infrastructure teams get a consistent, safe way to manage GPU resources across Kubernetes clusters, without requiring deep GPU expertise for every team member.
FinOps and finance leaders get measurable, attributable savings on one of the fastest-growing cost categories in their cloud bill, with clear before-and-after reporting at the workload and node pool level.
ML and AI engineering teams benefit indirectly but significantly: better GPU packing means fewer delays waiting for available capacity, and right-sized workloads mean less resource contention across the cluster.
Cloud architects get a consistent optimization approach that works across Kubernetes platforms and distributions — EKS, GKE, AKS, OpenShift, and more — without vendor-specific workarounds.
Book a Sedai demo to speak with a technical expert.
GPU Optimization is the first step in a larger investment in AI infrastructure. The initial release focuses on Kubernetes, where the majority of our customers run their GPU workloads today. We're already working on expanding autonomous execution across all capabilities, broadening platform support, and extending optimization beyond Kubernetes to GPU-based VMs.
The GPU space is moving fast, and we're moving with it, continuing to push GPU Optimization forward as AI infrastructure evolves and new capabilities become possible.
Sedai GPU Optimization is available now. Existing Sedai customers can make use of it within their current environment. If you're new to Sedai, you can learn more and book a demo.
If you're carrying GPU cost as a line item and you're not sure what's actually driving it, we'd love to show you what we're seeing.
