Sedai

Sedai is an autonomous cloud optimization platform built by Suresh Mathew and Benji Thomas, who previously developed a similar system at PayPal to manage the world's largest OpenStack cloud. The platform uses patented reinforcement learning to continuously rightsize resources, tune scaling policies, and eliminate cloud waste — without requiring manual intervention or risking production incidents. Sedai markets itself as the "world's first self-driving cloud" and holds eight U.S. patents on safe autonomous action in production environments.

What It Is

Sedai is a cloud cost and performance optimization platform that learns how applications actually behave — analyzing traffic patterns, dependencies, and golden signals — and then takes gradual, validated actions to reduce waste and improve reliability. It operates across the full cloud stack: Kubernetes (EKS, AKS, GKE, self-managed), virtual machines, serverless functions, databases, storage, data platforms, and GPU workloads. The platform is designed for engineering teams, SREs, platform engineers, and FinOps practitioners who want to eliminate repetitive optimization toil.

How the Autonomy Spectrum Works

Sedai offers three operating modes that teams can adopt incrementally:

• Datapilot — Observes the environment, builds understanding, and surfaces cost and performance insights without making any changes.
• Copilot — Recommends actions that engineers can approve and apply with a single click, removing manual effort while keeping humans in the loop.
• Autopilot — Takes action autonomously within customer-defined policies and guardrails, with every change explainable and fully reversible.

Teams can run different modes across services, environments, or teams simultaneously, and switch modes at any time.

Safety Architecture and Patents

Sedai's core differentiator, according to the company, is its patented approach to safe autonomous action. The platform holds eight U.S. patents covering its ability to make changes in production without causing incidents. Its Autonomous Optimization Engine makes gradual changes and performs continuous safety checks against SLOs and error budgets before, during, and after each action. The company states it has caused zero production incidents across all customer deployments.

Platform Coverage and Integrations

Sedai optimizes across a broad set of cloud infrastructure types:

• Kubernetes: Workload rightsizing (CPU/memory requests and limits), node and cluster optimization, HPA/VPA tuning, and smart scaling policies across EKS, AKS, GKE, ECS, Fargate, OpenShift, Rancher, and others.
• Cloud providers: AWS, Azure, GCP, and on-premises environments.
• Other workloads: Virtual machines, serverless functions, databases, storage, data and streaming platforms, and GPU/LLM inference workloads.

The platform integrates with CI/CD pipelines and is designed to avoid IaC drift. It is SOC 2 Type 2 certified and available on the AWS Partner Network and Azure Marketplace. Sedai is also a member of the FinOps Foundation.

Vendor-Reported Performance Claims

According to Sedai's platform page, customers have achieved an average 15% reduction in cloud costs, 38% fewer failed customer interactions, and 25% less engineering toil. The company page states Sedai manages over $3 billion in total cloud spend for enterprise customers and has executed more than 25 million optimization actions. The homepage attributes a $3.5M cloud cost reduction to one named customer (Palo Alto Networks). These figures are vendor-published claims.

Audience and Deployment Model

Sedai targets enterprise engineering teams, platform engineers, SREs, and FinOps practitioners at organizations running significant cloud workloads. The platform is delivered as a SaaS product — no agents are required for it to work. It is positioned for teams that want to move from reactive, manual cloud operations toward continuous, autonomous optimization without sacrificing control or safety.