Sedai
About
Sedai is an autonomous cloud optimization platform that uses patented agentic AI to reduce cloud costs, improve application performance, and prevent incidents across Kubernetes, VMs, serverless, storage, databases, GPUs, and AI workloads. The platform learns how applications actually behave — analyzing traffic patterns, golden signals, and dependencies — to make safe, incremental optimization changes without causing production incidents. Sedai supports AWS, Microsoft Azure, Google Cloud, and on-...
Details
Website
sedai.ioOfferings
12 active
VenSearch Intelligence
Ideal Customer
- Sedai targets mid-market to large enterprise organizations that run significant cloud infrastructure across Kubernetes, serverless, VMs, or multi-cloud environments and have dedicated platform engineering, SRE, or DevOps teams.
- The company explicitly serves customers ranging from Fortune 500 companies to fast-growing startups to government agencies, with notable enterprise wins at Palo Alto Networks, Capital One, HP, and Experian suggesting the primary sweet spot is larger organizations with complex, high-volume cloud environments.
Key Differentiators
- Sedai's primary differentiator is its 'safety-first' autonomous optimization model: it profiles applications for weeks before acting, applies changes incrementally, and automatically rolls back if post-change signals degrade — a methodology backed by eight U.S. patents on safe autonomous action.
- Unlike competitors such as CAST AI (Kubernetes-only, aggressive node automation) or IBM Turbonomic (rule-based, heavier platform), Sedai is SLO-aware across a broader resource stack including Kubernetes, VMs, serverless, storage, GPUs, databases, and AI tokens, and claims zero production incidents across 25 million autonomous actions.
Industries Served
- Sedai serves engineering teams across a broad range of industries.
- According to its own documentation, these include cybersecurity, financial services, SaaS, e-commerce, scientific research, healthcare, pharmaceuticals, and government.
Service Coverage
- Sedai operates as a global SaaS platform headquartered in Pleasanton, California (6701 Koll Center Parkway, Suite 250).
- The platform supports cloud infrastructure across all major providers — AWS, Microsoft Azure, Google Cloud Platform, Oracle Cloud, IBM Cloud, and on-premises environments — meaning customers can be located anywhere in the world.
Partnerships
- Sedai is a member of the AWS Partner Network through the AWS ISV Accelerate Program — a co-sell arrangement that connects Sedai directly with the AWS sales organization — and its platform is listed on both the AWS Marketplace and the Azure Marketplace.
- Sedai is also a FinOps Foundation member and supports FOCUS-compliant datasets.
Company Size & Ownership
- Sedai was founded in 2018 by Suresh Mathew (CEO) and Benjamin Thomas (President), both former PayPal engineers who built autonomous cloud optimization systems there.
- The company has approximately 100–155 employees across multiple data sources, with PitchBook reporting 154 employees and the company's own website stating 'more than 100 builders.' Estimated annual revenue ranges from $16–22 million across third-party sources.
Compliance & Certifications
- Sedai holds a SOC 2 Type 2 certification, which is prominently displayed across its partner, product, and awards pages.
- The company is also a member of the FinOps Foundation and supports the FinOps Open Cost and Usage Specification (FOCUS) standard for normalized cloud billing data.
Compiled from public sources by VenSearch · Last researched October 14, 2026
Offerings
12 offeringsAutonomous Optimization Platform
Sedai's core agentic platform uses patented AI to learn application behavior and autonomously optimize cloud infrastructure across Kubernetes, VMs, serverless, storage, databases, GPUs, and AI agents. It operates in three modes — Datapilot (observe only), Copilot (one-click approvals), and Autopilot (fully autonomous) — and holds eight U.S. patents on safe autonomous action. The platform has executed over 25 million optimization actions without causing a single production incident.
Cloud Cost Optimization
Sedai autonomously eliminates wasted cloud spend across all resource types including Kubernetes, VMs, serverless, storage, databases, GPUs, and AI tokens by learning application behavior and taking gradual, safety-checked optimization actions. Customers report 30-50% reductions in cloud costs, with examples including Palo Alto Networks saving $3.5M and KnowBe4 saving $1.2M. The platform operates continuously in real time without requiring manual engineering effort.
FinOps Automation
Sedai automates FinOps workflows by continuously identifying and acting on cost optimization opportunities across the cloud stack without requiring manual analysis or execution by engineering or finance teams. The platform integrates with the FinOps Foundation framework and provides org-wide visibility into cloud spend reduction. It eliminates the toil of manual rightsizing and cost governance tasks.
IaC & CI/CD Integration
Sedai writes every infrastructure optimization back to Git as a reviewable pull request in Terraform, Helm, or Kustomize format, preventing IaC drift by keeping declared state synchronized with the live environment. On Kubernetes, the Pod Interceptor applies changes directly to pods at creation without touching the deployment spec, avoiding GitOps reconciliation conflicts. CI/CD overwrite protection detects when a new deployment would revert a previous optimization and automatically re-applies the correct configuration.
Kubernetes Autoscaler Optimization
Sedai adds an intelligence layer on top of native Kubernetes autoscalers (HPA, VPA, Karpenter, Cluster Autoscaler, KEDA) by continuously right-sizing pod resource requests and tuning HPA scaling targets based on real workload behavior and SLOs. It resolves the native conflict between HPA and VPA by applying vertical scaling in small increments alongside horizontal scaling, and improves Karpenter bin-packing by ensuring pods are correctly sized before node provisioning. A Migration Assistant automates safe cutover from EKS Managed Node Groups to Karpenter.
Kubernetes Optimization
Sedai rightsizes workload requests and limits, tunes HPA/VPA scaling parameters, and optimizes nodes and clusters to cut costs while meeting SLOs across managed (EKS, AKS, GKE) and self-managed or on-premises Kubernetes environments. It improves bin-packing, removes idle nodes, and adjusts scaling policies based on time-of-day and seasonality, with automatic rollback if SLOs drift.
MCP Integration
Sedai's Model Context Protocol (MCP) integration exposes the same tools and capabilities available in Sed — Sedai's AI assistant — to any MCP-compliant client such as Claude, Cursor, VS Code Copilot, or custom internal agents. Users can query Sedai for optimization status, savings data, and resource opportunities, or trigger actions as part of larger agent workflows without building custom integrations. New capabilities added to Sed are automatically available through MCP with no separate roadmap.
Monitoring & APM Integration
Sedai integrates with APM and observability tools such as Datadog, Dynatrace, New Relic, Prometheus, AppDynamics, and others to correlate infrastructure metrics with application latency, error rates, and traffic signals before making any optimization decision. This unified model allows Sedai to identify workloads that appear underutilized but are latency-sensitive, preventing changes that would harm performance. Multiple monitoring providers can be connected simultaneously into a single workload model.
Proactive Incident Prevention
Sedai monitors real-time resource trends using regression models to detect trajectories toward outages before thresholds are breached, then autonomously remediates issues such as OOM events, CPU throttling, and memory leaks. It replaces static alerting thresholds with trajectory-aware signals that reduce false positives and provide early warning. Customers report an average 30% reduction in availability incidents.
Release Intelligence
Sedai automatically detects every deployment by identifying container image or workload configuration changes, then measures real-world impact against a pre-release baseline for up to 48 hours and assigns a scorecard rating across cost, latency, and errors. Teams gain quality trend tracking, release frequency insights, and native CI/CD pipeline integration without manual instrumentation. Customers report 75% faster mean time to identify regression-causing deployments.
Safety by Design
Sedai's safety architecture wraps every autonomous action in layered guardrails built from deep application learning, including topology-aware dependency mapping, incremental change execution, conflict detection, and automatic rollback if post-change signals degrade. The system profiles applications for weeks before acting and validates every change through a multi-point safety check before, during, and after execution. This capability is backed by eight U.S. patents on autonomous action safety.
Workflow & Alerts Integration
Sedai integrates with ITSM and alerting platforms including ServiceNow, Jira, Slack, Microsoft Teams, and PagerDuty to create formal change records for every autonomous action, ensuring full auditability and compliance coverage. For sensitive environments, Sedai can be configured to pause and wait for an authorized approver in the ITSM tool before executing a change, while less critical environments run autonomously. Per-environment configuration allows gated approvals for production alongside autonomous execution for development.
Considering Sedai?
Our team will work with you to determine if Sedai is the right fit for your organization. From research and assessment through procurement negotiation and ongoing vendor management — we handle the entire process at no cost to you.