Sedai develops autonomous cloud optimization software and positions its platform as the world’s first self-driving cloud. The platform continuously optimizes cloud resources to reduce cost, improve performance, and protect availability by learning how applications actually behave in production rather than relying on static rules or one-time recommendations.
Sedai is built for organizations operating modern, high-scale infrastructure across public cloud, Kubernetes, virtual machines, databases, storage, serverless, data platforms, and emerging AI workloads. Its operating model lets customers adopt autonomy progressively through observability, guided execution, and full autopilot, helping engineering teams replace repetitive cloud tuning with ongoing, policy-driven optimization.
Offerings, Capabilities, and Integrations
Sedai centers its offering on autonomous, application-aware optimization. The platform analyzes traffic patterns, dependencies, utilization, releases, and golden signals, then makes gradual, SLO-aware changes with continuous safety checks, rollback controls, and customer-defined guardrails. It also supports multiple operating modes so teams can move from insight and recommendation to one-click execution and full autonomous action.
Sedai is designed to fit into existing cloud and engineering workflows. It integrates with major cloud platforms, Kubernetes environments, monitoring and APM tools, notification systems, ITSM workflows, Infrastructure as Code repositories, CI/CD tools, and autoscaling technologies. This allows organizations to add autonomous optimization without replacing their current observability, governance, or delivery stack.
Products and Services
- Kubernetes Optimization: Autonomously rightsizes workloads, tunes scaling behavior, and optimizes nodes and clusters across managed and self-managed Kubernetes environments.
- GPU Optimization: Optimizes GPU infrastructure for AI workloads by modeling true utilization, reducing waste, and improving capacity efficiency across Kubernetes-based GPU environments.
- Database Optimization: Continuously tunes database capacity, storage, and performance settings to balance cost efficiency with latency, throughput, and reliability goals.
- Serverless Optimization: Optimizes serverless functions by tuning memory, concurrency, and related settings to improve price-performance while staying within performance guardrails.
- Storage Optimization: Optimizes storage classes, capacity, and performance settings to lower waste and coordinate storage changes with the rest of the cloud environment.
- VM Optimization: Rightsizes virtual machines and tunes related compute configurations to align capacity with real workload demand across multi-cloud environments.
- Release Intelligence: Generates autonomous release scorecards that help teams evaluate release quality in production using latency, error, saturation, and usage signals.
- Smart SLOs: Helps teams define, monitor, and optimize service level objectives using historical analysis, autonomous remediation, and performance-aware tuning.
- Sedai for FinOps: Turns cost visibility into executable optimization by giving FinOps teams safe recommendations that can be approved manually or run autonomously.
- Sedai for AI Agent Optimization: Provides a middleware SDK for AI agents that adds LLM observability, governance, reliability controls, and intelligent model routing across providers.
- ECS Optimization: Autonomously optimizes Amazon ECS services and tasks through rightsizing, scaling policy tuning, and release-aware performance management.
- Data Platform Optimization: Optimizes data platform infrastructure by rightsizing compute, reducing operational waste, and improving stability for data-intensive workloads.
Target Customers
Sedai targets engineering-led organizations that run production cloud infrastructure at scale and want to reduce manual optimization work. Its primary buyers and users include platform engineering teams, SREs, cloud operations teams, FinOps practitioners, and technology leaders responsible for balancing cost, performance, and reliability.
The platform is suited to enterprises as well as fast-growing companies that operate cloud-native and hybrid environments across AWS, Azure, Google Cloud, Kubernetes, serverless, virtual machines, databases, data platforms, and AI workloads. Sedai is also positioned for organizations that need governance, auditability, and controlled adoption of autonomous operations.
Cloud Integrations and Marketplace
- AWS Marketplace: Sedai has an AWS Marketplace presence for its autonomous cloud management platform and supports optimization and integrations across key AWS services such as EKS, ECS, EC2, Fargate, Lambda, storage, billing, and monitoring.
- Azure Marketplace: Sedai has an Azure Marketplace presence and supports Azure optimization and integrations spanning AKS, Azure VMs, storage, billing, monitoring, and governance workflows.
- Google Cloud: Sedai integrates with Google Cloud environments for services such as GKE, Google Compute Engine, and Dataflow, extending its optimization and observability model into GCP workloads.
Key People
- Suresh Mathew: Founder & CEO
- Benji Thomas: Co-founder & CTO
- Vaneet Bhaskar: CRO
- Hari Chandrasekhar: SVP of Engineering, Core
- Ethan Andyshak: VP of Product
- Nikhil Kurup: SVP of Engineering, ML Platforms
- Oksana Patel: VP of Marketing
- Shankar Jothi: VP of Engineering, AI/ML
- Aby Jacob: VP of Engineering
Key Facts
- Headquarters: Pleasanton, California, United States
- Employees: Approximately 120
- Annual Revenue: $5M-$10M
- Parent Company: None
- Subsidiaries: None
- Publicly Listed: No (privately held)
Analyst Recognitions
- Gartner: 2023 Gartner Hype Cycles — Included as a vendor in Autonomous Workload Optimization. 2023 Gartner Hype Cycles — Included as a vendor in AI-Augmented Software Engineering. 2022 Gartner Cool Vendors in Observability and Monitoring for Logging and Containers — Cool Vendor.
