Sedai

Autonomous cloud management that continuously optimizes cost, performance, and availability.

4.8 (5)

리뷰어 Daniel Nikulshyn·업데이트됨 2026년 5월

Observability Enterprise FinOps Multi-Cloud Cloud Management SRE AI Automation DevOps

개요

Sedai is an AI-driven platform that autonomously manages cloud infrastructure across providers like AWS, Azure, and Google Cloud. It uses machine learning to analyze workload patterns and make real-time decisions about resource sizing, scaling, and configuration without requiring human approval for each action. Designed for SRE, DevOps, and platform engineering teams, Sedai targets reductions in cloud spend and performance incidents by acting on signals that traditional monitoring tools only surface as alerts. It supports compute, containers, serverless, and data services, integrating with existing observability stacks to ground its decisions in production telemetry.

주요 기능

Autonomous rightsizing and scaling
Continuous cost optimization
Performance and availability monitoring
Support for compute, Kubernetes, and serverless
Integrations with Datadog, Prometheus, and CloudWatch
Policy-based guardrails and approvals

사용 사례

Autonomous Cloud Cost Reduction

Continuously rightsize compute, containers, and serverless workloads across AWS, Azure, and GCP to reduce cloud spend without manual tuning by SRE or DevOps teams.

Proactive Performance Optimization

Act on production telemetry from Datadog, Prometheus, and CloudWatch to resolve performance issues before they trigger incidents, going beyond alert-based monitoring.

Kubernetes Scaling Automation

Automatically tune resource requests, limits, and scaling configurations for Kubernetes workloads with policy-based guardrails and rollback safety.

Multi-Cloud Availability Management

Maintain availability SLOs across multiple cloud providers and services by letting Sedai make closed-loop configuration decisions grounded in workload patterns.

장단점

장점

Closed-loop automation reduces manual tuning
Multi-cloud and multi-service coverage
Optimizes both cost and performance simultaneously
Integrates with common observability tools
Safety guardrails and rollback options

단점

Enterprise pricing may not suit small teams
Autonomous actions require trust and onboarding time
Best value depends on workload scale and variability

리뷰

4.8

5개 평가의 평균.

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Marcus Bell

Solid for our team

We rolled this out across the team last quarter and integrates with common observability tools. Continuous cost optimization fits neatly into how we already work, and support for compute, Kubernetes, and serverless removed a step we used to do by hand. Best value depends on workload scale and variability, which is the main caveat, but it has held up under daily use.

Rina Desai

Does the job

Pretty happy overall. Autonomous rightsizing and scaling just works and integrates with common observability tools. but no dealbreakers — I'd recommend it to a friend without hesitating.

Devin Walker

Compared a few options

Evaluated this against two competitors. Where it wins: policy-based guardrails and approvals and closed-loop automation reduces manual tuning. On balance the feature set — especially integrations with Datadog, Prometheus, and CloudWatch — justifies the 5 stars for our use case.

Beatriz Costa

Solid for our team

We rolled this out across the team last quarter and closed-loop automation reduces manual tuning. Autonomous rightsizing and scaling fits neatly into how we already work, and autonomous rightsizing and scaling removed a step we used to do by hand. Best value depends on workload scale and variability, which is the main caveat, but it has held up under daily use.

Naomi Suzuki

Solid for our team

We rolled this out across the team last quarter and closed-loop automation reduces manual tuning. Performance and availability monitoring fits neatly into how we already work, and performance and availability monitoring removed a step we used to do by hand. but it has held up under daily use.