Sedai

Overview

Sedai is an autonomous cloud operations platform that uses patented deep reinforcement learning to continuously manage cloud infrastructure — detecting degradation, preventing outages, and optimizing resource allocation without human intervention. Unlike rule-based automation or threshold-triggered alerts, Sedai's RL Decision Engine learns the optimal trade-offs between cost, performance, and availability for each specific workload and continuously acts on those learnings.

The platform has executed over 25 million autonomous production actions with zero incidents — a remarkable track record that reflects the maturity of its RL approach. Customers including Palo Alto Networks, Experian, and HP have used Sedai to achieve up to 65% cost reduction on Kubernetes workloads while simultaneously improving availability. With $20M in Series B funding raised in 2025, Sedai is investing heavily in expanding its autonomous capabilities.

Key Features

• Deep RL Decision Engine: Continuously learns optimal cost/performance/availability trade-offs for each workload
• Self-Healing: Detects and autonomously remediates degradation in production before user impact
• Smart Scaling: Dynamic Kubernetes and VM resource allocation using reinforcement learning
• Seasonality Modeling: ML models that understand traffic patterns and pre-scale accordingly
• Copilot Mode: Human-approved recommendations for teams building trust with autonomous actions
• Autopilot Mode: Fully autonomous operation for mature deployments
• 65% K8s Cost Reduction: Documented cost savings through intelligent right-sizing
• AWS Marketplace: Available for streamlined procurement

Pricing Details

Sedai uses usage-based custom pricing available on request and through AWS Marketplace. Contact Sedai for pricing based on the scale of your cloud environment.

Pros and Cons

Pros

• Deep RL approach learns workload-specific patterns for more accurate optimization than rule-based systems
• 25M+ autonomous production actions with zero incidents is a compelling safety track record
• Gradual trust-building via Copilot → Autopilot mode progression reduces adoption risk
• AWS Marketplace availability simplifies procurement

Cons

• Full autonomous value requires time for RL models to learn workload patterns
• Custom pricing requires a sales conversation
• Best suited for cloud-native Kubernetes environments — less applicable to legacy infrastructure

Who Should Use This Tool?

Sedai is ideal for platform engineering and FinOps teams managing significant Kubernetes workloads who want to move beyond manual resource management and threshold-based autoscaling. Organizations with substantial cloud spend and cloud-native architectures will see the strongest ROI from Sedai's continuous optimization.

Final Verdict

Sedai's deep reinforcement learning approach and 25M+ autonomous action track record make it one of the most mature autonomous cloud operations platforms available. For cloud-native teams ready to embrace autonomous infrastructure management, Sedai offers a compelling path to both cost reduction and improved reliability.