Sedai positions itself as a self-driving cloud platform, and the analogy is apt. Where traditional cloud cost optimization tools show you dashboards and make recommendations that engineers must manually implement, Sedai uses patented reinforcement learning to autonomously analyze, optimize, and adjust cloud resources in real time. The platform continuously learns how your applications actually perform under real production conditions, identifies optimal configurations, and safely makes changes without human intervention.
The safety-first approach is central to Sedai's design philosophy. Rather than making sweeping changes, the platform takes action in gradual steps using a patented process to validate safety at each stage. This means each optimization is tested against live production behavior before being fully applied. The company claims to have never caused an incident across its customer base, which includes demanding organizations like Palo Alto Networks, Avis, Experian, and HP managing critical production infrastructure.
Sedai offers two operational modes to accommodate different comfort levels with autonomous control. Copilot mode presents optimization recommendations that engineers approve with a single click, maintaining human oversight while eliminating the analysis and planning work. Autopilot mode enables fully autonomous operations where Sedai executes optimizations without manual approval. Teams typically start with Copilot and graduate to Autopilot as trust builds through demonstrated safe operation.
Cost optimization capabilities span the full cloud resource spectrum. For Kubernetes workloads on EKS, ECS, or other platforms, Sedai optimizes both horizontal and vertical scaling by adjusting memory, CPU, and replica counts at the container and pod level. Cluster Compaction intelligently redistributes workloads and decommissions unused nodes. For serverless, it finds optimal memory and CPU settings for Lambda functions and dynamically manages provisioned concurrency when cost-effective. Purchasing optimization evaluates on-demand versus savings plans to recommend the lowest-cost infrastructure options.
Performance optimization runs in parallel with cost reduction. Sedai discovers how applications perform with different configurations and resource allocations, then continuously tunes settings to deliver the best possible user experience. Traffic prediction using ML detects seasonality patterns and proactively scales resources to meet demand peaks while minimizing spend during quiet periods. This predictive approach outperforms reactive autoscalers that only respond after demand has already changed.
Availability improvement is the third pillar. Sedai detects and remediates performance issues including out-of-memory errors and pod restarts before they cause outages, eliminating what the platform calls Failed Customer Interactions. It also predicts how resource needs will change in the future, enabling preemptive capacity adjustments rather than reactive incident response.