What Sedai Does
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.
Safety-First Design and Operational Modes
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 and Performance Optimization
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 and Release Intelligence
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.
Release Intelligence provides production performance analysis of every deployment with scorecards covering latency, cost, and error metrics. Teams can spot regressions instantly and understand the real-world impact of code changes on infrastructure performance. After each release, Sedai automatically adjusts configurations to optimal levels for the new code, eliminating the manual tuning that typically follows deployments.
Smart SLOs and Multicloud
Smart SLOs allow teams to define service level objective goals that Sedai autonomously optimizes resources to meet. The platform can also recommend SLO targets and error budgets based on historical workload performance, bridging the gap between engineering teams who may not have established formal reliability targets and the operational maturity those targets represent.
The platform supports multicloud environments across AWS, Azure, and Google Cloud with specific integrations for EKS, ECS, Lambda, EC2, S3, EBS, and Databricks. It connects to existing monitoring tools like Datadog and CloudWatch for real-time performance metrics and integrates with notification providers, CI/CD pipelines, and service management platforms. A full audit trail of all production changes supports compliance requirements.
The Bottom Line
Customer results are compelling. Palo Alto Networks saved $3.5 million through autonomous management of thousands of changes. Freshworks achieved 34 percent latency reduction with some Lambda functions seeing up to 95 percent improvement. KnowBe4 reached 98 percent autonomous operation across ECS and Lambda with 27 percent cost reduction. These enterprise-scale results across mission-critical workloads validate the autonomous approach for organizations large enough to justify the investment.
