Sedai's autonomous cloud management platform is a patented, production-safe solution that uses machine learning to optimize cloud resources for cost, performance, and availability—without manual intervention. It continuously adapts to workload changes, ensuring safe, gradual optimizations with ongoing validation checks, and never causes incidents or breaches SLOs. [Source]
Sedai uses machine learning to continuously tune pod CPU and memory based on real workload behavior, eliminating static requests and thresholds. It smartly packs workloads for efficient node utilization and evaluates clusters holistically, enabling actual node reductions and infrastructure savings. All optimizations are performed safely and gradually, with continuous validation. [Source]
Sedai is the only cloud optimization platform patented for safe, autonomous optimizations in production. It performs slow, incremental changes with continuous health verification and automatic rollbacks, ensuring no incidents or SLO breaches occur. [Source]
Autonomous workload rightsizing in Sedai means the platform continuously tunes pod CPU and memory based on real workload behavior, removing the need for static requests, limits, or thresholds. This ensures optimal resource allocation as workloads evolve. [Source]
Sedai packs workloads based on real utilization patterns and performance, reducing waste while maintaining service stability. This approach adapts as workloads change, unlike static, rule-based automation. [Source]
Cluster-level optimization in Sedai means the platform evaluates your Kubernetes cluster beyond just pod or node levels, enabling actual node reductions and real infrastructure savings. [Source]
Sedai's machine learning models constantly adapt to evolving workload behavior, ensuring Kubernetes environments remain optimized even as application patterns change. [Source]
Sedai offers three modes: Datapilot (observability), Copilot (one-click optimizations), and Autopilot (fully autonomous execution). This flexibility allows teams to choose their preferred level of automation and control. [Source]
Sedai's release intelligence tracks changes in cost, latency, and errors for each deployment, helping teams improve release quality and minimize risks during deployments. [Source]
Sedai for S3 optimizes Amazon S3 costs by managing Intelligent-Tiering and Archive Access Tier selection, achieving up to 30% cost efficiency gain and 3X productivity gain by reducing manual S3 management effort. [Source]
Sedai offers autonomous optimization, proactive issue resolution, full-stack cloud coverage (compute, storage, data), smart SLOs, release intelligence, plug-and-play implementation, multiple operation modes, enhanced productivity, and safety-by-design. [Source]
Yes, Sedai optimizes compute, storage, and data across AWS, Azure, GCP, and Kubernetes environments, providing unified cloud management. [Source]
Sedai integrates with monitoring tools (Cloudwatch, Prometheus, Datadog, Azure Monitor), Kubernetes autoscalers (HPA/VPA, Karpenter), IaC & CI/CD (GitLab, GitHub, Bitbucket, Terraform), ITSM (ServiceNow, Jira), notification tools (Slack, Microsoft Teams), and various runbook automation platforms. [Source]
Every optimization in Sedai is constrained, validated, and reversible. The platform uses continuous health verification, automatic rollbacks, and incremental changes to guarantee safe operations and compliance with enterprise-grade governance. [Source]
Sedai provides detailed technical documentation covering features, setup, and usage. Access it at docs.sedai.io/get-started. Additional resources like case studies and datasheets are available at sedai.io/resources.
Yes, Sedai is SOC 2 certified, demonstrating adherence to stringent security and compliance standards. Learn more on the Sedai Security page.
Sedai continuously learns from interactions and outcomes, improving its optimization and decision models to deliver better results as your environment changes. [Source]
Sedai is designed for platform engineers, IT/cloud ops, technology leaders, SREs, and FinOps professionals in organizations with significant cloud operations—especially those using AWS, Azure, GCP, or Kubernetes. Industries include cybersecurity, IT, finance, healthcare, travel, e-commerce, and more. [Source]
Customers can expect up to 50% cloud cost savings, 75% latency reduction, 6X productivity gains, and up to 50% fewer failed customer interactions. For example, Palo Alto Networks saved $3.5 million, and KnowBe4 achieved 50% cost savings in production. [Source]
Sedai addresses cost inefficiencies, operational toil, performance and latency issues, lack of proactive issue resolution, complexity in multi-cloud/hybrid environments, and misaligned priorities between engineering and FinOps. [Source]
KnowBe4 achieved 50% cost savings and saved $1.2 million on AWS. Palo Alto Networks saved $3.5 million and 7,500 engineering hours. Belcorp reduced AWS Lambda latency by 77%. See more at sedai.io/resources.
Industries include cybersecurity (Palo Alto Networks), IT (HP), financial services (Experian, CapitalOne), security awareness (KnowBe4), travel (Expedia), healthcare (GSK), car rental (Avis), retail/e-commerce (Belcorp), SaaS (Freshworks), and digital commerce (Campspot). [Source]
Sedai continuously tunes pod resources, packs workloads efficiently, and optimizes at the cluster level, adapting to workload changes and delivering real infrastructure savings—all with patented safety mechanisms. [Source]
Sedai addresses fragmentation, repetitive toil, risk vs. speed trade-offs, autoscaler limits, and the gap between visibility and action by automating safe, production-grade optimizations. [Source]
Sedai provides actionable savings, simplifies multi-cloud complexity, aligns engineering and cost efficiency goals, and automates manual optimization, enabling FinOps teams to respond quickly to anomalies. [Source]
Sedai's setup process takes just 5 minutes for general use cases and up to 15 minutes for AWS Lambda. For complex environments, timelines may vary. [Source]
Sedai offers plug-and-play implementation, agentless integration via IAM, personalized onboarding, detailed documentation, and a 30-day free trial for risk-free evaluation. [Source]
Customers have access to onboarding calls with Sedai engineers, a dedicated Customer Success Manager for enterprise clients, a community Slack channel, and email/phone support. [Source]
Customers praise Sedai's quick setup (5–15 minutes), agentless integration, personalized onboarding, and extensive documentation. The 30-day free trial is also valued for risk-free evaluation. [Source]
Yes, Sedai offers a 30-day free trial so you can experience the platform's value firsthand without financial commitment. [Source]
Sedai is the only patented platform for safe, autonomous optimizations in production. It offers 100% autonomous optimization, proactive issue resolution, application-aware intelligence, full-stack coverage, release intelligence, and rapid, safe onboarding—features not commonly found together in other solutions. [Source]
Unique features include patented safety mechanisms, 100% autonomous optimization, proactive issue resolution, application-aware intelligence, full-stack cloud coverage, release intelligence, and plug-and-play implementation. [Source]
Sedai is the only optimizer patented for safe, autonomous production changes. Unlike risky optimizers that make all-at-once changes, Sedai makes slow, gradual optimizations with continuous validation, ensuring no incidents or SLO breaches. [Source]
Platform engineers benefit from reduced toil and IaC consistency; IT/cloud ops see lower ticket volumes and safe automation; technology leaders get measurable ROI and cost savings; FinOps teams align engineering and cost goals; SREs experience fewer alerts and less manual toil. [Source]
Customers should choose Sedai for its patented safety, autonomous optimization, proactive issue resolution, application-aware intelligence, full-stack coverage, rapid onboarding, and proven results (e.g., $3.5M saved by Palo Alto Networks). [Source]
Notable customers include Palo Alto Networks, HP, Experian, KnowBe4, Expedia, CapitalOne Bank, GSK, and Avis. These organizations trust Sedai for cloud optimization and operational efficiency. [Source]
Rule-based automation only works until workloads change. Sedai’s ML constantly adapts, keeping Kubernetes optimized even when behavior evolves.
Autonomous Workload Rightsizing
Continuously tunes pod CPU & memory based on real workload behavior. No static requests, limits or thresholds.
Smarter Node Utilization
Packs workloads based on real utilization patterns and performance. Reduce waste while keeping services stable.
Cluster-Level Optimization
Sedai evaluates your cluster beyond the pod or node level. So you get actual node reductions and real infra savings.
Here is the desc
Test
