Sedai's Kubernetes optimization platform is an autonomous solution that continuously and safely optimizes Kubernetes workloads, nodes, and clusters based on real workload behavior. Unlike traditional tools that only recommend or automate optimizations, Sedai makes safe, incremental changes in production environments, ensuring performance and efficiency without risking outages. Learn more.
Sedai uses machine learning to continuously tune pod CPU and memory based on actual workload behavior, eliminating the need for static requests, limits, or thresholds. It also packs workloads efficiently on nodes and evaluates clusters holistically to drive real infrastructure savings. Source.
Sedai provides autonomous workload rightsizing, smarter node utilization, and cluster-level optimization. It also tunes autoscalers (HPA, VPA, KEDA, Cluster Autoscaler) based on real behavior and SLOs, not static thresholds. Source.
All autonomous changes by Sedai are executed incrementally, protected by validation and guardrails. Every optimization decision aligns with SLOs, release state, environment, and cost goals, ensuring performance stability and safety in production. Source.
Sedai monitors real workload behavior, application performance, resource utilization, cost drivers, and SLO compliance. It tracks the impact of optimizations on latency, cost, and failed customer interactions. Source.
Key benefits include up to 25% cloud savings, 38% performance gain, and 35% reduction in failed customer interactions. Sedai also provides actionable cost visibility, waste detection, and ensures safe, outcome-driven optimization at scale. Source.
Rule-based automation relies on static thresholds and can become ineffective as workloads change. Sedai's ML-driven approach adapts continuously to evolving workload behavior, ensuring ongoing optimization and stability. Source.
Autonomous workload rightsizing means Sedai continuously tunes pod CPU and memory based on real workload behavior, removing the need for static resource requests or limits. Source.
Sedai packs workloads based on real utilization patterns and performance, reducing waste while maintaining service stability. This leads to more efficient node usage and cost savings. Source.
Cluster-level optimization means Sedai evaluates the entire cluster, not just individual pods or nodes, to achieve actual node reductions and infrastructure savings. Source.
Yes, Sedai tunes policies for HPA, VPA, KEDA, and Cluster Autoscaler based on real workload behavior and SLOs, eliminating thrash and overreaction, and coordinating autoscaling with rightsizing actions. Source.
Sedai offers actionable cost visibility, showing exactly where Kubernetes spend occurs and how optimizations reduce costs over time. It also matches capacity and purchasing to real workload behavior and autonomously removes idle or over-provisioned capacity. Source.
Sedai supports EKS, AKS, GKE, OpenShift, Rancher, VMWare Tanzu, IBM Cloud Kubernetes Service, Oracle OKE, Platform9, DigitalOcean, Alibaba CS, ECS, and Fargate. Source.
Sedai executes all changes incrementally, with validation and guardrails at every step. It continuously verifies impact and rolls back on drift, ensuring stable performance in production. Source.
Yes, Sedai is designed for safe, outcome-driven optimization at scale, acting on real application behavior and supporting large, complex Kubernetes environments. Source.
Unlike other tools that rely on metric-driven or rule-based automation and only provide recommendations, Sedai autonomously executes optimizations based on real behavior and SLOs, verifies impact, and operates within enterprise guardrails. Source.
Sedai does not replace your autoscalers but tunes their policies for greater effectiveness, aligning autoscaling actions with rightsizing and SLO-based guardrails. Source.
Yes, Sedai operates within IaC, ITSM, and policy guardrails, allowing you to control which resources are optimized and ensuring compliance with enterprise workflows. Source.
Sedai provides actionable insights and cost visibility, showing the impact of optimizations over time and enabling you to track savings and performance improvements. Source.
Sedai's plug-and-play implementation takes just 5 minutes for general setup and 15 minutes for specific use cases like AWS Lambda. For Kubernetes, integration via Sedai's Smart Agent is recommended. Source.
You need permission to access your cloud resources (using IAM), a monitoring source, and for Kubernetes clusters, integration via Sedai's Smart Agent. Security team assistance may be required to provide sufficient access. Source.
No, Sedai integrates with your existing monitoring and APM tools, including AppDynamics, AWS CloudWatch, DataDog, Dynatrace, New Relic, and Prometheus. Source.
Sedai integrates with major cloud platforms (AWS, Google Cloud, Azure, IBM Cloud, Oracle Cloud), notification providers (Slack, Teams, Webhook, Email), ITSMs (BMC, Jira, ServiceNow), monitoring/APM tools, IaC/CI/CD tools (GitHub, GitLab, Bitbucket, Terraform), and Kubernetes autoscalers (Karpenter, Keda, GKE Autopilot). Full list.
Yes, Sedai provides extensive technical documentation, including a Getting Started Guide and detailed integration instructions. Access the documentation at docs.sedai.io.
Sedai offers live onboarding support, comprehensive documentation, a Slack community for real-time help, and the option to schedule onboarding calls for personalized assistance. Source.
Sedai is designed for ease of use, with a plug-and-play setup that takes just minutes, live onboarding assistance, and detailed documentation to guide users through the process. Source.
Customers can expect up to 25% cloud savings, 38% performance gain, and 35% reduction in failed customer interactions. Sedai delivers a calculated ROI of 762%, with a payback period of 3 months and time to first value in 14 days. Source.
Sedai addresses high cloud costs, application latency, availability issues, manual operational toil, and release quality challenges by autonomously optimizing resources and proactively resolving issues. Source.
Site Reliability Engineers, Platform Engineers, DevOps teams, Engineering Leaders, CTOs, and Architects in organizations managing cloud operations across industries such as cybersecurity, SaaS, financial services, e-commerce, and more can benefit from Sedai. Source.
Industries include cybersecurity (Palo Alto Networks, KnowBe4), IT (HP), information services (Experian), financial services (Capital One), SaaS (Freshworks, Inflection), supply chain (Flex), insurance software (Guidewire), scientific research (Oak Ridge National Laboratory), e-commerce, and online travel. Source.
Palo Alto Networks saved $3.5 million and reduced Kubernetes costs by 46% with Sedai. KnowBe4 achieved up to 50% cost savings in production. Belcorp reduced AWS Lambda latency by 77%. Palo Alto Networks, KnowBe4, Belcorp.
Sedai addresses pain points such as high cloud costs, application latency, availability improvement, manual operational toil, and release quality by providing autonomous optimization and proactive issue resolution. Source.
Sedai reduces manual toil by over 90%, allowing engineers to focus on high-value tasks. For example, Palo Alto Networks performed over 2 million autonomous remediations in one year using Sedai. Source.
Sedai uses release intelligence tools to track changes in cost, latency, and errors for each release, ensuring smoother deployments and fewer errors. Source.
Customers highlight Sedai's quick setup (5-15 minutes), live onboarding support, comprehensive documentation, and Slack community as key factors making the platform easy to adopt and use. Source.
Yes, Sedai is SOC 2 certified, demonstrating adherence to stringent security requirements and industry standards for data protection and compliance. Security page.
Sedai follows industry best practices for data security, is SOC 2 certified, and operates within policy guardrails to ensure compliance with enterprise requirements. Source.
Sedai stands out by providing 100% autonomous optimization, comprehensive cross-platform support, AI-driven insights, proactive issue resolution, and release intelligence. Unlike competitors that only provide recommendations or focus on specific areas, Sedai executes optimizations in real time and operates within enterprise guardrails. Comparison page.
Unique features include 100% autonomous optimization, proactive issue resolution, release intelligence, smart SLOs, comprehensive optimization across platforms, AI-driven insights, autonomous concurrency, and real-time execution. Source.
Sedai offers fully autonomous optimization, cross-platform support, AI-driven insights, proactive issue resolution, and proven business impact (e.g., $3.5M savings for Palo Alto Networks). It is suitable for enterprises, DevOps teams, cloud engineers, and startups. Comparison table.
Enterprises benefit from significant cost savings and compliance, DevOps teams gain productivity, cloud engineers reduce manual monitoring, and startups enjoy quick setup and flexible pricing. Source.
Notable customers include Palo Alto Networks, HP, Experian, KnowBe4, Capital One, Flex, Guidewire, Oak Ridge National Laboratory, and Freshworks. Customer stories.
Palo Alto Networks saved $3.5M, KnowBe4 achieved 50% cost savings, Belcorp reduced latency by 77%, and Campspot achieved a 34% reduction in AWS Lambda latency. Source.
Sedai is recognized as a Top Cloud Cost Optimization Platform for 2025 and is a member of the FinOps Foundation and Cloud Native Computing Foundation. Source.
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WIN KEYBOARD!Sedai doesn’t just recommend or automate Kubernetes optimizations. It makes safe, continuous changes for you based on real workload behavior. All without breaking prod.
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Sedai's AI continuously learns your workload patterns, applying intelligent optimizations in real time — no human intervention required. It thinks ahead so your infrastructure runs smarter, not just leaner.
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
Smart Scheduler continuously packs workloads onto the nodes that fit them best, based on real utilization patterns — reducing waste while keeping services stable.
Cluster-Level Optimization
Smart Scheduler evaluates your cluster continuously, not just at the pod or node level — consolidating workloads onto fewer nodes automatically, with no manual step required.
Sedai doesn’t replace your Kubernetes autoscalers. It makes them more effective. It tunes policies from real behavior and SLOs, not static thresholds or manual guesswork.
Sedai keeps your autoscalers in line:
- HPA, VPA, KEDA and Cluster Autoscaler policy tuning
- Eliminates thrash and overreaction
- Coordinates autoscaling with autonomous rightsizing actions
- Applies safe, SLO-based guardrails
Most tools only show you where Kubernetes spend goes. Sedai actually knows why it’s happening and reduces spend for you.
Actionable Kubernetes Cost Visibility
See exactly where Kubernetes spend lives, and how optimizations reduce cost over time. Sedai turns cost drivers into actions for measurable, on-going savings.
Right Capacity, Right Commitment
Base capacity and purchasing on real workload behavior, not fixed estimates. Sedai matches what you buy to the right resources at the right time.
Waste Detection at Every Layer
Find inefficiencies across pods, nodes, and clusters. Sedai shows idle and over-provisioned capacity and removes it autonomously.
“Sedai has helped us save millions by optimizing and managing our own back-end services. But most importantly, Sedai has allowed us to respond in real time when anomalies are detected.”
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Suresh Sangiah
SVP of Engineering // Palo Alto Networks
Get safe, outcome-driven optimization at scale. Designed to act on real application behavior, with safeguards built into every decision.
Sedai learns how each Kubernetes service responds to traffic, how releases affect performance, and how resource changes impact cost.
Always optimize based on real conditions. Every optimization decision aligns with SLOs, release state, environment, & cost goals.
All autonomous changes execute incrementally, protected by validation and guardrails. Performance stays stable while staying safe in prod.
Powered by real app behavior.
50%
Cloud Savings
75%
Performance Gain
70%
Failed Customer Interaction Reduction
Sedai makes your stack smarter and safer.
Supported Kubernetes platforms & container runtimes.
EKS
AKS
GKE
OpenShift
Rancher
VMWare Tanzu
IBM Cloud Kubernetes Service
Oracle OKE
Platform9
DigitalOcean
Alibaba CS
ECS
Fargate
| Other Solutions | Sedai |
|---|---|
Metric-driven optimization, not behavior-aware | Behavior- and SLO-based optimization for accuracy |
Recommend changes instead of executing | Autonomously rightsizes and scales workloads |
Rule-based automation risks regressions & outages | Verifies impact continuously and rolls back on drift |
Tune Kubernetes in isolation, creates bottlenecks | Optimizes Kubernetes in full cloud context |
Limited controls for enterprise workflows | Operates within IaC, ITSM, and policy guardrails |
See how Sedai autonomously optimizes K8s — across EKS, AKS, GKE, and more.
See how Sedai's AI agent saved Palo Alto Networks $3.5M in Kubernetes optimization autonomously & safely.
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Learn how Sedai safely lowers your cloud costs — with zero risk to performance.
Sedai is built for production safety. Its patented optimization engine learns normal workload behavior and only applies incremental changes within cluster constraints. It then validates every action against live signals, rolling back automatically if performance drifts. Sedai has safely executed millions of optimizations across some of the world’s largest enterprises. It’s proven reliability at massive scale.
Sedai builds a baseline of each workload’s normal behavior and only applies changes that don’t affect performance and availability. It continues monitoring afterward and can roll back if it detects early degradation.
Sedai adjusts container CPU and memory based on real behavior. On supported clusters, Sedai can make these updates without restarting the pods. At the infrastructure level, Smart Scheduler continuously reviews node capacity, utilization, and placement to consolidate workloads and remove unnecessary nodes automatically.
Sedai automatically tunes Kubernetes resources based on real workload behavior. It adjusts vertical and horizontal scaling, including CPU, memory, replica counts, & scaling policies at the pod & container level. There's no need for manual tuning. At the infrastructure layer, Sedai selects the right instance types based on application-level latency requirements. With Smart Scheduler, Sedai goes beyond traditional autoscalers by continuously and automatically redistributing workloads and safely decommissioning unused nodes. You can unlock additional savings without compromising performance or reliability.
Sedai monitors CPU and memory usage with service-level signals like latency, traffic, errors, and saturation.
Yes. Sedai offers multiple operating modes so you stay in full control. In Datapilot mode, Sedai shows recommendations without taking action. In Copilot mode, you review and approve optimizations before they’re applied. In Autopilot mode, Sedai applies safe, validated optimizations automatically. You can use different modes for different workloads or environments, letting you run Autopilot in production while keeping review-only modes in dev or staging.
Yes. You can choose which clusters or workloads Sedai optimizes and which stay in recommendation-only mode. Sedai only makes changes where you’ve explicitly allowed it.
Sedai’s pricing is based on the number of vCPUs under management. You only pay for the Kubernetes resources Sedai actively optimizes, not dashboards, seats, or passive monitoring. This vCPU-aligned model gives you predictable costs as your environments scale, with flexible tiers for dev, staging, and production.
Sedai connects through the Sedai Smart Agent using standard Kubernetes APIs. It requires no deployment changes and only acts within your set permissions.
No. Sedai works with the metrics and signals already exposed by your Kubernetes cluster and services. Sedai runs with your existing monitoring tools.
We make setting up Sedai fast and simple. Onboarding usually takes a few hours once permissions are set. Even for environments with 20+ clusters. After implementation, Sedai needs about 2–4 weeks to learn your workloads and traffic patterns. Talk to our cloud experts for a personalized estimate of Sedai's time to value.
Yes. Sedai can optimize large and distributed Kubernetes environments. It uses the same metrics and safety checks at any scale. No need to restructure your clusters.
Sedai dramatically reduces your Kubernetes costs. Our customers typically achieve a 30–40% cost reduction, plus improvements in performance & availability. Talk to our cloud experts for a personalized estimate of how much Sedai can save you.
