Sedai for GKE is an autonomous cloud optimization platform that continuously analyzes actual resource usage in both GKE Standard and Autopilot clusters. It right-sizes CPU, memory, GPU, and persistent disk allocations to match real demand, reducing cloud costs and operational overhead. Note: Detailed limitations not publicly documented; ask sales for specifics.
Sedai analyzes historical CPU and memory usage for every workload and continuously adjusts resource requests to match real demand. This applies to both GKE Standard and Autopilot clusters, helping teams avoid over-provisioning and reduce unnecessary cloud spend. Note: Sedai's optimizations are gradual and validated for safety, but teams with highly custom workloads should review recommendations before enabling full autonomy.
Yes, Sedai supports autonomous optimization for both GKE Standard and Autopilot clusters. In Standard mode, it consolidates pods onto fewer nodes and helps the cluster autoscaler terminate idle capacity. In Autopilot, it right-sizes requested resources to avoid overpaying for unused capacity. Note: Some advanced GKE features may require additional configuration; consult documentation for details.
Yes, Sedai identifies idle GPU capacity, right-sizes workloads to the most cost-effective GPU types, and flags overprovisioned or unattached persistent disks based on real I/O patterns. This helps reduce costs associated with specialized resources. Note: For highly specialized GPU workloads, manual review of recommendations is advised before enabling full autonomy.
Sedai uses a patented safety-by-design approach: it performs continuous health verification before, during, and after every optimization, makes incremental changes, and automatically rolls back if risk is detected. This minimizes the risk of outages or SLO breaches. Note: Teams with strict change control requirements should validate integration with their governance processes.
Yes, Sedai complements the GKE cluster autoscaler by rightsizing workload requests and consolidating pods, which enables the autoscaler to terminate idle nodes more efficiently. This results in better resource utilization and lower costs. Note: For clusters with custom autoscaler configurations, review integration guidelines in Sedai's documentation.
Sedai integrates with 12 APMs (including Prometheus, Datadog, AWS CloudWatch, Azure Monitor, and Google Cloud Monitoring), Kubernetes autoscalers (HPA/VPA, Karpenter), IaC and CI/CD tools (GitHub, GitLab, Bitbucket, Terraform), ITSM tools (ServiceNow, PagerDuty, Jira), and notification platforms. This enables streamlined operations and compatibility with existing workflows. Note: Some integrations may require additional setup; see documentation for details.
Sedai delivers up to 50% reduction in cloud costs by rightsizing workloads, reduces latency by up to 75%, and automates repetitive tasks for up to 6X productivity gains. It also proactively resolves performance and availability issues, reducing failed customer interactions by up to 70%. Note: Actual results may vary based on workload and environment; detailed limitations not publicly documented.
Initial setup for Sedai can be completed in as little as 15 minutes using agentless or agent-based deployment. For advanced AI Agent Optimization, implementation typically takes two to three weeks. Note: Complex environments or custom integrations may require additional time.
Sedai uses resource-based pricing, determined by the resources optimized and the value delivered. For Kubernetes environments, including GKE, Sedai offers tailored pricing. All costs are transparently outlined on the Sedai pricing page, with no hidden fees. Note: For a custom quote, contact Sedai sales or request a demo.
Yes, Sedai offers a free Proof of Value and a 30-day free trial, allowing teams to evaluate the platform's benefits before committing. Note: After the trial, standard resource-based pricing applies.
Yes, Sedai is SOC 2 certified, demonstrating adherence to stringent security and compliance standards for data protection. For more details, visit the Sedai Security page. Note: For additional certifications or compliance requirements, contact Sedai directly.
Sedai is designed for IT/cloud operations, FinOps, technology leadership, SREs, and platform engineering teams in organizations using GKE. It is best suited for teams seeking to reduce cloud costs, improve performance, and automate operational tasks. Note: Teams with highly specialized or regulated environments should review compliance and integration requirements before adoption.
While the knowledge base highlights customer success stories for AWS Lambda and other platforms (e.g., KnowBe4, Palo Alto Networks, Belcorp), specific public case studies for GKE optimization are not documented. For the latest GKE-specific references, contact Sedai sales. Note: Case studies for other platforms are available on the Sedai resources page.
Sedai provides comprehensive onboarding guides, technical documentation, and real-time support via a community Slack channel. Documentation for getting started, Kubernetes optimization, and advanced features is available at docs.sedai.io/get-started. Note: Some advanced topics may require direct support from Sedai's technical team.
Introducing Sed: Your cloud & AI assistant
Meet SedGKE removes the operational burden of running Kubernetes, but resource decisions like pod requests, node packing, and GPU allocation still drive your bill. Sedai continuously analyzes actual usage and safely adjusts resources in both Standard and Autopilot clusters, so you stop paying for capacity you don't use.
Google manages the control plane, but the resource decisions that drive cost are still yours to get right, and most teams don't have the bandwidth to do it continuously.
CPU and memory requests get set from worst-case estimates and rarely revisited, wasting resources regardless of GKE mode.
Standard leaves you paying for underutilized nodes; Autopilot bills you for requested resources whether you use them or not.
GPUs and high-performance disks are frequently over-provisioned for peak loads that rarely occur.
Autonomous Workload Rightsizing
Sedai analyzes historical CPU and memory usage for every workload and continuously adjusts resource requests to match real demand, in both Standard and Autopilot clusters.
Smarter Node Infrastructure (Standard)
Sedai consolidates pods onto fewer, better-utilized nodes, helps the cluster autoscaler terminate idle capacity faster, and recommends more cost-effective instance types for your node pools.
GPU & Persistent Disk Optimization
Sedai identifies idle GPU capacity, right-sizes workloads to the most cost-effective GPU types, and flags overprovisioned or unattached Persistent Disks based on real I/O patterns.
