Frequently Asked Questions
Amazon EKS Optimization Features & Capabilities
How does Sedai optimize Amazon EKS clusters?
Sedai continuously right-sizes pod CPU and memory requests based on actual consumption, eliminating static worst-case allocations and reducing cloud waste. It also selects optimal instance types, redistributes workloads, and applies cluster compaction to maximize node utilization and reduce compute spend. Additionally, Sedai provides workload-level cost attribution and purchasing recommendations across on-demand, savings plans, and reserved terms. Note: Sedai's optimizations are gradual and validated for safety, but detailed limitations for highly specialized EKS configurations are not publicly documented; ask sales for specifics.
Does Sedai support both managed node groups and self-managed nodes in EKS?
Yes, Sedai supports optimization for both managed node groups and self-managed nodes within Amazon EKS clusters. This allows teams to benefit from autonomous optimization regardless of their node management strategy. Note: For advanced or custom node group configurations, consult Sedai's documentation or sales team for compatibility details.
How does Sedai interact with Cluster Autoscaler or Karpenter in EKS environments?
Sedai integrates with Kubernetes autoscalers such as Cluster Autoscaler and Karpenter. By continuously right-sizing pod resource requests, Sedai ensures that autoscalers receive accurate signals, preventing overprovisioning caused by inflated resource requests. This leads to more efficient scaling and reduced cloud costs. Note: Integration specifics may vary by autoscaler version; review Sedai's technical documentation for the latest compatibility details.
Will Sedai's changes affect running workloads or cause pod restarts?
Sedai's patented safety-by-design approach ensures that optimizations are made incrementally and validated for health at every step. While some optimizations (such as resource request changes) may require pod restarts, Sedai performs continuous health verification and can automatically roll back changes if risk is detected. Note: In environments with strict uptime requirements, teams should review Sedai's rollout and rollback mechanisms to ensure alignment with their policies.
Do you support Fargate profiles on EKS?
Sedai supports optimization for EKS clusters, including those using Fargate profiles. This enables teams running serverless Kubernetes workloads on AWS Fargate to benefit from autonomous optimization. Note: Some advanced Fargate-specific features may have limited support; consult Sedai's documentation for the latest details.
How long does it take before Sedai starts making changes in an EKS environment?
Initial setup for Sedai can be completed in as little as 15 minutes using agentless or agent-based deployment. For most EKS use cases, Sedai begins reading metrics and analyzing workloads immediately after setup. Autonomous optimizations typically start within days, depending on the organization's chosen mode (Datapilot, Copilot, or Autopilot) and safety policies. Note: For AI Agent Optimization, implementation may take two to three weeks. Highly regulated environments may require additional validation steps.
What integrations does Sedai support for EKS optimization?
Sedai integrates with 12 APMs, including Prometheus, Datadog, AWS CloudWatch, Azure Monitor, and Google Cloud Monitoring. It supports Kubernetes autoscalers (HPA/VPA, Karpenter), CI/CD tools (GitHub, GitLab, Bitbucket, Terraform), ITSM tools (ServiceNow, PagerDuty, Jira), and notification platforms. This ensures Sedai fits into existing EKS workflows. Note: Integration depth may vary by tool; review Sedai's documentation for specifics.
Pricing & Plans
How is Sedai priced for Amazon EKS optimization?
Sedai uses a resource-based pricing model, where costs are determined by the resources optimized and the value delivered. For Kubernetes environments like EKS, Sedai offers tailored pricing, and all costs are transparently outlined on the Sedai pricing page. Discounts from connected cloud billing accounts (e.g., Reserved Instances, Savings Plans) are factored into cost and savings calculations. Note: For a custom quote or to discuss your EKS use case, contact Sedai's sales team. Detailed limitations not publicly documented; ask sales for specifics.
Implementation & Ease of Use
How easy is it to get started with Sedai for EKS optimization?
Customers report that Sedai's initial setup can be completed in as little as 15 minutes using agentless or agent-based deployment. The platform integrates with existing EKS tools and workflows, and operates autonomously to minimize management burden. Personalized onboarding, extensive documentation, and a community Slack channel provide real-time assistance. Note: For complex EKS environments, onboarding may require additional validation steps.
What technical documentation is available for Sedai's EKS optimization?
Sedai provides comprehensive onboarding guides, Kubernetes optimization documentation, and step-by-step instructions for implementing Sedai in EKS environments. These resources are available at docs.sedai.io/get-started. Note: For advanced or custom EKS scenarios, consult Sedai's support team for guidance.
Security & Compliance
What security and compliance certifications does Sedai have?
Sedai is SOC 2 certified, demonstrating adherence to stringent security requirements and industry standards for data protection and compliance. For more details, visit the Sedai Security page. Note: For additional certifications or compliance needs, contact Sedai's security team.
Business Impact & Customer Proof
What measurable results have customers achieved with Sedai for EKS and cloud optimization?
Customers have reported up to 50% reduction in cloud costs, up to 75% reduction in application latency, and up to 6X productivity gains for engineering teams. For example, KnowBe4 achieved a 99.5% reduction in AWS Lambda response time, and Palo Alto Networks saved $3.5 million through Sedai's optimization. Note: Results may vary by environment and workload; see Sedai's resources page for detailed case studies.
Which industries and companies have used Sedai for cloud optimization?
Sedai's customers include companies in cybersecurity (Palo Alto Networks, KnowBe4), beauty and personal care (Belcorp), travel and hospitality (Campspot), background check services (Inflection), and customer engagement software (Freshworks). These organizations have used Sedai to optimize cloud costs and performance in high-stakes environments. Note: For industry-specific references, visit Sedai's resources page.
Pain Points & Use Cases
What common EKS pain points does Sedai address?
Sedai addresses overprovisioned pods that corrupt autoscaler signals, manual optimization burdens at cluster scale, and reliability risks from incorrect resource settings. By continuously tuning resources and validating changes, Sedai eliminates cloud waste, reduces operational toil, and protects availability. Note: For highly customized EKS workloads, some manual review may still be required; consult Sedai's support for guidance.
Who can benefit most from Sedai's EKS optimization?
Sedai is designed for IT/cloud operations managers, FinOps leads, technology leaders (CTO, CIO, VP Engineering), SREs, and platform engineers managing EKS clusters. It is best suited for organizations seeking to reduce cloud costs, improve performance, and automate repetitive operational tasks. Note: Teams with highly specialized or legacy EKS setups should confirm compatibility with Sedai's sales or support team.
