Frequently Asked Questions
Product Information & Technical Requirements
What is Sedai's autonomous optimization for self-managed Kubernetes?
Sedai's autonomous optimization for self-managed Kubernetes continuously analyzes workload utilization and performance, then safely adjusts pod and container resource allocations using reinforcement learning. This approach helps rightsize clusters, reduce cloud costs, and improve performance without requiring manual intervention. Sedai is designed to work with your existing toolchain, refining configurations made by HPA or GitOps controllers rather than replacing them. Note: For air-gapped or highly regulated environments, deployment options and integration requirements should be reviewed with Sedai's technical team. Source
Does Sedai replace our HPA or GitOps controller?
No, Sedai does not replace your HPA (Horizontal Pod Autoscaler) or GitOps controller. Instead, Sedai continuously refines their configurations to optimize for cost and performance, connecting via a lightweight agent or GitOps integration. This allows you to maintain your existing toolchain while benefiting from autonomous optimization. Note: Sedai requires integration with your current Kubernetes environment for optimal results. Source
How does Sedai connect to a self-managed or air-gapped Kubernetes cluster?
Sedai connects to self-managed or air-gapped Kubernetes clusters via a lightweight agent or through GitOps integration, depending on your security and operational requirements. This flexible approach allows Sedai to fit within your existing security posture. For fully air-gapped deployments, consult Sedai's technical documentation or support team to ensure compatibility. Note: Some advanced features may require outbound connectivity; review requirements with Sedai before deployment. Source
Can Sedai be deployed fully within our own environment, including air-gapped setups?
Yes, Sedai can be deployed fully within your own environment, including air-gapped Kubernetes clusters. Deployment options include agent-based or GitOps integration to meet your security and compliance needs. Note: Some features may require specific network configurations; detailed limitations not publicly documented—ask Sedai sales or support for specifics. Source
What level of control do we have over changes Sedai makes?
Sedai provides multiple modes of operation—Datapilot, Copilot, and Autopilot—allowing you to choose the level of autonomy and control over optimizations. You can review, approve, or automate changes based on your risk tolerance and governance requirements. Note: For organizations requiring strict change management, Sedai can be configured for manual approval workflows. Source
Does Sedai support FedRAMP-regulated environments?
Sedai is SOC 2 certified, demonstrating adherence to stringent security and compliance standards. However, detailed support for FedRAMP-regulated environments is not publicly documented; please contact Sedai sales or support for specifics regarding FedRAMP compatibility. Source
What technical documentation is available for deploying Sedai on Kubernetes?
Sedai provides comprehensive technical documentation, including getting started guides, Kubernetes optimization instructions, and deployment best practices. These resources are available at docs.sedai.io/get-started. Note: For advanced or custom deployments, consult Sedai support for tailored guidance. Source
Features & Capabilities
What are the key features of Sedai for self-managed Kubernetes optimization?
Sedai offers autonomous workload optimization, GPU optimization for AI/ML/HPC workloads, and continuous refinement of HPA and GitOps configurations. It supports agent-based or GitOps integration, works with 12+ APMs (including Prometheus, Datadog, AWS CloudWatch), and optimizes across AWS, Azure, GCP, and Kubernetes. Note: Some advanced integrations may require additional setup. Source
Does Sedai support GPU optimization for AI, ML, and HPC workloads?
Yes, Sedai identifies idle GPU capacity and right-sizes allocations to match real demand, reducing costs for AI, ML, and HPC workloads. This feature is especially valuable for organizations with high GPU spend. Note: GPU optimization requires proper integration with your Kubernetes environment. Source
What integrations does Sedai support for Kubernetes optimization?
Sedai integrates with 12+ APMs (including Prometheus, Datadog, AWS CloudWatch, Azure Monitor, 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. Note: Integration depth may vary by tool; review documentation for specifics. Source
Pricing & Plans
How is Sedai priced for self-managed Kubernetes optimization?
Sedai uses a resource-based pricing model, where costs are determined by the resources optimized and the value delivered. For Kubernetes environments, tailored pricing is available. All costs are transparently outlined on Sedai's pricing page, with no hidden fees. Note: For specific pricing details, contact Sedai sales or request a demo. Source
Is there a free trial or proof of value for Sedai?
Yes, Sedai offers a free Proof of Value and a 30-day free trial, allowing you to evaluate the platform's benefits before committing. Note: After the trial, standard resource-based pricing applies. Source
Use Cases & Business Impact
What business outcomes can I expect from using Sedai for Kubernetes optimization?
Customers using Sedai for Kubernetes optimization have achieved up to 50% reduction in cloud costs, up to 75% reduction in application latency, and up to 6X productivity gains for engineering teams. Sedai also reduces failed customer interactions by up to 70% through proactive issue resolution. Note: Actual results may vary based on environment and implementation. Source
Who can benefit from Sedai's self-managed Kubernetes optimization?
Sedai is designed for IT/cloud operations managers, SREs, platform engineers, FinOps leads, and technology leaders responsible for optimizing Kubernetes environments. It is suitable for organizations seeking to reduce cloud costs, improve performance, and automate operational tasks. Note: Teams with highly custom or legacy Kubernetes setups should confirm compatibility before deployment. Source
Are there real-world examples of Sedai's impact on Kubernetes optimization?
Yes, Sedai customers such as Palo Alto Networks, KnowBe4, and Belcorp have reported significant cost savings and performance improvements. For example, KnowBe4 achieved up to 50% cost savings and a 99.5% reduction in response time, while Palo Alto Networks saved $3.5 million through Sedai's optimization. See more case studies at sedai.io/resources. Note: Results are specific to each customer environment. Source
Security & Compliance
What security and compliance certifications does Sedai have?
Sedai is SOC 2 certified, demonstrating adherence to industry standards for data protection and compliance. For more details, visit Sedai's security page. Note: For additional certifications or compliance requirements, contact Sedai directly. Source
Implementation & Support
How long does it take to implement Sedai for self-managed Kubernetes?
Initial setup for Sedai can be completed in as little as 15 minutes using agentless or agent-based deployment. For advanced use cases like AI agent optimization, implementation typically takes two to three weeks. Note: Complex or highly customized environments may require additional time. Source
What support resources are available for Sedai customers?
Sedai provides personalized onboarding sessions, extensive documentation, and a community Slack channel for real-time assistance. Comprehensive technical guides are available at docs.sedai.io/get-started. Note: For enterprise support or custom SLAs, contact Sedai sales. Source
