Frequently Asked Questions
Product Information & Azure AKS Optimization
What is Sedai's Azure AKS Optimization and how does it work?
Sedai's Azure AKS Optimization is an autonomous cloud platform feature that continuously rightsizes pods and containers in AKS clusters, then optimizes node type, count, and segregation to match workload needs. It analyzes real-time CPU and memory data, makes small, validated changes to pod scaling, and recommends better-matched instance types, node pool resizing, and workload segregation. This approach eliminates waste from oversized or underutilized resources and attributes costs back to responsible teams. Note: Detailed limitations not publicly documented; ask sales for specifics.
Does Sedai optimize both node pools and pods in Azure AKS?
Yes, Sedai optimizes both pods and the underlying node pools in Azure AKS. It continuously rightsizes pods and containers, then recommends and executes optimizations for node type, count, and segregation, ensuring that infrastructure matches actual workload requirements. Note: For highly specialized or custom AKS configurations, consult Sedai support for compatibility details.
How does Sedai avoid causing performance issues while making changes in AKS?
Sedai uses a safety-by-design approach for all optimizations. It performs continuous health verification before, during, and after every action, makes incremental changes rather than all-at-once adjustments, and supports automatic rollbacks if risk is detected. This minimizes the risk of outages or SLO breaches during optimization. Note: For mission-critical workloads, teams should review Sedai's safety documentation and test in staging environments before full production rollout.
Can Sedai distinguish between latency-sensitive services and batch jobs in AKS?
Yes, Sedai's application-aware intelligence can differentiate between latency-sensitive services and batch jobs. It tunes optimization trade-offs based on workload type, ensuring that latency-sensitive workloads are prioritized for performance, while batch jobs can be optimized for cost. Note: For workloads with highly custom requirements, manual review of optimization recommendations is advised.
How does Sedai help with cost attribution in shared AKS clusters?
Sedai provides workload-level cost attribution across compute, storage, GPU, and network usage in shared AKS clusters. This enables organizations to trace costs back to the responsible team or business unit, improving accountability and financial transparency. Note: For organizations with complex chargeback models, integration with existing financial systems may require additional configuration.
Does Sedai work alongside Azure's Cluster Autoscaler?
Sedai supports integration with Kubernetes autoscalers, including HPA/VPA and Karpenter. While it can operate alongside Azure's Cluster Autoscaler, Sedai's autonomous optimization provides additional capabilities such as application-aware tuning, workload-level cost attribution, and safety-by-design features. Note: For environments with custom autoscaler configurations, consult Sedai documentation for compatibility details.
Features & Capabilities
What features does Sedai offer for Azure AKS optimization?
Sedai offers autonomous workload optimization, node and cluster optimization, workload-level cost attribution, application-aware tuning, and integration with monitoring, ITSM, and CI/CD tools. It supports continuous health verification, automatic rollbacks, and incremental changes for safe optimization. Note: Some advanced features may require additional configuration or integration; see Sedai's technical documentation for details.
What integrations are available for Sedai's Azure AKS optimization?
Sedai integrates with 12 APMs (including Prometheus, Datadog, AWS CloudWatch, Azure Monitor, and Google Cloud Monitoring), supports HPA/VPA and Karpenter for Kubernetes, and works with GitHub, GitLab, Bitbucket, Terraform, ServiceNow, PagerDuty, Jira, and notification tools. It also supports runbook automation and optimizes resources across AWS, Azure, and GCP. Note: Integration availability may vary by environment; check Sedai's documentation for the latest supported integrations.
Pricing & Plans
How is Sedai's Azure AKS optimization priced?
Sedai uses a resource-based pricing model, where costs are determined by the resources optimized and the value delivered. For Kubernetes environments like Azure AKS, tailored pricing is available. All costs are transparently outlined on Sedai's pricing page, and discounts from cloud billing accounts (e.g., Reserved Instances, Savings Plans) are factored into cost and savings calculations. Note: For detailed pricing or custom requirements, contact Sedai sales or request a demo.
Implementation & Support
How long does it take to implement Sedai for Azure AKS, and how easy is it to get started?
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. Personalized onboarding, extensive documentation, and a community Slack channel are available for support. Note: Implementation time may vary for complex or highly customized AKS environments.
What technical documentation and support resources are available for Sedai's Azure AKS optimization?
Sedai provides comprehensive onboarding guides, Kubernetes optimization documentation, and support for Databricks and GPU optimization. Access technical resources at docs.sedai.io/get-started. Personalized onboarding and community support are also available. Note: Some advanced topics may require direct engagement with Sedai's support team.
Performance & Business Impact
What measurable business impact can Sedai deliver for Azure AKS users?
Sedai can deliver up to 50% reduction in cloud costs by rightsizing workloads and eliminating cloud waste, up to 75% reduction in latency for improved application performance, and up to 6X productivity gains for engineering teams by automating repetitive tasks. It also reduces failed customer interactions by up to 70% through proactive issue resolution. Note: Actual results may vary based on workload characteristics and implementation scope.
Are there real-world examples of Sedai's impact on Azure AKS or similar environments?
Yes. For example, KnowBe4 achieved up to 50% cost savings and a 99.5% reduction in response time using Sedai with AWS Lambda. Palo Alto Networks saved $3.5 million through Sedai's optimization. While these case studies are not specific to Azure AKS, they demonstrate Sedai's ability to deliver significant cost and performance improvements in cloud environments. See more at sedai.io/resources. Note: For Azure AKS-specific outcomes, contact Sedai for references.
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 Sedai's Security page. Note: For additional certifications or compliance requirements, contact Sedai's compliance team.
Limitations & Best Fit
What are the limitations of Sedai's Azure AKS optimization?
Detailed limitations are not publicly documented. For highly specialized AKS environments, custom autoscaler configurations, or advanced chargeback models, consult Sedai's sales or support team for specifics. Best fit for organizations seeking autonomous, safe, and application-aware optimization of Azure AKS clusters; teams with unique requirements may need to validate compatibility before deployment.
