What is Sedai's Kubernetes Autoscaler Intelligence Layer?

Sedai's Kubernetes Autoscaler Intelligence Layer is an autonomous optimization platform that continuously right-sizes pod requests and tunes scaling targets based on real workload behavior. It works alongside native autoscalers like HPA, VPA, and Karpenter, ensuring that scaling decisions are based on accurate, up-to-date inputs rather than static estimates. This approach helps teams achieve more efficient resource utilization and lower cloud costs. Note: Sedai's optimizations are designed to be safe and gradual, but teams with highly custom autoscaler logic may require additional validation.

How does Sedai improve Kubernetes autoscaling compared to native tools like HPA, VPA, and Karpenter?

Sedai addresses key limitations of native Kubernetes autoscalers by continuously analyzing live workload behavior and SLOs to set optimal HPA targets, right-size pod requests, and recommend cost-effective instance types for node provisioning. Unlike HPA and VPA, which can conflict and are often disabled in production, Sedai's vertical scaling works safely alongside HPA. For Karpenter, Sedai ensures pods are right-sized before bin-packing, reducing node count and spend. Note: Sedai does not replace native autoscalers but enhances their effectiveness; teams must still configure base autoscaler policies.

What are the key features of Sedai for Kubernetes autoscaling?

Sedai provides: Continuous right-sizing of pod requests and limits Automatic tuning of HPA targets based on live workload behavior and SLOs Vertical scaling that works safely alongside HPA (conflict-free) Pod right-sizing for efficient Karpenter bin-packing and lower node costs Instance type recommendations for node autoscalers Application-aware scaling that factors in performance and SLOs, not just infrastructure metrics Note: Sedai's advanced features may require integration with supported monitoring and cloud platforms.

Which Kubernetes autoscalers and platforms does Sedai support?

Sedai integrates with Horizontal Pod Autoscaler (HPA), Vertical Pod Autoscaler (VPA), Karpenter, Cluster Autoscaler, and KEDA. It supports Kubernetes environments on AWS (EKS), Azure (AKS), GCP (GKE), and more. Note: Some advanced features may require specific platform versions or configurations.

What measurable business outcomes can Sedai deliver for Kubernetes environments?

Sedai can deliver up to 50% reduction in cloud costs by rightsizing workloads and eliminating cloud waste, up to 75% reduction in application latency, and up to 6X productivity gains for engineering teams by automating repetitive scaling and optimization tasks. These outcomes are based on real customer deployments, such as Palo Alto Networks saving $3.5 million and KnowBe4 achieving a 99.5% reduction in Lambda response time. Note: Actual results may vary depending on workload and environment.

How does Sedai ensure safe and reliable optimizations in production Kubernetes clusters?

Sedai's patented safety-by-design approach includes continuous health verification, automatic rollbacks, and incremental changes. Every optimization is validated before, during, and after execution to prevent incidents or SLO breaches. This allows Sedai to operate autonomously in production environments without compromising reliability. Note: Teams with highly custom or regulated environments should review Sedai's safety documentation before enabling full autonomy.

How long does it take to implement Sedai for Kubernetes autoscaler optimization?

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: Integration with existing monitoring, CI/CD, and ITSM tools may require additional configuration.

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. It supports AWS, Azure, and GCP environments. Note: Some integrations may require additional setup or permissions.

Where can I find technical documentation for Sedai's Kubernetes optimization?

Comprehensive technical documentation, including getting started guides, Kubernetes optimization instructions, and integration details, is available at https://docs.sedai.io/get-started. Note: Some advanced topics may require contacting Sedai support for guidance.

How is Sedai priced for Kubernetes autoscaler 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 and can be reviewed on the Kubernetes optimization page. Discounts from cloud billing accounts (e.g., Reserved Instances, Savings Plans) are factored into cost and savings calculations. Note: For a detailed quote, contact Sedai sales.

Does Sedai offer a free trial or proof of value for Kubernetes optimization?

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: Some advanced features may require a paid plan after the trial period.

What security and compliance certifications does Sedai have?

Sedai is SOC 2 certified, demonstrating adherence to stringent security and data protection standards. This certification ensures compliance with industry requirements for cloud operations. For more details, visit the Sedai Security page. Note: For additional compliance needs, contact Sedai's security team.

What are some real-world success stories using Sedai for Kubernetes optimization?

Palo Alto Networks saved $3.5 million through Sedai's autonomous Kubernetes optimization, as detailed in their case study. KnowBe4 achieved up to 50% cost savings and a 99.5% reduction in Lambda response time. Belcorp reduced AWS Lambda latency by 77%, and Campspot achieved a 34% reduction in latency. Note: Results are specific to each customer environment.

Which industries have benefited from Sedai's Kubernetes optimization?

Industries represented in Sedai's case studies include cybersecurity (Palo Alto Networks), security awareness training (KnowBe4), beauty and personal care (Belcorp), travel and hospitality (Campspot), background check services (Inflection), and customer engagement software (Freshworks). Note: Industry-specific requirements may affect implementation details.

Are there any limitations or scenarios where Sedai may not be the best fit?

Detailed limitations are not publicly documented; teams with highly custom autoscaler logic, strict regulatory requirements, or unique infrastructure may require additional validation and should consult Sedai's sales or support team for specifics. Sedai's safety-by-design approach minimizes risk, but not all environments are supported out-of-the-box.

The Intelligence Layer Your Autoscalers Are Missing

HPA scales pods, Karpenter provisions nodes, but neither knows whether your workloads are actually sized correctly. Sedai continuously right-sizes pod requests and tunes scaling targets based on real workload behavior, so your autoscalers are always working from accurate inputs.

Autoscalers Scale Load. They Don't Fix Sizing.

Autoscalers like HPA and Karpenter are reactive by design. The inputs they scale from are usually wrong before scaling even begins.

Resource requests are set once and inherited blindly.

Estimates go stale, but HPA still scales from whatever they say.

VPA fights with HPA in live clusters.

Vertical and horizontal autoscaling conflict at runtime, so most teams disable VPA in production or limit it to off-hours, leaving right-sizing unaddressed.

Node provisioning inherits pod-level inefficiency.

Karpenter bins pods onto nodes based on declared requests, so inflated requests mean larger nodes, lower density, and higher spend regardless of actual utilization.

Key Capabilities

Sedai adds an intelligence layer at both the pod and node level, filling the gaps that leave native autoscalers guessing.

Set the Right HPA Target, Not Just a Guess

Sedai analyzes workload behavior and SLOs to automatically set the optimal HPA target, balancing pod size against replica count for the most cost-effective configuration.

Get Vertical and Horizontal Scaling at the Same Time

Kubernetes blocks HPA and VPA from running together. Sedai's vertical scaling works alongside HPA, safely adjusting pod requests and limits in small increments — conflict-free.

Help Karpenter Pack Nodes More Efficiently

Oversized pods waste node capacity. Sedai right-sizes pods first so Karpenter bin-packs onto fewer, cheaper nodes, and recommends instance types based on usage and pricing.

“We’ve gone from what used to be automated, deterministic workflows to autonomous, with Sedai. The human element is indispensable, and it always will be. But more and more engineering toil is being done by AI, so as humans, we can move up the value chain. That’s how we can deliver what our customers expect of us.”

Suresh Sangiah

SVP of Engineering // Palo Alto Networks

Works With Your Stack

Horizontal Pod Autoscaler (HPA)

Vertical Pod Autoscaler (VPA)

Karpenter

Cluster Autoscaler

KEDA

The Sedai Difference

Without Sedai

HPA utilization targets are set manually and rarely revisited
Kubernetes prevents using HPA and VPA on the same workload
Oversized pods force Karpenter to provision larger, more expensive nodes
Node autoscalers pick from the full instance catalog without guidance
Scaling decisions are based on infrastructure metrics alone

Sedai continuously determines and tunes the optimal HPA target based on live workload behavior
Sedai's vertical scaling works alongside HPA simultaneously, without the native conflict
Right-sized pods let Karpenter pack workloads onto fewer nodes at lower cost
Sedai recommends a targeted instance type list so autoscalers make more cost-effective selections
Sedai factors in application performance and SLOs, ensuring scaling doesn't compromise reliability

Resources

Sedai Kubernetes Optimization

See how Sedai autonomously optimizes K8s — across EKS, AKS, GKE, and more.

How We Solved the GPU Problem for Kubernetes

We solved GPU optimization. In this episode of 1 IDEA, Suresh Mathew sits down with Pooja Malik, Distinguished Engineer at Sedai, to talk through how Sedai's engineering team built GPU optimization from scratch, why the standard metrics fail, and what's still unsolved.

Watch video

How Palo Alto Networks Takes Control of Its High-Stakes Cloud

Learn how Palo Alto Networks dramatically reduced its cloud costs with Sedai

How Palo Alto Netoworks Saved $3.5M with Sedai

How Palo Alto Networks Saved $3.5M with Sedai's AI Agent

See how Sedai's AI agent saved Palo Alto Networks $3.5M in Kubernetes optimization autonomously & safely.