Frequently Asked Questions

Product Information: Kubernetes Pod Interceptor & Smart Scheduler

What is Sedai's Kubernetes Pod Interceptor and how does it optimize workloads?

Sedai's Kubernetes Pod Interceptor is a new workload optimization execution model that right-sizes pods as they are spawned from deployment specs, avoiding conflicts with CI/CD pipelines. Instead of editing the deployment spec—which CI/CD often reverts—Sedai intercepts the pod at creation and applies optimization downstream. This prevents drift alerts and ensures optimizations are implemented without manual intervention. Note: Teams that require strict IaC-to-live-pod fidelity may prefer Sedai's legacy execution model, which remains available. Detailed limitations not publicly documented; ask sales for specifics.

How does Sedai's Smart Scheduler improve node utilization in Kubernetes?

Smart Scheduler works alongside the native Kubernetes scheduler to enable workload-aware placement and continuous strategic descheduling. It packs compatible workloads densely based on their resource profiles (CPU-bound, memory-bound, general-purpose, or sensitive), and automatically consolidates stranded pods to free up nodes for reclamation. This process is continuous and does not require manual intervention, translating workload savings into fewer nodes and lower cloud bills. Note: Smart Scheduler runs incrementally and safely; if issues arise, workloads revert to default Kubernetes scheduling. Best fit for teams seeking automated node consolidation; teams needing full control over scheduling may want to consider alternatives.

Can Sedai's Pod Interceptor and Smart Scheduler be used together?

Yes, Pod Interceptor and Smart Scheduler are designed to work together. Pod Interceptor applies optimizations without triggering CI/CD pipeline conflicts, while Smart Scheduler consolidates the resulting savings onto fewer nodes automatically. Used together, Sedai can identify optimizations, apply them safely, and realize cost savings without manual steps or approval queues. Note: Adoption can be incremental; teams can opt specific namespaces, workloads, or node pools in or out. Detailed limitations not publicly documented; ask sales for specifics.

What options are available for teams that prefer IaC-driven environments?

Sedai offers both the Pod Interceptor model and the legacy execution model. Teams that require live pods to always mirror IaC definitions can use the legacy model, supported by Sedai's IaC loopback feature. Guardrails as Code are available in both models to govern the scope of changes. Note: Pod Interceptor is the default for new customers, but legacy options remain for IaC purists. Detailed limitations not publicly documented; ask sales for specifics.

Features & Capabilities

What are the key features of Sedai's autonomous cloud optimization platform?

Sedai's platform includes autonomous cloud management for Kubernetes and serverless workloads, application-aware intelligence, safety-by-design (continuous health verification, automatic rollbacks, incremental changes), release intelligence, and full-stack cloud coverage across AWS, Azure, GCP, and Kubernetes. Modes of operation include Datapilot, Copilot, and Autopilot, allowing organizations to adopt autonomy at their own pace. Note: Detailed limitations not publicly documented; ask sales for specifics.

What integrations does Sedai support?

Sedai integrates with 12 APMs (including Prometheus, Datadog, AWS CloudWatch, Azure Monitor, Google Cloud Monitoring), Kubernetes autoscalers (HPA/VPA, Karpenter), CI/CD tools (GitHub, GitLab, Bitbucket, Terraform), ITSM tools (ServiceNow, PagerDuty, Jira), notification platforms, runbook automation, and cloud providers (AWS, Azure, GCP). Note: Integration coverage may vary by environment; check documentation for specifics.

Implementation & Ease of Use

How long does it take to implement Sedai's optimization solutions?

Initial setup for Sedai can be completed in as little as 15 minutes using agentless or agent-based deployment. AI Agent Optimization typically takes two to three weeks. For Databricks environments, setup can be completed in under 15 minutes. Note: Implementation time may vary based on environment complexity and integration requirements.

What feedback have customers provided about Sedai's ease of use?

Customers report quick onboarding (as little as 15 minutes), seamless integration with existing tools and workflows, minimal management burden due to autonomous operation, and simple, volume-based pricing. Comprehensive support includes personalized onboarding, extensive documentation, and a community Slack channel. Note: Ease of use may vary based on organizational processes and integration needs.

Pricing & Plans

How is Sedai's pricing determined?

Sedai uses resource-based pricing, determined by the resources optimized and the value delivered. Kubernetes-specific pricing is available, and all costs are transparently outlined on Sedai's pricing page. Discounts from cloud billing accounts (e.g., Reserved Instances, Savings Plans) are integrated into cost and savings calculations. Note: For detailed pricing, contact Sedai's sales team or request a demo.

Business Impact & Performance

What measurable business impact can Sedai deliver?

Sedai delivers up to 50% reduction in cloud costs, up to 75% reduction in application latency, up to 70% reduction in failed customer interactions, and up to 6X productivity gains for engineering teams. It proactively resolves issues before they impact users and tracks the impact of software deployments on cost, performance, and system risk. Note: Actual results may vary based on environment and usage; teams with highly custom workloads may require additional tuning.

Security & Compliance

What security and compliance certifications does Sedai hold?

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: Additional certifications may be available; contact Sedai for specifics.

Use Cases & Customer Success

Who can benefit from Sedai's optimization solutions?

Sedai is designed for IT/cloud operations managers, FinOps leads, technology leaders (CTO, CIO, VP Engineering), SREs, platform engineers, and organizations focused on infrastructure availability, cost efficiency, developer velocity, and operational productivity. Note: Teams with highly specialized requirements may need custom integration; ask sales for specifics.

Can you share specific customer success stories using Sedai?

KnowBe4 achieved up to 50% cost savings and reduced response time from 18.5 seconds to 80 milliseconds (99.5% duration reduction) with AWS Lambda. Palo Alto Networks saved $3.5 million through Sedai's optimization. Belcorp reduced AWS Lambda latency by 77%. Campspot achieved a 34% reduction in AWS Lambda latency. Inflection improved platform performance and reduced cold start latency. Freshworks optimized AWS Lambda for better user experience. For more details, visit Sedai's resources page. Note: Results are specific to each customer environment; not all outcomes are guaranteed.

Technical Documentation & Support

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

Comprehensive onboarding resources and detailed guides for Kubernetes optimization are available at docs.sedai.io/get-started. Documentation covers setup, optimization strategies, and integration with Sedai's platform. Note: Documentation is updated regularly; contact support for the latest resources.

Autonomous FinOps: The Ultimate Live Webinar

Sign Up
Sedai Logo

Introducing Kubernetes Pod Interceptor: Optimize Without Fighting Your Pipeline

Ethan Andyshak Headshot

Ethan Andyshak

VP of Product

September 10, 2026

Introducing Kubernetes Pod Interceptor: Optimize Without Fighting Your Pipeline

Featured

Sedai has released Pod Interceptor and Smart Scheduler, two changes to how Sedai executes and schedules on Kubernetes, so the optimization you've already identified actually lands.


Every platform team knows the challenge: you have a savings target, a dashboard full of recommendations, and a long list of reasons why none of it gets implemented this quarter. CI/CD guardrails fight automated changes. Manual optimization steps get skipped. Nodes stay provisioned long after the work they were sized for has moved on.

Today we're introducing two changes to how Sedai carries out optimization on Kubernetes: the Pod Interceptor – a new workload optimization execution model – and the Smart Scheduler, a new scheduling layer.

The problem: your own tooling is in the way

Getting a workload from an idea to a running application in Kubernetes takes a few steps: it starts as a definition in your infrastructure-as-code (IaC), gets realized as a deployment spec in Kubernetes, and finally spawns as a live pod on your cluster. Your CI/CD pipeline's job is to make sure the first two stay in sync, so when Sedai (or anything else) reaches in and edits the deployment spec directly to right-size a workload, CI/CD sees drift and reverts it. Optimization and GitOps end up in a tug-of-war.

Previously, the way we helped you avoid that fight was Sedai Sync — detecting when CI/CD reverted an optimization and reapplying it directly to the live environment. It works, but it's one more moving part, and it doesn't remove the underlying tension.

There's a second, quieter cost too: even when optimization isn't fighting your pipeline, it's still often manual. Sedai's existing Cluster Compaction feature can consolidate workloads onto fewer nodes, but it requires someone to log in and click "run.” Unsurprisingly, that's a step most teams don't get around to.

Pod Interceptor: Optimization Without the Drift Fight

The Pod Interceptor changes where Sedai acts. Instead of editing the deployment spec — the step your CI/CD pipeline is actively watching — Sedai intercepts the pod as it's spawned from that spec, and right-sizes it before it becomes a running instance. Your CI/CD pipeline still sees your IaC and your deployment spec in agreement. No conflict, no revert, no drift alert. The optimization simply happens a layer downstream, where nothing is watching for it.

It's a deliberate trade-off. Some teams are IaC purists who want the live pod to always mirror what's written in code. For them, our existing model, along with our IaC loopback feature, still applies. Other teams care less about that literal match and more about avoiding pipeline conflicts. For them, this is exactly the "good hygiene" they're after. 

A few things worth calling out:

  • Pod Interceptor will be the default for new customers, with existing customers rolling out now. Our original execution model remains available as an option for anyone who prefers it.
  • Guardrails as Code is still available to govern every change, in both the legacy and Pod Interceptor models — it's what limits how far Sedai is allowed to go.
  • Sedai Sync steps back to a supporting role for Kubernetes workloads under Pod Interceptor. With drift no longer the default case, it runs quietly in the background as a safety net for edge cases (like a brief window during pod rescheduling) and remains the primary mechanism for the legacy execution model and non-Kubernetes workloads.

Pod Interceptor is an escape hatch: a way to run autonomous optimization without an overhaul of your CI/CD configuration. Combined with the legacy model, it also means Sedai offers the broadest set of execution options in the category — where most competitors give you one way to run, we give you two.

Deployment spec update modelPod Interceptor

Smart Scheduler: turning savings into fewer nodes, automatically

Right-sizing a workload only becomes a lower cloud bill once it translates into fewer nodes. That's historically been a manual step, and it's what Smart Scheduler is built to close, continuously and without anyone clicking a button.

Smart Scheduler works alongside the native Kubernetes scheduler, and does two things:

  1. Workload-aware placement. The default Kubernetes scheduler spreads pods across nodes to load-balance, without any sense of which workloads belong together. Smart Scheduler adds that awareness: it looks at whether an application is CPU-bound, memory-bound, general-purpose, or "sensitive" (fragile, low-replica workloads that need extra care), and routes it to nodes accordingly, packing compatible workloads densely rather than spreading them thin.
  2. Continuous strategic descheduling. Even well-placed pods can end up stranded — the last workload keeping an otherwise-emptied node alive. Smart Scheduler identifies these cases and consolidates them elsewhere, freeing the node to be reclaimed.

The combination means nodes get used more efficiently from the moment pods are created, and get cleaned up automatically as workloads change, all running continuously, with no manual "compact now" step and no approval queue.

It's a similar goal to node auto-provisioners like Karpenter, but without the migration lift: no re-platforming required, and it works across cloud providers where Karpenter-style consolidation is harder to adopt (notably Azure and GCP, versus AWS).

Because Smart Scheduler runs alongside the default scheduler rather than replacing it, adoption is incremental and safe: you can opt specific namespaces, workloads, or node pools in or out, and if Smart Scheduler ever has an issue, workloads simply fall back to default Kubernetes scheduling behavior — the same behavior you're running today.

Smart Scheduler

Built to work together

Pod Interceptor and Smart Scheduler solve different problems. One is about how Sedai makes changes without fighting your pipeline, the other is about what happens to workload savings once they're made. Used together, they mean Sedai can identify an optimization, apply it without triggering a GitOps revert, and consolidate the resulting savings onto fewer nodes, all without a human in the loop.

If you want to go deeper on how Pod Interceptor interacts with your existing IaC workflows, our IaC integration resources are being updated to reflect the new model, reach out to your Sedai team for the latest.

Learn More

Have questions about how Pod Interceptor or Smart Scheduler would work in your environment? We're happy to walk through how it works for your clusters.

Kubernetes Pod Interceptor