The AI Kubernetes Show

The Kubernetes AI Show dives deep into the real-world challenges of adopting AI on Kubernetes platforms.

The AI Kubernetes Show is a technology podcast hosted by Unknown Host, with 27 episodes on record and a Required Pod Score of 80. PitchCentric scores this show on Booking Probability, Listen Score, and live audience signals refreshed every 24 hours.

Unknown Host hosts The AI Kubernetes Show, a technology show with 27 episodes published.

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  Episode #8

  Disaggregated Inference on Kubernetes

  Aug 12, 202657 minS2

  Every LLM inference request splits into two phases with completely different hardware profiles: pre-fill and decode. But most Kubernetes clusters still run them on the same pod. Morgan Foster, who works on the llm-d project and the AI Gateway Working Group out of Red Hat's Office of the CTO, joins William Morgan to explain what that means. Foster and Morgan get into why real-time inference workloads fit the primitives Kubernetes already has, while training often runs on Slurm instead. They walk through disaggregated inference: how pre-fill and decode get scheduled on separate pods, how KV cache blocks move between them over RDMA, and what happens today when a pre-fill or decode pod dies mid-request (nothing handles the failover cleanly, and the computed KV blocks just get thrown away). They also cover why faster token generation buys agentic systems more capability, not just quicker responses, and why Kubernetes proxies, built around header-based ingress routing, are struggling to govern AI traffic that depends on streamed JSON request bodies and inference happening outside the cluster. FIND AND FOLLOW US ON: ✦ Spotify: https://open.spotify.com/show/4LLTNjMbd7wNCYhSI9pkQU?nd=1&dlsi=3baba6bcc6b44cae ✦ Apple Music: https://podcasts.apple.com/us/podcast/the-ai-kubernetes-show/id1895650894 TAKEAWAYS ✓ Why real-time inference workloads fit Kubernetes' existing primitives, while training often runs on Slurm instead ✓ How disaggregated inference splits the pre-fill and decode phases of an LLM request across separate pods ✓ Why KV cache blocks move between pods over RDMA, and what happens if the pre-fill or decode pod dies mid-transfer ✓ Why more tokens per second buys agentic systems more capability, not just faster responses ✓ Why Kubernetes proxies built for header-based routing struggle with AI traffic that depends on streamed request bodies ✓ What the AI Gateway Working Group is building to fix it Read the blog post at: https://www.buoyant.io/ai-kubernetes-episode/disaggregated-inference-on-kubernetes

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  Episode #7

  The State of Cloud Native AI and What’s Still Evolving

  Jul 29, 202648 minS2

  There is no agreed upon definition of what cloud native AI means yet, but if you're trying to figure out where Kubernetes AI standards are really headed, check out this episode with someone who's worked on the CNCF's first attempt to define cloud native AI. William Morgan talks with Ron Petty (RXM, CNCF founding contributor, and member of the CNCF's TCG AI) about what's actually settled in cloud native AI and what isn't. They cover the CNCF's shift from working groups to technical community groups (TCGs), Petty's own framing of cloud native AI as a two-way street between building models and using AI to run cloud native systems, and the 15-criteria self-reported conformance program (mostly GPU management) that gets you an "AI certified" stamp. TAKEAWAYS ✓ Why Ron Petty defines cloud native AI as a two-way street: building models on cloud native infrastructure, and using AI to run cloud native systems ✓ What the CNCF's self-reported AI conformance program actually checks, including GPU management and having an AI router on your cluster ✓ Why certificate management and rotation is still unsolved, and how that connects to why Linkerd defaults to mutual TLS ✓ What's actually wrong with Model Context Protocol's design, according to a proxy engineer's read on it ✓ Why long-running AI agents don't need fundamentally different guardrails than short-running ones ✓ How K8sGPT uses existing Kubernetes checks plus an LLM to help junior engineers diagnose cluster issues faster Read the blog post at: www.buoyant.io/ai-kubernetes-episode/the-state-of-cloud-native-ai-and-whats-still-evolving

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  Episode #6

  Treat Testing as a Platform Service on Kubernetes

  Jul 15, 202648 minS2

  Most testing tools bolt onto a CI pipeline and hope for the best. Ole Lensmar, CTO of Testkube, built a company that changes that script. Pull test execution out of CI/CD entirely and run tests as Kubernetes workloads instead. In this episode, Lensmar walks through why Testkube uses Kubernetes as its test execution engine, running Selenium, Playwright, K6, JMeter, and Postman tests as cluster workloads instead of asking teams to adopt new tools. He and podcast host William Morgan dig into who should own testing (developers own functional tests, while the platform team owns performance, security, and chaos testing). Lensmar's core recommendation is to treat testing and quality as a platform capability, the same way you already treat CI and CD. More AI-generated code means more tests are needed, intelligent test selection can keep pipelines fast as test counts grow, and AI is already useful for triaging failed test logs using Kubernetes and Grafana MCP servers. TAKEAWAYS: ✓ Where testing ownership splits between developers and the platform team, and where it doesn't ✓ Why "testing as a platform service" should get the same priority as CI and CD ✓ How intelligent test selection works, and why you should never rely on it alone ✓ Where AI already adds value today: triaging failed test logs with Kubernetes and Grafana MCP servers ✓ Why testing the AI components of your application (agents, evals) is becoming its own discipline. Read the blog post at:

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  Episode #5

  LLM Inference on Kubernetes: New Primitives, Real Challenges

  Jul 1, 202644 minS2

  Running LLM inference on Kubernetes requires new primitives for routing, autoscaling, and GPU scheduling. Here's what platform engineers need to know.

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  Episode #4

  Running Multi-agent AI on Kubernetes: Lessons from Imagine Learning

  Jun 17, 202648 minS2

  In this episode of The AI Kubernetes Show, Blake Romano, Staff Software Engineer at Imagine Learning, walks through what it actually looks like to build and run AI agents on Kubernetes at scale. He talks about the architecture choices, the failures, and why the organizational context you bring to the LLM matters more than which Software Development Kit (SDK) you use. Imagine Learning is a K-12 education company building digital platforms for students and educators, and Blake has been driving AI and platform engineering initiatives there.

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