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Artwork for The AI Kubernetes Show

The AI Kubernetes Show

The AI Kubernetes Show
Kubernetes
Artificial Intelligence
Open Source
Linkerd
Gateway API
Platform Engineering
Cloud Native Security
Guardrails
MCP
Schonfeld
Webassembly
Large Language Models
Bloomberg
Sustainability
Strategy
Security
Okteto
Fleet Of AI Agents
Replicated
Devops

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

PublishesTwice monthlyEpisodes26Founded3 months ago
Number of ListenersCategory
Technology

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Artwork for The AI Kubernetes Show

Latest Episodes

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 nat... more

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, Lensm... more

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

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, ... more

Learn platform-level security patterns for AI agents on Kubernetes. Close the production gap with LLM guardrails, tool filtering, and short-lived tokens.

Learn how Schonfeld scaled their internal AI platform, SchonAI, using Kubernetes and established guardrails to manage AI agent code volume. Build your AI-native workflow now.

Secure your Kubernetes environment. Learn why zero trust cybersecurity is the only defense against AI agents and non-deterministic agentic software in your supply chain.

The future of software development isn't about single agents—it's about building AI agent fleets! Dive into this conversation with Okteto CEO Ramiro Berrelleza to understand how this shift is fundamentally changing platform engineering and accelerati... more

Key Facts

Accepts Guests
Contact Information
Podcast Host
Number of Listeners
Find out how many people listen to this podcast per episode and each month.

Recent Guests

Ole Lensmar
CTO at Testkube
Testkube
Episode: Treat Testing as a Platform Service on Kubernetes
Abdel Siguar
Developer Advocate at Google
Google
Episode: LLM Inference on Kubernetes: New Primitives, Real Challenges
Blake Romano
Staff software engineer at Imagine Learning, focused on AI initiatives and platform engineering
Imagine Learning
Episode: Running Multi-agent AI on Kubernetes: Lessons from Imagine Learning
Evelyn Chu
Software engineer in IBM Research
IBM Research
Episode: AI Agents Security: Guardrails & Production on Kubernetes
Scott Feinberg
Lead AI and platform efforts at Schonfeld; former roles at Google, AWS, and other tech/finance organizations
Schonfeld
Episode: Platform Engineering & Kubernetes: Guardrails For AI Code
Mike Lieberman
Co-founder of Kusari, focused on cybersecurity and supply chain security
Kusari
Episode: One Dependency Away: Supply Chain Security in the Age of AI
Ramiro Berrelleza
CEO of Okteto
Okteto
Episode: Moving from Single Agents to AI Agent Fleets
Grant Miller
CEO of Replicated and creator of Enterprise Ready
Replicated
Episode: Why Testing and Validation are the Unsolved AI Code Challenges
Dinesh Majrekar
CTO of Civo
Civo
Episode: AI: Bubble or Bug? A CTO’s Perspective on Engineering in the AI Era

Host

William Chia
Host of The AI Kubernetes Show

Chart Rankings

How this podcast ranks in the Apple Podcasts, Spotify and YouTube charts.

Apple Podcasts
#83
Sweden/Technology

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Talking Points

Recent interactions between the hosts and their guests.

LLM Inference on Kubernetes: New Primitives, Real Challenges
Q: Why is inference important to optimize, and who is this talk aimed at?
Optimizing inference is crucial because not everyone has unlimited GPUs or cloud budget; open-weight models and optimized pipelines can make high-performance LLM deployment feasible for more teams. The talk targets practitioners who operate with limited resources and need practical strategies to get LLMs running efficiently on open-source stacks and managed Kubernetes environments.
LLM Inference on Kubernetes: New Primitives, Real Challenges
Q: Let's split into two topics: inference and agentic workloads. Could you describe the key differences and challenges for each?
Inference tends to be heavy on GPU usage, requires careful placement, caching, and fast image delivery; the goal is to optimize throughput and latency for large models. Agentic workloads, however, introduce memory management, tool access, non-determinism from LLM prompts, complex inter-service communication, and higher security and identity considerations due to dynamic tool use and external calls.
OpenSSF CTO on AI, Open Source Security, and the Human Element
Q: What made the patches viable and how did they ensure upstream adoption?
Top teams used a mix of LLMs and tools across providers, and the patches had to be tested in a realistic environment—then a liaison helped present these patches to upstream maintainers for potential merging.
OpenSSF CTO on AI, Open Source Security, and the Human Element
Q: Can you tell us a little about the AI cyber challenge and its relation to upstream maintainers?
The challenge partnered with DARPA to create cyber reasoning systems using AI to find and patch vulnerabilities, and it required patches to be submitted upstream, establishing a pathway from discovery to real-world integration.
Platform Engineering's AI and Observability Playbook
Q: How should organizations structure their teams to adopt AI in platform engineering?
Adopt platform engineering as a product mindset with self-service capabilities, clear baselines for observability, and governance. Provide a strong foundation of telemetry, simple access for developers, and ongoing education and advocacy so teams can safely consume AI/LLMs.

Audience Metrics

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Frequently Asked Questions About The AI Kubernetes Show

What is The AI Kubernetes Show about and what kind of topics does it cover?

This show focuses on real-world challenges at the intersection of AI and Kubernetes, with episodes exploring how AI-native workflows, fleet-based agent systems, and platform engineering practices are transforming software delivery. Topics frequently cover governance, security, observability, and organizational change when adopting AI tooling in cloud-native environments, often featuring CTOs, principal engineers, data scientists, and CNCF/open‑source leaders who share practical lessons, guardrails, and implementation patterns. A standout trait is the emphasis on enterprise applicability—on-prem and hybrid deployments, open-source tooling, and cross-team collaboration between platform, DevOps, and data science groups. The format tends to ble... more

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How many episodes of The AI Kubernetes Show are there?

The AI Kubernetes Show launched 3 months ago and published 26 episodes to date. You can find more information about this podcast including rankings, audience demographics and engagement in our podcast database.

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What guests have appeared on The AI Kubernetes Show?

Recent guests on The AI Kubernetes Show include:

1. Ole Lensmar
2. Abdel Siguar
3. Blake Romano
4. Evelyn Chu
5. Scott Feinberg
6. Mike Lieberman
7. Ramiro Berrelleza
8. Grant Miller

To view more recent guests and their details, simply upgrade your Rephonic account. You'll also get access to a typical guest profile to help you decide if the show is worth pitching.

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