KubeCon + CloudNativeCon India 2025

Do you know about Large Language Models or LLMs? It’s been a while since we’ve seen a technology like this grab everyone’s attention, techies and non-techies alike. Ever since OpenAI’s ChatGPT burst onto the scene, powered by an LLM, AI has become a household name. But for newcomers, especially in the cloud native ecosystem, these concepts can feel overwhelming.
In this talk, we’ll start from the very basics, explaining what LLMs are and busting some common myths. We’ll break down the key terms with simple diagrams and analogies that are easy to digest. You’ll get a clear, beginner-friendly look at how LLMs work, why Kubernetes is perfect for running them, and how they fit together in the cloud-native world.
We’ll even walk through running an LLM on Kubernetes using vLLM for some practical insights and touch on how this opens the door to AIOps. If you’re curious about stepping into the AI ecosystem and understanding its cloud foundations, this talk is for you.

Running a homelab means running a diverse set of applications like NextCloud, readeck.org, usememos.com.

They are written in many languages and frameworks and in general lack a good way to monitor them. The old way to monitor them involved having a basic blackbox_exporter enabled synthetic monitoring. Even instrumented, each application has a different set of metrics, leading to complex alerting and dashboards.

Grafana Beyla, powered by eBPF and OpenTelemetry, combined with Prometheus promises to generate first class observability signals for services regardless of the language and frameworks used. We will walk through deploying Beyla for a homelab, and the challenges in having good observability even with Beyla.

We will walk through:

1. Deploying Beyla in your environment with Prometheus
2. Setting up dashboards for the data
3. Robust alerting over the signals, especially in low, sporadic traffic (homelab) scenarios.
4. The missing signals from eBPF and how you can fill those gaps

Kubernetes Operators are revolutionasing cloud-native application management—but when designing an operator, should we go one CRD per operator or manage multiple CRDs within a single operator? How does cascading CRDs impact reconciliation, dependency handling, and performance?

In this talk, we'll dive deep into real-world use cases to explore the trade-offs between these approaches. Whether we are building custom Kubernetes controllers or optimising cloud-native infrastructure, this session will help one make the right design choice for scalability, maintainability, and performance.