KubeCon + CloudNativeCon India 2026

The cloud-native ecosystem is creating new opportunities for engineers across the world. The Kubestronaut program, built around advanced Kubernetes certifications, showcases how structured learning and technical excellence can help engineers gain industry recognition, deepen their expertise, and accelerate career growth.

This keynote explores how continuous learning, Kubernetes certifications, open-source participation, and hands-on platform engineering can help engineers build strong technical foundations and meaningful careers in cloud native technologies. From learning Kubernetes concepts to enabling others through mentorship and community engagement, the journey reflects the power of practical learning and consistent upskilling.

The session also highlights how community-driven programs, technical certifications, and real-world experience can help aspiring engineers gain confidence, deepen their expertise, and actively contribute to the growing Kubernetes ecosystem. As cloud-native adoption continues to expand, the next generation of engineers has an opportunity not only to build scalable systems but also to shape the future of the community itself.

Flipkart, one of India's leading digital commerce entities, runs hundreds of tightly coupled microservices that must withstand the massive traffic surges of Big Billion Days and festive sales. To stop treating outages as an afterthought, Flipkart's Central Reliability Engineering team built a centralized chaos engineering platform on top of Litmus Chaos. To make it work at their scale, they customized Litmus in four meaningful ways: a hybrid multi-tenancy architecture that splits the difference between cluster-wide and namespace-wide installs, a DaemonSet-based high-availability model for chaos injection, a first-class Script Runner fault for dynamic target selection and context chaining, and a hybrid VM chaos extension for workloads that don't run on Kubernetes. This talk shares what they built, why, and what they contributed back upstream.

Our session introduces a cloud-native, in-house platform for modular LLM inference at enterprise scale. Built on Kubernetes, the architecture unifies open-source and vendor models via OpenAI-compatible APIs and supports distributed serving with popular inference runtimes like vLLM, SGLang, and Triton.

Powered by NVIDIA Dynamo, the system optimizes GPU fleets through intelligent scheduling, KV-aware routing, prefix caching, and NIXL-based GPU-to-GPU data transfer, further optimized by allocating fractions of a GPU using KAI-Scheduler. The platform delivers streaming, speculative decoding, quantization, and autoscaling to zero via KEDA.

We ensure comprehensive observability with Prometheus, Grafana, and ELK, all governed by GitOps principles using ArgoCD and secured with enterprise-grade practices. For end-user consumption, the platform integrates with Open WebUI via standard APIs. We’ll cover the architecture, key components, and cloudability-driven cost governance strategies that empower data science teams while accelerating safe, sustainable AI innovation across AstraZeneca.
Our session introduces a cloud-native, in-house platform for modular LLM inference at enterprise scale. Built on Kubernetes, the architecture unifies open-source and vendor models via OpenAI-compatible APIs and supports distributed serving with popular inference runtimes like vLLM, SGLang, and Triton.

National Payments Corporation of India (NPCI) processes more than 24 billion monthly transactions and 700 million daily payments on one of the world’s largest real-time payment systems, all powered by open source, cloud native infrastructure. In this keynote, NPCI will share how they built their AI infrastructure using Kubernetes, Kubeflow, Prometheus, vLLM, OpenStack, NVIDIA AI platforms and many more open-source components to deliver real-time fraud detection in under sub second latency at national scale. The session will explore the architecture behind their payment rail including GPU-accelerated AI inference, distributed cloud infrastructure, and cloud-native tooling powering fraud and AI infrastructure, and financial AI models. Get a look inside their reference architecture and how cloud-native technologies are becoming critical infrastructure for AI at scale with sovereignty.

India moves on Rapido. The country's largest taxi platform serves over 4 million rides daily, matching riders to drivers in real time across 150+ microservices at 200K requests per second.

None of this would exist without the CNCF ecosystem. In this keynote, we walk through how Kubernetes, Istio, Prometheus, Thanos, Cert Manager, and External DNS work together to power a platform where every second of latency means a rider waiting in the heat.

This is the story of what cloud native makes possible when the stakes involve millions of livelihoods, not just uptime metrics.

Two years ago, every platform team was building developer platforms. Today the same teams, and the AI Cloud providers selling to them, face a harder question. How do you safely share GPU infrastructure across multiple AI teams without stepping on each other?

Kubernetes is already the production platform for AI inference. Containers, autoscaling, multi-tenancy, RBAC are all solved. The remaining problem is GPUs themselves. Today Kubernetes asks for them in whole numbers. One pod, one GPU, even when the workload uses 10% of it.

This keynote walks through how the CNCF ecosystem, and Kubernetes itself, has answered. HAMi virtualizes one physical GPU into multiple slices today, each with its own memory budget. DRA evolves the platform's resource model so Kubernetes finally understands GPUs as rich devices instead of opaque numbers.

Then, we move to a live demo. There will be a MacBook on stage, connected directly to an NVIDIA DGX Spark. A single Blackwell GPU will run two open source LLMs for two teams, generating answers simultaneously. There are no slides and no recordings.

That's the AI factory. And yes, Kubernetes has solved it, with a little help from its friends.