KubeCon India 2026: Sovereign AI On A Cloud-Native Stack

KubeCon + CloudNativeCon India 2026 in Mumbai ran on one throughline: sovereign AI as an architectural need rather than a slogan, with population-scale platforms like Sarvam and NPCI running on the same cloud-native tools as commercial ones, and a recurring CNCF-native stack of Prometheus, Thanos, Argo CD, and vLLM. Here is what the keynotes actually showed and which patterns travel.

A few weeks back, we covered AWS Summit Seoul 2026 and how Korean companies are bringing agentic AI into production. At KubeCon + CloudNativeCon India 2026, a similar theme emerged, but with unique challenges. India's vast population and 22 official languages mean serving citizens can involve answering a farmer in Marathi or handling a loan question in Hindi, often over the phone instead of through an app. This diversity creates a different engineering challenge, which shaped the focus of the Mumbai keynotes.

The throughline the keynotes shared

The word 'sovereign' came up often in the KubeCon India keynotes, and two talks highlighted it directly. Sarvam discussed building sovereign AI for all of India, while the National Payments Corporation of India (NPCI) emphasized that cloud-native technologies are now essential infrastructure for large-scale, sovereign AI. This trend isn't limited to India. At Naver Cloud's DAN25 conference in Seoul in November 2025, CEO Kim Yuwon and others also focused on sovereign AI, describing it as vertical models built by companies that understand their own country's language, data, and industry. Seeing this idea appear independently in both Seoul and Mumbai shows that sovereign AI is now an architectural need, not just a slogan.

Scale was expected at this event, but it wasn't the main theme. Every keynote addressed large-scale operations, though not all in the same way. Sarvam and NPCI focused on serving the entire population, while Flipkart, Rapido, and JioHotstar represented big commercial platforms.

Sarvam's approach to sovereign AI is rooted in policy. It is one of twelve organizations chosen under the Innovation Center pillar of the IndiaAI Mission, receiving ₹246.72 crore in funding and computing resources. Sarvam is building core language and speech models like Bulbul (text-to-speech for 11 Indian languages with 39 voices) and Saaras (speech-to-text for all 22 scheduled languages).

What the enterprise keynotes actually showed

Sarvam's Avi Kothari and Vinayak Gavariya described a conversational-AI platform that India needs to speak in 22 languages, from Marathi to a farmer confirming her crops, to Tamil for a citizen accessing a government scheme, to Hindi for a loan query. The scale they cited was millions of API calls and voice conversations daily, at sub-second latency, while remaining auditable, and they grounded it in a concrete deployment: a national crop-verification program that reached 17 million farmers in a single 24-day campaign, with a roadmap to 80 million farmers the following season. The stack was entirely CNCF: Linkerd for a zero-trust service mesh, Strimzi-managed Kafka for event streaming, Argo CD and Helm for GitOps delivery across roughly 20 components, Prometheus and Thanos for unified multi-cluster observability, and CloudNativePG as the database of record.

Tittu Varghese from NPCI highlighted what population-scale means for payments. NPCI processes over 24 billion transactions per month and 700 million payments per day. They also run real-time fraud detection at sub-second speeds using Kubernetes, Kubeflow, Prometheus, vLLM, OpenStack, and NVIDIA AI platforms. This was the clearest example of AI inference being part of essential national infrastructure.

Flipkart's session focused on reliability rather than AI inference, with its Reliability Engineering team building a centralized, multi-tenant chaos-engineering platform using the LitmusChaos project to strengthen hundreds of microservices. They customized Litmus in four ways: a hybrid multi-tenancy setup, a DaemonSet-based high-availability model for chaos injection, a Script Runner for dynamic target selection, and a hybrid extension for VM workloads outside Kubernetes. Their efforts were recognized on stage by winning the CNCF End User Case Study Contest. They moved about 90% of the chaos experiments into staging clusters and contributed 5 fixes upstream, including improvements to database index and duplicate-name validation.

Rapido and JioHotstar provided more examples at the consumer scale. Rapido's CTO, Srivatsa Katta, and Adarsh K Kumar talked about India's largest taxi platform, which handles over 4 million rides daily by matching riders and drivers in real time. Their system runs on more than 150 microservices and processes 200,000 requests per second using Kubernetes, Istio, Prometheus, Thanos, Cert Manager, and External DNS. Pradeep Bishnoi from JioHotstar explained how they stream live cricket to 50–60 million users at once during big events, using Kubernetes, Amazon EKS, OpenTelemetry, Karpenter, and Envoy Gateway across many clusters worldwide. He also previewed new AI-driven features, like conversational Kubernetes troubleshooting and smarter SRE automation.

The GPU question kept coming up

Another key theme was that the main challenge in AI workloads is not getting enough GPUs, but using them efficiently. Saiyam Pathak from vCluster explained that while containers, autoscaling, multi-tenancy, and RBAC are solved, Kubernetes still requires whole GPUs even if a workload only needs 10% of one. His solution is HAMi, which splits a physical GPU into memory-based slices, and DRA, which lets Kubernetes treat GPUs as more than just simple counts. He showed this live, with a MacBook controlling an NVIDIA DGX Spark, where a single Blackwell GPU ran two open-source LLMs for two teams simultaneously. In another session, Ravindra Patil from Red Hat addressed the same issue at the routing layer with llm-d. He compared it to a basic round-robin balancer that spreads repeated prompts across pods, causing unnecessary cache reloads. Instead, llm-d sends each request to a pod that already has the needed cache (cache-aware) or to the least-loaded pod if starting fresh (load-aware), using a pipeline of scrapers, filters, and scorers to pick the best target. This approach led to fewer redundant cache loads and better P95/P99 latency.

The CNCF-native stack that recurred

Several projects appeared in different keynotes. Prometheus was mentioned in almost every talk, often with Thanos for multi-cluster observability. Argo CD was the main tool for GitOps delivery. vLLM handled inference, and the discussion about splitting GPUs (HAMi, DRA, KAI-Scheduler) echoed the Dynamic Resource Allocation theme from AWS Summit Seoul. Service meshes were widely used, with companies choosing between Linkerd and Istio.

The themes worth taking home

A few key patterns emerged. First, sovereignty has become a core architectural need, not just a buzzword, and this idea is emerging across regions, from Sarvam and NPCI in Mumbai to Naver's DAN25 in Seoul. Second, when the stakes were truly population-scale, like Sarvam's millions of farmers or NPCI's 700 million daily payments, the same cloud-native tools used by Flipkart, Rapido, and JioHotstar for commercial traffic handled the load. Third, the GPU challenge has shifted from getting enough hardware to making better use of it, with solutions like HAMi, DRA, and KAI helping Kubernetes manage fractional, capability-aware GPUs. The main takeaway is that none of this relies on new infrastructure built just for AI; it's about using the existing CNCF stack well.