KubeCon + CloudNativeCon India 2025

This talk explores how KServe and OPEA are redefining the enterprise AI stack through modular and composable architectures. KServe delivers a scalable, hardware-agnostic Kubernetes native platform for production model deployment. OPEA complements this infrastructure with an open, multi-provider platform that provides optimized GenAI solutions through standardized interfaces. By building on KServe's capabilities, OPEA allows teams to focus on developing innovative applications rather than managing complex infrastructure concerns.
Join us to learn how deconstructing AI systems into specialized Lego blocks leads to more extensible and powerful enterprise AI architectures. We will demonstrate how this separation of concerns speeds up innovation, enabling teams to focus on app development for their app development. We will also discuss practical approaches for implementing this "building block" methodology in your organization's AI strategy.

This session delves into the critical aspects of developing production-ready Large Language Model (LLM) applications using Java. We'll explore how to leverage Java's strengths to build scalable and efficient LLM systems, addressing key challenges such as performance optimization, resource management, and seamless integration with existing infrastructures.

Attendees will gain practical knowledge on handling massive datasets, optimizing model inference, and fine-tuning LLMs for optimal performance. We'll discuss strategies for ensuring the reliability and scalability of your LLM deployments, empowering you to create robust and high-performing AI applications. Whether you're a seasoned Java developer or new to the AI domain, this session will provide valuable insights and guidance for your LLM development journey, equipping you with the tools and knowledge to navigate the complexities of building production-grade LLM systems.

Kubernetes-native development is exhilarating—until complex workflows, slow feedback loops, and steep learning curves slow you down. Teams often spend days or weeks setting up environments, managing dependencies, and debugging microservices. GitLab found that nearly 44% of organizations take over two months to onboard new developers—precious time lost in today’s fast-paced tech world.
This session shows how we cut environment setup from days to minutes, slashed feedback loops by over 90%, and reduced onboarding time by 50%.
The secret? A powerful blend of CNCF tools—Kind and DevSpace—combined with simple lightweight Node.js and Bash/PowerShell scripts, We have automated environment provisioning, code synchronization, and cluster management so that developers can focus on shipping features.
Discover how to integrate Kind and DevSpace for a seamless workflow with real metrics and tips. Developers, architects, and DevOps can streamline Kubernetes, speed onboarding, and boost iterations.

The Tenant Exporter enhances observability in multi-tenant Kubernetes clusters by delivering curated namespace metrics for GPU workloads at Adobe. Built on Prometheus, it exposes metrics like ingress requests, container CPU/memory, GPU utilization, and quotas. Users can select metrics via a ConfigMap, with quotas managing load.

The architecture features a Prometheus instance for metric collection, an Nginx proxy for load balancing, and secure authentication using prom-label-proxy with kube-rbac-proxy. A live demo will showcase configuring self-service metrics for a GPU namespace, integrated with Prometheus.

Attendees will learn to deploy this system, manage quotas, and scale metric delivery across clusters. Drawing from Adobe's experience with thousands of namespaces, this session offers best practices for observability using CNCF tools to reduce overhead and improve insights. Join us to elevate your Kubernetes observability strategy and optimize GPU workload management.