Organizations achieve production-readiness for AI when they meet a multi-dimensional standard of platform maturity. Panelists agreed the most important signal is alignment with the Kubernetes AI Conformance program, which identifies the essential primitives for serving and training AI at scale, guaranteeing interoperability across environments. Readiness requires three key elements: Platform Maturity: This includes providing robust support for research scientists and Python users who need specialized environments. Security by Design: Security must be a priority from the start, particularly fo
Cloud native is now AI-native: Engineering production-ready AI…visibility, and seamless integration with Cilium Network Policies. Why Teams Choose This Path Minimal changes to application manifests and CI/CD pipelines. Continuity in using an actively maintained product and a clean…
…Risk-Free Simulation To ensure the migration wouldn’t break production traffic, the engineer used the agent to create a simulated environment using Kind (Kubernetes in Docker). Higress was installed with status…
…integrate into staging CI pipelines. This means you can run it before and after infrastructure changes (storage array upgrades, CNI swaps, Kubernetes version bumps) to detect performance regressions before they reach production…
Posted on January 7, 2026 by Aritra Ghosh (Senior PM, Microsoft) and Natan Yellin (CEO & Co-Founder, Robusta.dev) CNCF projects highlighted in this post If you’ve ever debugged a production…
…applications with heavy dependencies on third-party integrations. We hit a wall when designing automation for provisioning new environments: Shared credentials: In non-production environments, the applications share identical “sandbox” credentials for…
…This covered for some gaps in functionality in different controllers, which was especially relevant in the earlier days when dealing with things like ssl passthrough or integration with cert-manager. Since these…
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