Locked runtime versions prevent using newer AI frameworks

Infrastructure implementations lock runtimes (PyTorch, vLLM, etc.) to specific versions to cache container images and ensure compatibility, preventing teams from testing or deploying newer models or frameworks that fall outside the supported list. Runtime compatibility issues add 2-4 weeks to deployment timelines.

Implement customizable runtime environments that allow teams to specify framework versions without waiting for infrastructure updates. Support bring-your-own-library patterns and automate environment reproducibility across development and deployment. Document and test upgrade paths for common AI frameworks.