There are many open-source projects across inference, orchestration, observability, vector search, data pipelines, evaluation, and model management. Most are relatively easy to test, but production operation is a different problem.For those running open-source AI infrastructure in production:- What are you running, for what workload, and would you recommend?- Do you operate yourself versus consume as a managed service?- Have you replaced or abandoned any tools because they were too difficult or expensive to operate?- What problems only appeared after moving beyond the prototype stage?- Anything that you would do differently if rebuilding the stack today?Thanks