How are you operating AI infrastructure in production?

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

Comments URL: [https://news.ycombinator.com/item?id=49163280](https://news.ycombinator.com/item?id=49163280)

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