Everyone's rushing to deploy AI workloads in production.

but what about observability for these workloads?

AI workloads introduce entirely new observability needs around model evaluation, cost attribution, and AI safety that didn’t exist before.

Even more surprisingly, AI workloads force us to rethink fundamental assumptions baked into our “traditional” observability practices: assumptions about throughput, latency tolerances, and payload sizes.

Curious to hear more insights on this topic from others here.