Hi everyone,

we're building a voice chatbot for a customer using a mix of technologies — Databricks, Azure AI Foundry, and a few external containerized services.

Currently, we're tracking requests and logs via Lakebase with custom traces, but I'm now evaluating whether it makes sense to shift to MLflow (Databricks-managed) for tracing instead.

I came across this tutorial on connecting an external environment to MLflow: 👉 Connect your dev environment to MLflow – Databricks Docs

The guide focuses on local/dev setups, but our use case is different:

• The chatbot runs in an external container (not inside Databricks)
• We need to track GenAI traces (inputs, outputs, latency, etc.) in production
• We want a centralized observability layer directly in Databricks

My questions:

1. Is it technically feasible to send MLflow traces from an external production container to a Databricks-managed MLflow instance?
2. Is this approach recommended for production, or are there known limitations/gotchas?
3. Any alternative patterns you'd suggest for GenAI observability in this kind of hybrid architecture?

Thanks in advance 🙏

Alessandro