Model choice is being judged without a shared view of cost and quality.
Application owners need freedom to compare approved models, while finance and governance need evidence that stays inside the governed environment.
Who you’d be doing this for
“Finance asked which workflow caused the jump, and I had three people pulling exports for half a day.”
Lulu Mugabo · AI Engineering Manager
Owns production AI workflows that consume Foundation Model APIs and must defend model choices to finance and governance.
What is at stake
Finance cannot connect token spend to the workflows creating it, while engineers compare models outside production evidence. You have to weigh reusable visibility against the customer’s limits on prompts and governed data.
Why it isn’t already fixed
Every obvious fix costs something else. That’s the part you’d have to decide.
- model quality vs. token accountability
- workflow autonomy vs. shared governance
- pilot speed vs. reusable instrumentation
- sensitive prompts vs. decision-ready evidence
Why Databricks
Foundation Model APIs, Unity Catalog, and MLflow place governed inference, evaluation evidence, and customer data controls in the same deployment pattern.
Written with these in mind
Not your kind of problem? 107 more at Databricks, or browse every organization.
This is the setup. The work is inside.
Running it puts you in the room: the full situation and its constraints, stakeholders who push back in their own words, and the decisions that are yours to make. What you produce becomes a Day One Plan — work you can show someone instead of describing.