A cheaper classification lane must remain dependable when models change.
AI engineers need lower token use while the serving layer must preserve governed, schema-valid responses.
Who you’d be doing this for
“I changed one model for cost and spent two days fixing every parser downstream.”
Alexandros Economou · AI Engineer
Builds production workflows that classify incoming requests before sending them to more expensive models.
What is at stake
Design partners are rebuilding parsers whenever a lower-cost model returns a different response shape. You must weigh a narrow first API contract against enough flexibility to make model switching worthwhile.
Why it isn’t already fixed
Every obvious fix costs something else. That’s the part you’d have to decide.
- token efficiency vs schema stability
- low latency vs request validation
- model portability vs a minimal API contract
- new capability vs operational simplicity
Why Databricks
Foundation Model APIs already serve governed inference across partner and self-hosted models, so a stable structured-output contract protects customers from catalog churn.
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.