Every team shipping production AI eventually faces the same problem: the model API you depend on will fail. It will rate-limit you mid-request, return malformed JSON, deprecate without warning, raise prices, or simply go down on a Tuesday afternoon. If your application treats the model as a hard dependency, your application is as reliable as your weakest provider.
This talk walks through the design of a dual-provider AI abstraction layer in production at Bloom, a vertical AI platform for the wedding industry. We'll cover the API contract that lets a single call route between Claude and GPT-4o without the calling code knowing which provider answered, the cost-routing logic that picks providers based on task class rather than just price, the fallback semantics when the primary fails mid-stream, and the observability layer that tells you which provider you actually paid for at the end of the month.
Attendees leave with a concrete pattern for treating LLM providers the way mature systems treat any other unreliable upstream dependency: with versioned contracts, explicit fallback paths, cost ceilings enforced above the model, and audit trails that survive a postmortem. Useful for any team currently coupled to a single AI vendor and wondering what happens the day that vendor changes the deal.