Topic starter
18/08/2026 2:36 pm
TL;DR: Switching LLM providers can fail even when the API call works, because code, prompts, and evaluation history often stay tied to one model, according to Braintrust. The practical lesson is that portability depends on decoupling routing from behavior testing and preserving reusable datasets for future decisions.
Editorial analysis by NHI Mgmt Group, based on content published by Braintrust: “Avoiding LLM provider lock-in: How to switch models without rewriting your app”.
Key questions
Q: How should teams reduce the risk of LLM provider lock-in?
A: Teams should reduce lock-in by moving provider-specific logic out of application code and into an AI gateway or abstraction layer.
Q: Why do LLM migrations fail even when the API connection works?
A: Because a working API call proves only that the request reached the new model.
Q: What breaks when evaluation history is tied to one provider?
A: Migration teams lose the regression cases and baselines needed to judge whether a candidate model is safe.
Practitioner guidance
- Separate routing from quality validation Use a shared client interface for inference, but require a side-by-side experiment before any provider change reaches production traffic.
- Keep evaluation datasets provider-neutral Store prompts, expected outputs, labels, and regression examples outside any single provider so they can be reused after a migration.
- Test behavioural drift on your own data Run current and candidate models against the same production examples, edge cases, and scorer definitions, then review threshold failures before cutover.
Bottom line: LLM provider lock-in usually appears as a portability gap across code, prompts, and evaluation history, not as a single API integration failure.
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18/08/2026 3:21 pm
Provider lock-in is an evaluation governance problem before it is a platform problem: once teams cannot reuse the evidence that justified a model choice, switching becomes an exercise in guesswork. The article shows that code portability alone is insufficient because behaviour and data dependencies carry the real migration cost. For practitioners, the control question is whether each model decision can be re-tested against the same evidence base.
A few things that frame the scale:
- AI-related credential leaks surged 81.5% year-over-year in 2025, with the surrounding AI infrastructure leaking 5x faster than core LLM providers, according to the State of Secrets Sprawl 2026.
A question worth separating out:
Q: What is the difference between model routing and model portability?
A: Routing is the ability to send traffic to another model through a common interface. Portability is broader: the code, prompts, datasets, and evaluation methods must remain usable after the switch. A system can route successfully and still be non-portable if its quality evidence cannot travel with it.
👉 Read our full editorial: Avoiding LLM provider lock-in requires portable code, data, and evals