I'm a developer, using AI to help draft this note. A familiar client interface can make two LLM APIs look interchangeable. Before switching, check the behavior your application actually relies on.
Five checks before deployment
- Model identifiers. Select a model from the destination's current catalogue. Do not assume that an identifier from the existing provider is portable.
- Required features. List the parameters and capabilities your application uses: structured output, tools, images or context length. Verify each against the selected route.
- Streaming semantics. An SSE connection alone does not tell you when usable generated content arrives. Observe what the client receives and when the interface can render it.
- Errors and retries. Exercise an invalid request and your timeout path. Check what the client retries and whether your application can duplicate an operation.
- Usage and billing. Save the model, request settings and reported usage. Check the applicable input, output and cache categories before interpreting a cost estimate.
Keep the rollout small
Begin in staging with a sanitized request set and explicit acceptance criteria. Keep the existing provider available, separate the provider call from your application logic, and record failures as well as successful responses.
A successful request confirms that particular request worked. It does not establish feature parity, production capacity or a lower bill. Expand the rollout only after the requirements you wrote down have been checked.
Disclosure: this draft was generated with AI assistance. No comparative performance benchmark is claimed.
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