Avoiding LLM provider lock-in: How to switch models without rewriting your app
Blog post from Braintrust
LLM provider lock-in involves dependencies that complicate switching providers, which can stem from code, behavioral, and data coupling. Code coupling, related to provider-specific SDKs and code changes, can be mitigated by gateways like the Braintrust AI Gateway, which standardizes request formats across multiple providers. Behavioral coupling arises from prompts tailored to specific models, leading to different outputs with a new model, necessitating thorough evaluation to ensure quality requirements are met. Data coupling involves reliance on provider-specific logs and evaluation history, which may hinder migration unless data is exportable and reusable with new providers. Solutions like Braintrust store data separately from model configurations, allowing cross-provider evaluation without rebuilding test cases. When switching providers, it's crucial to validate the candidate model through experiments and evaluations to ensure it meets the application's quality standards, using tools like Braintrust and OpenRouter to streamline the process and reduce engineering effort.
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