LLM Vendor Lock-in: How OpenAI and Anthropic Trap Enterprise Customers

The document examines the risks of lock-in when using third-party AI infrastructure, particularly focusing on large language model (LLM) providers, and introduces a Lock-in Scorecard to evaluate these risks across six dimensions: data policy, model stability, fine-tune portability, API compatibility, operational risk, and contractual risk. OpenAI, Anthropic, Google Vertex, Mistral, and Cohere are assessed, showing varying levels of lock-in risk, with Mistral and Cohere offering the lowest risks due to open-source models and flexible deployment options. The document highlights the significance of systematic risk evaluation for enterprise teams, especially in regulated industries, and suggests a multi-provider strategy or self-hosting as potential solutions to mitigate lock-in and maintain control over AI models. Additionally, it underscores the importance of evaluating model stability, data privacy, and operational reliability before committing to a provider, while also considering the trade-offs between model performance and lock-in risk.

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