Vendor lock-in in AI has several distinct forms that require different mitigation strategies.
Model-level lock-in occurs when workflows are designed around the specific capabilities, output formats, or API characteristics of a single model provider. Migrating to a different provider requires not just a commercial change but a technical redesign of prompts, parsing logic, and quality evaluation.
Platform lock-in occurs when AI capabilities are built on a managed platform — AWS Bedrock, Azure OpenAI, Google Vertex AI — that bundles model access with infrastructure, security, and governance in ways that create switching costs at the infrastructure layer even if the model itself is theoretically interchangeable.
Data lock-in occurs when an AI system's effectiveness depends on proprietary data processing, fine-tuning, or retrieval infrastructure that is specific to one vendor's format or architecture.
Contractual lock-in occurs through multi-year commitments, volume discounts with minimums, or regulatory approval processes that make switching commercially or practically infeasible within the contract term.
The vendor map section of this site includes lock-in risk assessments for major AI cost management and model provider categories. For the broader strategic implications, see the AI TCO Framework.