Model Neutrality: Why You Should Avoid AI Vendor Lock-in

The software industry is undergoing a massive generational shift from cloud to AI agents. This shift is happening at a much faster pace than the previous cloud transition.

The lesson from the cloud era is clear. Hyperscalers like AWS and GCP locked in customers not through the commodity products themselves, such as storage or compute, but through the Tooling Layer, such as CloudFormation or ARM templates. Terraform succeeded by providing a neutral abstraction layer to solve this lock-in problem.

The same strategy is now being repeated in the AI model market. Rather than lowering token prices, model providers are trying to lock in customers through Harness or orchestration layers such as Claude Agent SDK, OpenAI Agents API, and Vertex AI Agent Builder. When business logic gets embedded in the harness, it creates a much stronger lock-in than simply switching the model itself.

Model neutrality matters far more than cloud neutrality. Here's why:

• Overwhelming pace of change: Model performance shifts dramatically on a month-to-month basis, and being locked into a specific vendor carries a high risk of missing out on technological leaps.
• Selective commoditization: While the performance gap between models is narrowing, models still have different strengths in specific areas such as coding or multimodal capabilities.

Ultimately, the key is securing a neutral harness that allows you to flexibly swap in the model best suited for each task.