Model Lock-in is the New Vendor Lock-in

Model lock-in is the new vendor lock-in. And it may be worse.

We've all heard of vendor lock-in — the risk of becoming too dependent on a single supplier, ending up exposed to price increases and high switching costs. In the world of AI, a riskier dynamic is emerging.

The Commoditization Trap

The frontier model layer is commoditizing over time. For many common enterprise use cases, the top models are increasingly fungible and regularly leapfrog each other in benchmarks. The differences are real, but not decisive for every use case.

AI labs understand this. They are investing billions to build massive data centers and capture long-term demand. To reinforce that position, they are surrounding their models with proprietary tooling sold through user subscriptions and enterprise plans.

The strategy is straightforward: get companies trained on tools that only work with one provider's model, and over time make switching operationally and politically expensive.

Why Inference Economics Make This Riskier

This risk is distinct from traditional vendor lock-in because inference economics are very different from software economics. Margins are thinner and costs are uncertain. Much of today's pricing reflects intense competition and subsidized growth — not steady-state reality.

Per-token prices have been falling. But per-query costs are a different story — more capable models, longer context windows, and more agentic workflows all push per-query cost upward even as per-token cost falls. If inference costs rise in the future, companies that trained their staff on tooling from a model provider may find their own margins at the mercy of that provider's pricing decisions.

That's a fragile place to be.

The Safer Path

The safer path is to stay model-agnostic:

• Train staff on tools that can work across model providers
• Favor platforms and workflows that can swap models easily
• Preserve the ability to adopt new models as they emerge
• Keep the path open to local open-weight models, even if they aren't top-tier today
• Use independent model-agnostic SaaS vendors rather than lab-native tooling

Relying heavily on tooling from the major labs may feel convenient today. Strategically, it's an avoidable mistake.

This is one reason VerbaGPT is built to be model-agnostic. Virgo can orchestrate OpenRouter, Groq, and Cerebras. Taurus can work across local agent frameworks. The underlying models are configuration — not architecture. When a new model emerges that's 3x more capable at half the cost, you switch configuration, not platforms.

Originally posted on LinkedIn · January 17, 2026