How vendor lock-in happens in AI

It usually starts with convenience. A platform offers a complete solution — model, orchestration, storage, integrations, all bundled. It's easier to use than assembling components yourself. So you build on it. You configure your workflows, train your fine-tunes, store your data, and wire your other systems to its APIs.

Two years later, the platform raises prices 40%. Or gets acquired. Or the feature you depend on gets deprecated. Or a better model appears elsewhere and you can't use it because switching means rebuilding everything. You're stuck. The cost of migration is now higher than the cost of staying — which is exactly where the vendor wants you.

This isn't hypothetical. It's happened repeatedly across cloud infrastructure, CRM, and now it's beginning to happen in AI tooling as the market consolidates.

The four principles of a portable AI stack

1. Separate your data from your tools. Your training data, your prompts, your workflow logic, your evaluation benchmarks — these should live somewhere you control, not inside a vendor's proprietary system. If the data that makes your AI system work is stored only in the vendor's platform, you don't own your stack. You rent it.

2. Abstract your model dependencies. Don't hardcode calls to a specific model provider throughout your codebase. Build a thin abstraction layer — a model router — that lets you swap the underlying model without touching the business logic. Today you might use GPT-4o; next year a smaller, cheaper model might outperform it on your specific task. You should be able to switch in hours, not months.

3. Use open standards for integrations. When your AI system needs to talk to your CRM, your database, your communication tools — use standard protocols and APIs rather than platform-specific connectors. Platform-specific connectors mean migration requires rewiring every integration. Standard APIs mean you move the orchestration layer and the integrations stay in place.

4. Own your orchestration logic. The logic that defines how your AI system works — the sequence of steps, the decision rules, the fallback conditions — should live in code you control, not in a visual workflow builder inside a vendor's platform. Visual builders are fast to build in; they're slow and painful to migrate out of.

What this looks like in practice

A portable AI stack typically has: a model abstraction layer that routes to different providers based on task type and cost, a prompt library stored in version-controlled files rather than embedded in a platform, data pipelines that write to your own storage rather than a vendor's proprietary store, workflow orchestration in code (Python, TypeScript) rather than a drag-and-drop builder, and standard API integrations to your existing business tools.

This setup costs slightly more to build initially — you're doing more configuration work rather than clicking through a wizard. But it gives you full control and full portability. Any component can be swapped without rebuilding the whole system.

When some lock-in is acceptable

Not all lock-in is equal. If a platform provides something genuinely proprietary that you can't replicate elsewhere — a unique data source, a specialized model, a specific regulatory compliance capability — the lock-in may be the point. The question is whether the thing locking you in is a feature or just friction.

Lock-in to a platform because it's the only place to get a specific capability: potentially acceptable. Lock-in to a platform because your data and logic are stored in its proprietary format and migrating would be painful: avoidable, and worth avoiding.

How to audit your current stack

Ask three questions about every AI tool you use today. First: if this vendor doubled their prices tomorrow, could we move? Second: if this vendor shut down in six months, how long would our migration take? Third: is the value we get from this platform based on a genuine capability we can't get elsewhere, or is it based on the switching cost we've accumulated?

The answers tell you where your risk is concentrated. Start building portability into the highest-risk dependencies first.