We thought our devs were using AI. We were wrong.

We did what most engineering teams do.

Bought an OpenAI API key. Shared it on Slack. Told everyone to start using AI in their workflow.

It felt like the right move. Productivity went up. Developers were happy. Managers were impressed.

Then the invoice arrived.

Nobody could explain it. We could not tell which team spent what, which model was being used, or whether anyone had accidentally sent customer data to an external provider. We had full AI adoption and zero visibility.

That is when we realized we had confused access with governance.

The problem is not the AI. It is the missing layer between your team and the API.

Most teams operate with raw provider keys floating around in .env files, Slack messages, and IDE configs. When someone leaves, you hope they did not take the key with them. When a pipeline misbehaves overnight, you find out from the billing alert, not from your own monitoring.

We started asking ourselves some uncomfortable questions:

• Who on the team is using GPT-4o versus a cheaper model?
• Is anyone sending PII to an external provider without knowing it?
• What happens if our OpenAI key gets exposed in a public repo?
• Can we switch to Anthropic without rewriting half our tooling?

None of these are exotic concerns. They are the natural consequences of scaling AI access without an infrastructure layer to govern it.

What actually helped

We needed something that sat between our developers and every AI provider, handling authentication, enforcing limits, logging every request, and letting us swap providers without touching application code.

Think of it the way an API gateway manages microservices. Same idea, but for LLM traffic.

Developers point their tools like Cursor, Continue.dev,...at a single endpoint. Two environment variables. Nothing else changes. Behind the scenes, every request is logged, every token counted, every provider key protected.

Governance without friction. That is the only kind developers will actually tolerate.

If your team is using AI without this layer, the question is not whether something will go wrong. It is when.

And, finally, we ended up building a solution for this internally and eventually turned it into a product called GateLLM. If you are curious about the architecture and governance framework behind it, we wrote a full whitepaper on it.

Free to download here.

Curious how others are handling this especially teams running multiple providers. What does your current setup look like?