Most AI testing setups wire straight into one model from one provider. Which means one point of failure under your whole test pipeline.

Then that provider has an outage mid-run. Or your costs climb with zero visibility. Or you go to swap a cloud model for a local one and find your code welded to a single vendor’s SDK.

An AI Gateway sits between your tests and every model you’d want to call. It’s the piece most testers skip.

We’ll build the pattern with LiteLLM, an open-source gateway that speaks one unified API across 100+ providers. You call it once. It handles the routing, whether the model runs in the cloud, on your laptop, or within your company’s internal infrastructure. 

The parts that matter for testers: one compatible API, so you stop rewriting integration code. Routing and load balancing across models. Rate limiting, so a big suite doesn’t get throttled. 

Then, cloud models vs local models: when each earns its place, and how to switch between them with a single config change. Plus, what that opens up for privacy-sensitive and offline testing.

You’ll walk out knowing how gateways and model switching actually work, with a reference architecture you can hand to your team.