The OpenAI API is the fastest way to ship an LLM feature, and for many workloads it should stay exactly where it is. The reasons teams start looking elsewhere are specific: per-token pricing that scales linearly with usage until a high-volume workload dominates the cloud bill, data leaving your environment to a third party in regulated or sensitive contexts, model and version deprecations that force periodic prompt rework, and the strategic discomfort of a single-vendor dependency. Open-weight models you host yourself address all four, at the cost of taking on inference infrastructure. Whether that trade is worth it depends almost entirely on utilization.
When self-hosting actually saves money
Per-token API pricing has no fixed cost: you pay only for what you use, which is ideal for spiky or low-volume workloads. Self-hosting inverts that. You provision GPUs (or a managed inference endpoint) and pay for them whether or not they are busy, so the economics only work when you keep them reasonably utilized. The rule of thumb is that steady, high-volume, predictable traffic favors self-hosting, while bursty or experimental workloads favor staying on the API. Model the crossover with your real token volume in the calculator before committing, and be honest about GPU utilization rather than assuming a full load.
Llama vs Mistral as the open target
Llama (Meta) is the most widely deployed open-weight family, with broad tooling support, a range of sizes, and a large community, which makes it a safe default when you want the deepest ecosystem and the most published guidance. Mistral models are known for strong quality-to-size efficiency, which can mean lower GPU cost per request for a given quality bar, and Mistral also offers its own API if you want open weights without running the hardware yourself. For many teams the right move is to benchmark both on their own prompts rather than choosing on reputation.
The migration is an evaluation problem, not just a plumbing one
Technically, the path is well understood: stand up the model with an inference server such as vLLM or TGI, front it with an OpenAI-compatible gateway so your existing client code barely changes, migrate your prompts and function-calling definitions, and shift traffic gradually. The hard part is quality. Different models respond differently to the same prompt, so the real work is building an evaluation suite from your actual use cases and A/B testing the open model against the incumbent before you move production traffic. Treat “does it pass our evals” as the gate, not “does it return a response.” Each path below opens to a full cost model, a phase-by-phase plan, and an in-depth guide.