Proprietary LLM APIs bill per token and send your data to a third party. At high, predictable volume, self-hosting open-weight models — Llama, Mistral, DeepSeek, Qwen — on your own inference (vLLM/TGI) can cut cost and keep data in-house. The trick is doing it behind an OpenAI-compatible gateway so application code barely changes, and proving quality with evals.
When it makes sense
Self-hosting wins on steady, high-volume workloads and data-residency requirements. For spiky or low-volume usage, a hosted API is often cheaper and simpler. Be honest about which you have before committing GPUs.
Stand up inference
Serve the model with vLLM or TGI (GPU nodes or managed inference), exposing an OpenAI-compatible endpoint. Front it with a gateway so you set OPENAI_BASE_URL and minimal else changes in apps. Bring your prompt library, tool/function schemas, RAG/grounding, and—critically—your evaluation suite.
A/B before you commit
Run the open model against your eval set and compare quality, latency, and cost-per-1k-tokens versus the incumbent. Then shift traffic gradually, workload by workload, behind the gateway — keeping the incumbent API as instant fallback. Tune quantization for the latency/quality/cost balance you need.
Validation & guardrails
Quality/regression evals vs incumbent, latency/throughput and cost tests, and guardrail/safety + jailbreak checks. Rollback is routing traffic back to the incumbent at the gateway — instant, since app code is unchanged.
Sizing & cost
Model on monthly token volume (annualized) and required latency SLOs; self-hosting trades per-token API spend for GPU/compute and MLOps effort.
Open a source→target page for vLLM/gateway steps and a token-based TCO model.