Open Source LLMs in the Enterprise: A 2026 Field Guide
When self-hosting pays off, when it doesn't, and what the real total cost looks like.
The argument for self-hosting an open-weight model is rarely about cost. It is almost always about control: data residency, audit access, model lifecycle predictability, and the ability to fine-tune on proprietary data without a third-party processing agreement.
Pure-cost analyses usually favor managed APIs at low to moderate volume. The crossover happens later than most teams think, and it depends heavily on whether they actually staff for inference operations. A self-hosted deployment that nobody is paid to babysit is more expensive than the spreadsheet suggests.
For teams that decide to self-host, partnering with a custom software development group experienced in inference infrastructure is often cheaper than hiring a dedicated MLOps function from scratch.