Open source language models give companies more control over data residency, customization, and unit economics. They are not automatically cheaper or easier — the decision depends on volume, privacy needs, and internal ML ops maturity.
Self-hosting tends to make sense when you process sensitive documents, need predictable high volume, or want fine-tuned behavior on proprietary terminology. API providers still win for rapid prototyping and bursty workloads.
Plan for more than the model weights: GPUs or accelerators, evaluation harnesses, prompt/version control, monitoring for hallucinations, and access controls. The model is only one layer of the product.
Start with a narrow use case — internal search, support draft replies, or code assistant for a single repo — and measure accuracy against a golden set before expanding.
Compliance and audit trails matter. Log prompts and outputs carefully, redact secrets, and define retention. Open weights do not remove legal responsibility for how the system is used.
