Is self-hosting an LLM worth it for compliance?

Start with the actual legal requirement before choosing hardware. Data staying in a particular country is different from limiting who can access it or which organizations are in the processing chain. A self-hosted model might address some of those concerns without automatically satisfying all of them.

Map the whole path, including logs, backups, monitoring and any services the model calls. Then have whoever owns compliance check whether that architecture meets the client's requirements.

There's a middle ground between an external model API and running GPUs on your own premises. You can deploy an open-weight model on GPU infrastructure in a tenancy you control, with an agreed location and access setup.

That still leaves you responsible for capacity, updates and uptime. Check the provider's access and data handling terms too, since a controlled tenancy doesn't remove the provider from the picture.

Don't pick a model on benchmark headlines alone. Try a few open-weight candidates against the tasks your application actually handles and compare their outputs with your current API.

The surrounding setup matters too. A task-specific evaluation harness, routing and orchestration can make a difference, so test the whole workflow rather than just sending the same prompt to each model. Coding benchmarks may be useful signals, but they won't settle quality for your particular users.

Hardware depends heavily on the model and how you run it. Before committing to a large model, check its deployment requirements and test the response time and capacity you need. The model isn't the only cost either, you also need a plan for updates and outages.

Self-hosting doesn't solve prompt injection. If users can submit arbitrary content, treat that content as untrusted, especially if the model can access data or trigger actions. Keep those permissions narrow and review what the application does with model output.