Running LLMs on Your Own Infrastructure: When Data Residency Forces the Decision
Separate the real constraint from the anxious one
Roughly half the self-hosting conversations we have start as a security preference and end as a procurement question. Before pricing GPUs, get the actual requirement in writing: is it a contractual data residency clause, a regulator's expectation, a customer's security questionnaire, or a general discomfort with sending data to an API? The first two usually force self-hosting or a regional dedicated deployment. The third is often satisfied by a zero-retention enterprise agreement and a regional endpoint, which is dramatically cheaper.
The middle option most teams miss
Between public API and full self-hosting sits regional managed inference — models running inside a specified cloud region under an enterprise contract with no training on your data. For a lot of European clients that satisfies GDPR and internal policy without anyone owning a GPU. For Gulf clients, in-region deployments in the UAE and Saudi Arabia have expanded considerably. Check this option seriously before committing to infrastructure; it removes the operational burden while answering the residency question in a form auditors accept.
What self-hosting actually costs
The hardware line is the visible cost and the smaller one. Budget also for an engineer who understands inference serving, GPU capacity that sits idle outside business hours, model evaluation each time you consider an upgrade, and a plan for what happens when the open-weight model you standardised on falls two generations behind. For steady high-volume workloads — millions of calls a month, predictable shape — the economics can beat API pricing meaningfully. For bursty low-volume internal tools, self-hosting is almost always more expensive per useful answer.
Smaller models have gotten good enough for a lot of jobs
The interesting shift is that mid-size open-weight models now handle classification, extraction, summarisation and structured generation well enough for production, especially with retrieval and a tight prompt. You do not need frontier capability to pull fields out of an invoice or route a support ticket. Match model size to the task instead of standardising on one model for everything, and self-hosting gets far more affordable because most of your volume runs on something modest.
Architect so the decision stays reversible
Put every model call behind your own interface with a provider adapter underneath. Keep prompts, evals and retrieval logic outside the provider SDK. Log requests in a provider-neutral format. Teams that do this can move between hosted and self-hosted inference in days; teams that scatter SDK calls across the codebase spend a quarter on it. Given how fast pricing and capability are still moving, optionality is worth the small upfront discipline.
A practical sequence
Start on a managed regional endpoint with strong contractual terms and get the product working. Instrument volume and cost. Once you have real usage data, model the self-hosting break-even honestly including staff time. Move only the workloads where the numbers clearly favour it — usually the high-volume, low-complexity ones — and leave the rest hosted. Hybrid is not a compromise here; it is the sensible endpoint.
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