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LLMs · September 2026 · 6 min read

Private LLMs: on-premise or API?

Commercial model APIs and self-hosted open-weight models each have a place. The factors that should drive the choice, and why many organisations end up with both.

Every production LLM system starts with a deployment decision: call a commercial model through an API, or run an open-weight model on infrastructure you control. The choice shapes cost, quality, risk and how much your team has to operate. There is no universal answer, but there is a clear set of questions.

The factors that matter

  • Data sensitivity and contracts. Some data cannot leave your environment, by law, by contract or by policy. That settles the question for those workloads.
  • Quality for the task. The largest commercial models still lead on the hardest reasoning tasks. For many focused tasks, such as extraction, classification or answering from your own documents, a well-chosen and possibly fine-tuned open-weight model performs comparably.
  • Cost structure. APIs are priced per token, which is efficient at low or unpredictable volume. Self-hosting is a largely fixed cost, which wins at sustained high volume.
  • Latency and availability. Local models avoid network round trips and third-party outages, which matters for interactive and industrial uses.
  • Operational burden. Self-hosting means owning hardware, serving, scaling, monitoring and upgrades.
  • Change control. API models can change underneath you. A self-hosted model changes only when you decide.

Why many end up with both

The pragmatic answer is often a hybrid. Sensitive workloads run on private models inside your environment; non-sensitive tasks that benefit from frontier capability use a commercial API. A routing and policy layer decides which request goes where, applies the same guardrails and logging to both, and makes it possible to switch providers without rewriting applications.

A short checklist

  1. Classify the data each use case touches.
  2. Define the quality bar with a test set before choosing a model.
  3. Estimate volume and model the cost both ways.
  4. Decide who will operate the system, and whether they can.
  5. Build an evaluation suite that runs on every model or prompt change.
  6. Keep the application independent of any single model provider.

Answer these honestly and the deployment choice usually becomes obvious.

Want to talk this through?

We are happy to discuss how this applies to your organisation, with no obligation.