The one that fits the task, data, and budget. There is no single right answer.

Cloud or on-premise for a private LLM?

Either can work. Choose based on data, volume, budget, and risk.

Grounding the model in your own data so answers are based on your content, under rules you set.

Guide

Private LLM deployment guide

Deploying a private LLM is mostly a series of decisions made before any code ships. This guide covers what to decide first so the result is something you can actually run and trust.

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Decide the model and where it runs

Choose a model that fits the task, data sensitivity, volume, and budget. Decide whether it runs in cloud, private cloud, hybrid, or on-premise.

Plan data access and retrieval

Decide what data the model can reach and how it is grounded. Retrieval over your own documents needs rules about what is in scope and what is not.

Build in security and governance

Access control, an audit trail, and human review are part of the design, not additions. Decide who can ask, see, and act, and how use is recorded.

Plan hosting, inference, and monitoring

Decide where inference runs, how it scales, and how you monitor cost, latency, and quality after launch.

When this matters

You need a model inside your data boundary.
Retrieval over private data is in scope.
Governance and access control are requirements, not nice to have.

What to avoid

Standing up a model before deciding data access rules.
Treating a proof of concept as a production plan.
Leaving monitoring and review for later.
Connecting data to a model without controls.

FAQ

Common questions

Which model is best?: The one that fits the task, data, and budget. There is no single right answer.
Cloud or on-premise for a private LLM?: Either can work. Choose based on data, volume, budget, and risk.
What is retrieval?: Grounding the model in your own data so answers are based on your content, under rules you set.

Keep reading

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