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SERVICES / DEPLOYMENT

On-prem AI deployment

Running models on owned infrastructure can meet real data, integration or continuity needs. It also makes capacity, updates and support your team’s responsibility.

Establish why it belongs on-prem

Identify which data and workflows must stay in the environment, which dependencies are allowed and who will administer the system. A local pilot does not yet prove that the full operating model works.

Compare owned infrastructure with cloud and hybrid options against the same workload and access requirements.

Size against a known workload

Model memory is only one input. Concurrency, latency, storage, document retrieval, networking and maintenance matter too. Test representative load before committing to hardware.

Define model updates and rollback, failure observation and access for both users and operators.

Make operations understandable

A scoped engagement may include architecture, quality checks, operating instructions and transfer to the responsible team.

On-prem does not mean absolute isolation or establish legal compliance by itself. Connections, suppliers, logs, people and processes all need review.

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