On-premise AI runs on infrastructure managed within an organisation’s environment. It can provide useful control over processing, network access and operational dependencies. It also creates responsibilities for capacity, security, updates and model operation.
The choice should follow the workload and requirements. On-premise deployment is not a universal legal requirement, and keeping a server inside a building does not automatically keep every data flow inside it.
State the requirement precisely
“Data must stay with us” can mean several things: storage in a particular jurisdiction, processing in a controlled network, restrictions on provider access or an ability to continue without an external service. Those requirements need different evidence.
Map prompts, attachments, embeddings, retrieval results, diagnostic logs and backups. Check outbound connections made by model downloads, package managers, telemetry and support tooling. An on-premise application can still send information to an external API.
A private cloud network can offer useful controls too, but it remains subject to the cloud service’s architecture and contracts. Network isolation is one control, rather than a complete definition of data sovereignty.
Separate the legal question
HIPAA does not categorically ban cloud processing. HHS guidance explains the relevant business-associate arrangements and safeguards where a cloud provider handles electronic protected health information.
Likewise, Canadian privacy guidance does not treat PIPEDA as a blanket ban on processing outside Canada. Accountability and appropriate protection still matter, and other applicable rules may change the decision.
Use the organisation’s actual obligations and contracts to define deployment requirements. Infrastructure selection alone does not establish compliance.
Benchmark the workload you intend to serve
Choose representative input lengths, output lengths, concurrent users and task types. Measure time to first token, total response time, throughput and failure rate under that workload.
A model fitting in memory is not sufficient evidence that the service will meet demand. Long contexts, concurrency and operational headroom affect capacity. Different quantisation and serving configurations can change quality as well as speed.
Evaluate the proposed model on the business task. An available open-weight model may be a good fit, or it may fail a requirement that a hosted service meets. Model licence terms and acceptable-use conditions remain relevant even when weights are stored locally.
Account for operating ownership
A private deployment needs people or a contracted service to maintain it. Responsibilities include patching, artifact verification, access review, backup and recovery, monitoring and handling model or dependency updates.
Define what happens when a node fails, demand exceeds capacity or an update changes outputs. Keep a tested rollback path and a versioned record of the deployed model, runtime and application configuration.
Do not assume a fixed payback period. Compare realistic utilisation, electricity or cloud capacity, staff time and support against the hosted option. Include the cost of idle capacity and peaks rather than extrapolating from a fully occupied benchmark.
Keep application controls in place
A locally hosted model can still hallucinate, follow malicious document instructions or propose an unauthorised tool action. Private hosting does not replace retrieval permissions, application validation or human review where the task requires it.
If the workflow performs writes, constrain them through the connected system’s permissions and the surrounding software. Keep secrets out of prompts and logs. Decide which observable records are necessary and how long they should be retained.
Choose a deployment, not an identity
Some applications suit private inference, some suit hosted services, and some use an explicitly approved combination. Start with the information and operational constraints, test the candidate and document the trade-offs.
Ryzolv’s AI Engineering practice can help with the application, integration and deployment work. The right deployment is the one that satisfies the agreed requirements and can be operated responsibly.
Written by Raj Dalal, Advisor at Ryzolv.
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