Private & Self-Hosted AI Decision Checklist

Use this checklist to turn “we need private AI” into a testable deployment requirement. Complete it with the business owner, infrastructure team, security owner, and the people who will operate the application. Record the evidence behind each answer. An unknown should become an assigned decision, not an assumption hidden in the architecture.

The checklist supports Private & Self-Hosted AI Solutions, including private retrieval, self-hosted inference, and hybrid workflows. It does not select a model or certify a deployment. Its purpose is to make the required boundary and the operating commitment explicit before implementation.

1. Name the reason for private deployment

Identify the actual constraint: processing location, restricted connectivity, direct runtime control, particular data restrictions, or another operating requirement. State who owns the requirement and what evidence would demonstrate that it is met. Distinguish a mandatory boundary from a preference that can be compared with cost and capability.

Write one concrete workflow beside that requirement. Include users, input types, expected outputs, permitted actions, and volume. A deployment pattern that suits a batch document job may not suit an interactive application with sharp demand spikes. The workflow profile makes the infrastructure question answerable.

2. Map all information flows

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Trace a representative case through the whole system. Include every fallback and diagnostic path. A local model with an external document parser has a different boundary from a fully local pipeline. A private endpoint describes connectivity; verify processing and retention separately for the chosen service.

Microsoft's data privacy documentation distinguishes processing from storage and describes feature-specific handling. Use that as an example of the specificity required when reviewing a managed service, not as a statement that all providers or deployments behave alike.

3. Record the model and software artifacts

List the exact model release, tokenizer, serving runtime, containers, dependencies, and any additional code loaded with the model. Identify the source and integrity verification for each artifact. Record how updates enter a restricted environment and who approves them.

Check the applicable license and use conditions for the actual release. Record any obligations relevant to your intended use, distribution, or modification. Model cards can help locate intended-use and evaluation information; they are not a substitute for reading the license. See Hugging Face's model-card documentation.

4. Test capacity and task quality together

Define representative input lengths, output lengths, concurrency, peak arrivals, and queue tolerance. Measure task quality under the serving configuration you intend to run. Include difficult documents and the languages actually used by the business. Record the response when requests exceed supported limits.

A capacity worksheet should separate steady demand, bursts, maintenance periods, and a component failure. Specify what can queue, what can be deferred, and what must remain available. Record the measurement setup so a later model or configuration change can be compared fairly.

5. Inspect the deployment surface

Review user-facing endpoints, administrative interfaces, worker communication, outbound network access, file handling, and credentials. Record which identity can access each surface. Verify the actual controls instead of assuming that a serving framework's API key protects every route. The vLLM security guide provides a concrete example of this limitation and the need for additional deployment controls.

For applications with tools, separate model inference access from authority to act in business systems. For retrieval, verify permissions on both original evidence and derived summaries. For logs, decide which content is necessary for diagnosis and which should be excluded or restricted.

6. Specify hybrid routing and failure behavior

If some tasks may use an external service, define the permitted data classes, endpoints, and transformations. Enforce that rule in the application. Test an unavailable local model, a routing error, an oversized payload, and an unsupported file. The fallback must still satisfy the original boundary.

Document whether the system may stop, queue, use a reduced local capability, or ask for review. Avoid a general “use the best available model” fallback where the deployment requirement restricts where the data can go. Test the route with representative examples and record which evidence proves the rule held.

7. Complete ownership and cost records

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Compare cost per accepted task at the required service level. Include the people needed to sustain the system. Record assumptions separately from measurements and show which variables change the decision. A control requirement can justify a deployment even when it is not the lowest-cost option; make that tradeoff visible.

Make the next decision concrete

Finish with a boundary statement, a feasible deployment option, the unresolved questions, and the evidence needed to close them. For an illustrative internal knowledge application, the first open question might be whether a permitted local model can produce sufficiently grounded answers from approved documents at the expected load. That is a useful pilot objective.

Use the Architecture Decision Guide to frame the application pattern and Custom AI Development to connect a defined design to implementation. If the choice changes enterprise sourcing, AI Technology & Platform Strategy provides the wider decision context. Discuss an AI project with the completed checklist.