The private AI evaluation guide
Private AI that earns its place in production.
Private AI gives an enterprise a defined environment in which to run and govern AI work. On-prem AI places that environment within its own premises. Evaluate both through the same questions: where does data go, who controls access, does the workflow meet quality and speed requirements, and what does successful work cost?
For US organizations spending $1M or more annually on AI, deployment control and inference cost belong in the same decision. Start with one production workflow and compare the current service with a proposed private deployment. The evidence should let finance, engineering, and security make a decision together.
Define what private and on-prem mean for your workflow
Ask the supplier to describe the deployment boundary explicitly. A private environment may be operated on company premises or in a dedicated cloud environment. Local inference describes where model execution happens; it does not, by itself, describe every destination used by search, tools, telemetry, or support.
Turn a requirement such as “keep data under our control” into testable conditions. Identify permitted locations, people, service providers, outbound connections, retention periods, and deletion responsibilities. Record which conditions are mandatory. Data sovereignty also raises contractual and jurisdictional questions for your procurement and legal teams; a deployment label alone does not resolve them.
Evaluate the engine, runtime, and harness together
The Happiest Labs local AI platform combines its own inference engine, runtime, and orchestration harness. These layers influence different parts of the workflow:
- Inference engine: executes the model. Evaluate its output quality and execution performance on the selected work.
- Runtime: supports running the workflow. Establish the proposed configuration, dependencies, resource behavior, and operational responsibilities.
- Orchestration harness: coordinates model calls, context, tools, and intermediate steps. Examine repeated work, tool permissions, retries, and the path to a completed result.
A fast model call can still sit inside a slow or expensive workflow. Evaluate the complete path from input to accepted result. Confirm supported models, integrations, deployment arrangements, and required controls for the proposed engagement before selecting a pilot.
Map data and controls before choosing a deployment
Use the map below as a procurement worksheet. Each row is a question to investigate, not a statement that Happiest Labs or another supplier provides a particular control. For every answer, record the configuration tested, evidence location, reviewer, and unresolved gap.
| Workflow boundary | Question to resolve | Evidence to request |
|---|---|---|
| Prompts and inputs | Where do user text, files, and temporary copies go? | Input path, storage locations, retention settings, and an observed request trace. |
| Retrieval and context | Where are indexes stored, and whose source permissions apply? | Connector inventory, index lifecycle, and allowed-versus-denied retrieval tests. |
| Execution and tools | Which services, identities, and outbound destinations can the workflow use? | Execution diagram, tool permissions, and observed network destinations. |
| Outputs | Who can read, export, or share completed and intermediate results? | Access tests, export paths, retention rules, and deletion verification. |
| Logs and telemetry | Which content enters diagnostics, and who receives it? | Sample redacted events, collection settings, recipients, and retention. |
| Backups | Which copies survive deletion or leave the deployment boundary? | Backup destinations, access ownership, expiry rules, and a restore exercise. |
| Updates and support | How do changes enter, and what can support personnel access? | Update provenance, approval and rollback procedures, and support access terms. |
These questions apply an end-to-end view of deployment. The joint NSA and partner guidance on deploying AI securely addresses the environment as well as continuing protection and maintenance. Your reviewers should determine which controls and tests your specific workload requires.
Choose a workflow with a clear owner and acceptance test
A useful first candidate has recurring demand, identifiable costs, inputs you can use in an evaluation, and outputs a business owner can judge. A bounded document comparison or structured extraction task can make the decision concrete. These are assessment candidates; confirm actual support before committing to either.
Write down the task boundary, data classification, required tools, peak demand, and consequences of a wrong result. Name the person who accepts the output and the person who operates the service. An unclear business outcome or unresolved data-access requirement is a reason to refine the scope before migrating.
This is consistent with the NIST AI Risk Management Framework's mapping and measurement approach: establish the use context, responsibilities, and intended outcomes, then evaluate the system in relevant conditions. The framework is voluntary guidance, not a product certification.
Set quality and speed gates before measuring savings
Agree what must pass before the comparison begins. For extraction, specify required fields and acceptable errors. For analysis, check material conclusions and supporting evidence. Include difficult inputs, appropriate refusal or escalation, and cases where the requested information is missing.
Measure end-to-end completion time at representative demand, including retrieval, tools, retries, and review. Report a latency distribution and timeout rate rather than a single best run. State how concurrency and queueing were tested. Let the workflow owner set the thresholds; this guide does not invent a universal accuracy or response-time target.
Use the same task set and acceptance rules for both setups, with a held-out final sample. Repeat trials where variability matters and verify the actual result, not merely the system's claim that it completed the task. Anthropic's agent evaluation guidance explains the distinction between tasks, repeated trials, graders, and outcomes.
Compare the cost of accepted work
Divide the full applicable cost of every attempt by the tasks that meet the agreed gates. Include failed attempts, retained services, licensing, operations, and required review. Show transition costs separately. A deployment can improve data control without producing cash savings; report those outcomes separately so the business case stays useful.
The FinOps Foundation's unit economics guidance connects technology costs with business outcomes. Our savings methodology turns that principle into a workflow comparison. Use the inference cost audit kit to organize inputs, and the enterprise AI cost reduction guide to compare possible interventions.
Make a scoped migration decision
Proceed when mandatory controls are verified, quality and speed gates pass, and the business owner accepts the economics and operating plan. Before rollout, identify who handles incidents, how a failed change is rolled back, and which evidence triggers a pause. Expand only after observing the agreed workflow in operation.
Keeping an API remains reasonable when it meets the data requirements and provides better task results or economics, when demand is uncertain, or when the team cannot yet own operations. A mixed approach is also a candidate, provided routing and fallback destinations are explicit. Decide per workflow rather than requiring a company-wide replacement.
Download the blank deployment scorecard
Download the private AI deployment scorecard (CSV). Enter requirements first; leave outcomes unverified until evidence supports them. Record separate findings for the current and proposed setup. A failed mandatory requirement must remain visible rather than being averaged into an overall score.
To assess a specific deployment, request an inference cost assessment and demo. Start with a high-level workflow summary. Agree a suitable channel before sharing sensitive architecture, logs, billing exports, or task samples.