AI Vendor Evaluation Checklist for Startups

Evaluate an AI vendor against a real workflow, a representative test pack and the terms of the plan you would actually buy. General benchmark scores and a polished demonstration cannot establish how the service will handle your information or how much correction your team will need.

Write essential requirements first. A failed requirement on data handling, permissions or output safety should disqualify a candidate before weighted preference scores are calculated.

Prepare a purchase brief

Define the users, monthly workload, input types, required outputs and downstream actions. Specify integrations, response-time needs and what happens during an outage. Separate an employee assistant from an API-powered customer feature: they have different integration and billing requirements.

Create a test set from examples you are permitted to use. Include normal cases, rare costly cases, missing information and a request outside the intended scope. Keep a held-back portion for the final comparison. Do not choose only the examples that looked good during the vendor demonstration.

Inspect terms and controls

Area	Question to resolve	Evidence to retain
Data use	Can prompts, outputs or feedback be used for training?	Plan-specific policy and contractual terms
Retention	What is stored, where and for how long?	Retention and deletion documentation
Permissions	Can users or connected services access excess information?	Configuration and permission-test results
Cost	What happens at expected and higher usage?	Scenario calculation and current pricing
Reliability	How do limits, outages and changes affect the workflow?	Service commitments and fallback test
Exit	Can data, configuration and records be exported?	Export sample and migration outline

OpenAI documents no training on business data by default. Anthropic documents a similar default for its commercial offerings, with stated exceptions and choices. Neither statement means that nothing is stored or that every connected application has the same terms. Read retention, feedback, subprocessors and connector permissions separately.

Run a consistent comparison

Give candidates the same source materials and task requirements. Record the plan, model where visible, configuration and test date. Keep tuning examples separate from the held-back comparison. If features differ, explain what was enabled rather than pretending every plan is identical.

Score factual correctness, source support, required coverage and unacceptable failures. Add reviewer time and cost per usable completed task. A fast response that creates significant correction work can be more expensive than a slower, reliable one.

For integrations, test role permissions, malformed inputs, timeouts and duplicate events. For retrieval, confirm that an unauthorized document remains inaccessible. For code tools, run appropriate tests and human review; do not treat generated review comments as a security certification.

Worked example: compare support assistants

Illustrative selection: two suppliers generate drafts from the same approved help pages. Candidate A produces more polished language. Candidate B more often identifies an unanswered policy question and asks for review. The team should score those behaviors against its actual requirement, rather than automatically rewarding completeness.

Suppose A needs four minutes of correction per usable answer and B needs two. Include that difference in the monthly cost scenario alongside subscriptions or API usage. If either candidate reveals restricted account information, stop the comparison for that configuration and investigate the permission failure before continuing.

Keep the conclusion narrow: the selected plan met the stated requirements on this test pack at this date. That does not establish universal superiority or future reliability.

Make the final decision reviewable

Retain requirements, evaluation cases, results, pricing assumptions, terms and unresolved questions. Assign ownership for security review, commercial approval and ongoing operation as appropriate to the purchase. Set a re-evaluation trigger for major model, plan, policy or workflow changes.

Choose a fallback and an exit path before dependence grows. Test an export where possible; a claim of portability is less useful than a file your team can interpret. Resolve unknowns in writing or narrow the pilot until they are no longer essential.

Use the named tool-selection guide to assemble candidates, AI applications and limits to define the job and the integration overview for implementation.

Turn the checklist into a supplier evidence request

Ask each shortlisted supplier for the same information, scoped to the actual plan. Describe the workflow and data categories without sharing sensitive examples unnecessarily. Request the relevant policy or contractual passage rather than accepting a generic statement that the product is “enterprise ready.”

A useful request identifies the account owner, intended users, connectors, processing locations where relevant, retention, deletion, support access, incident notification, subprocessors and model changes. Some answers may depend on negotiated terms or eligibility. Record those limits and do not transfer an assurance from a different plan.

If a supplier cannot answer an essential question, mark it unresolved. The appropriate response may be to use a sanitized offline pilot, select another candidate or defer the purchase. A promise to investigate later is not evidence that the requirement is met now.

Distinguish defaults from guarantees

A documented default may be configurable. A feature may exist but require a different seat, an administrator setting or a separate agreement. A policy statement may describe the provider’s own service while leaving third-party connectors under other terms.

For example, no training on commercial inputs by default is a valuable fact, but it does not mean zero retention. An administrator’s ability to disable feedback also differs from proof that it has been disabled in your workspace. Keep configuration evidence with the policy record.

Likewise, an advertised export option may cover documents but not automation history, prompts or external account state. Identify exactly what must move if you leave. The exit requirement should match your workflow, not only the vendor’s list of export formats.

Build a cost-per-usable-task scenario

Illustrative comparison: Candidate A costs 180 currency units monthly and requires four minutes of review per usable response. Candidate B costs 300 and requires two minutes. At 300 responses monthly and an internal planning rate of 30 units per hour, review costs are 600 for A and 300 for B.

The simplified totals are therefore 780 and 600 units before integration, consumption outside the subscription, maintenance and failures. The cheaper subscription is not the cheaper completed workflow in this fictional example. Actual results must come from your own test.

Repeat the calculation at higher volume and with worse correction rates. Check what happens when included usage runs out and whether model choice changes consumption. Have the purchasing owner confirm billing periods, minimum commitments and cancellation. Use the automation calculator to expose assumptions rather than inventing vendor prices.

Evaluate supplier fit with a decision matrix

Requirement class	Example	Decision treatment
Essential	Permitted handling of the required information	Pass, fail or unresolved
Essential	No unauthorized retrieval or action	Must pass relevant tests
Operational	Recovery, support and change visibility	Evaluate against operating needs
Economic	Cost at realistic volume and review burden	Compare complete scenarios
Preference	Interface style or familiar formatting	Score only after essentials

Choose criteria before comparing demonstrations. Let each supplier show useful capabilities, but run your own held-back cases afterward. Keep the comparison fair by documenting enabled features and plan differences. Do not handicap one candidate with missing context and then treat the result as a model ranking.

Use the output evaluation guide to make failure severity explicit. A critical disclosure cannot be offset by better writing style elsewhere in the scorecard.

Test exit and continuity before dependence grows

Export a harmless sample and have someone other than the builder interpret it. Document how to reconnect sources, reapply permissions and resume queued work elsewhere. If the service becomes unavailable, identify which tasks continue manually and which must stop.

Review contract renewal and supplier change notices on a named cadence. A new model, reduced allowance or altered connector can require re-evaluation even without a new purchase. Keep dated copies or references to the terms relied on, together with the approved configuration.

At the final review, state the narrow conclusion: this supplier and configuration fit the defined workflow under the observed test conditions. Record exceptions and the next review trigger. That is a defensible purchase decision without pretending to have established a universally best AI vendor.

Sources and further reading

• OpenAI business data commitments
• Anthropic commercial model-training policy
• GitHub Copilot plan documentation, an example of plan-specific capabilities and allowances
• Zapier pricing, an example of usage-based workflow costs