Choose an AI provider by testing it against your app’s real tasks, data constraints, reliability needs, and expected workload—not by relying on a universal ranking or a demo. Define what failure would cost, set acceptance thresholds, verify terms for the exact service configuration, and document why the selected provider is fit for your use.
1. Define the use case and the consequences of failure
Before comparing providers, write down what the production app will ask the AI service to do and what happens when it gives a poor answer or becomes unavailable. A low-stakes drafting feature and a feature that affects safety, finances, eligibility, or access to essential services need different levels of scrutiny.
- Tasks and users: Describe the intended tasks, who will use the feature, and whether outputs are shown directly, reviewed by a person, or used by another system.
- Inputs and outputs: Identify formats, typical size, expected output structure, and whether the service will use tools or other connected systems.
- Workload: Estimate normal and peak traffic, request sizes, and any need for long-context processing.
- Failure impact: List unacceptable errors, how you will detect them, and what the app should do if the provider is wrong, slow, or unavailable.
- Data and constraints: Inventory the information sent to the service, its sensitivity, location requirements, and applicable legal or organizational limits.
NIST’s AI Risk Management Framework (AI RMF) is a voluntary framework for managing AI risks across design, development, use, and evaluation. Released January 26, 2023, it groups work into four functions: Govern, Map, Measure, and Manage. NIST says the framework is being revised. Its development involved 240 contributing organizations, according to NIST’s AI Resource Center; that figure describes framework development, not provider performance.
2. Set acceptance criteria before testing providers
Agree on what “good enough” means for this application before running vendor demos or comparing model claims. NIST’s AI RMF says human judgment should determine which trustworthiness metrics matter and what thresholds are appropriate in context. A threshold suitable for an internal summarizer may not be suitable for a customer-facing decision workflow.
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Build an application-specific evaluation set
Use realistic, permitted examples that reflect the inputs your app will actually receive. Include ordinary cases, difficult cases, edge cases, and inputs that should trigger a refusal or escalation. Keep examples consistent across candidates so the comparison is meaningful.
Choose measures that reflect the feature
Depending on the use case, define thresholds for task quality, factuality or groundedness, safety, structured-output validity, latency, and failure behavior. Record the tested configuration, including the model and settings, so results can be interpreted later. NIST’s AI Resource Center provides testing, evaluation, verification, and validation (TEVV) resources for teams choosing relevant evaluation methods.
Do not treat a polished demonstration as evidence that a provider meets your acceptance criteria. The useful result is a repeatable evaluation on your app’s tasks, with failures reviewed by people who understand the consequences.
3. Verify data handling for the exact service path
Read the terms and documentation that apply to the precise product, endpoint, feature, account, and configuration you intend to use. A broad privacy or security statement may not answer how a particular feature handles prompts, outputs, logs, or connected tools.
- Retention and deletion: Determine what is retained, for how long, and how deletion works.
- Training and secondary use: Check whether submitted data can be used to train models or for other purposes, and which terms or settings control that use.
- Logging and access: Establish what is logged, who can access it, and how logs are protected.
- Location and subprocessors: Check where processing and storage occur and which other organizations may handle the data.
- Exceptions and eligibility: Confirm whether a claimed control applies to your account and every feature in your intended workflow.
Official OpenAI and Anthropic materials illustrate why retention and data-control claims must be checked against product scope, eligibility, and exceptions. Do not interpret a “zero retention” label as a blanket guarantee across all features or configurations; verify its applicability in the current provider documentation and contract.
Rank #2
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4. Assess security evidence and who is responsible
Ask what security controls have been independently assessed, which contracted services that evidence covers, and what remains your organization’s responsibility. Clarify whether the service depends on an underlying cloud provider or other subprocessors, and how responsibilities are divided between your team and the provider.
The UK National Cyber Security Centre (NCSC) says organizations should determine whether a cloud provider is “secure enough” for their requirements. Its guidance makes assurance depth dependent on intended use, data sensitivity, and the consequences of data being leaked or corrupted or a service being unavailable. For sensitive data, bulk personal data, or situations where a breach or outage could have substantial impact, the NCSC recommends assessing its 14 cloud security principles. Its cloud guidance does not replace a data protection impact assessment (DPIA) where one is required.
5. Check operational fit against your app’s service objectives
Compare operational commitments with the app’s service-level objectives (SLOs), not with an attractive headline figure detached from its plan or contract. Review the exact tier you expect to use and confirm that it has sufficient capacity and an escalation path for production incidents.
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- Rate limits, capacity, and how requests behave during demand spikes.
- Support coverage, response commitments, and incident communication.
- Regional deployment options and any location limits.
- Observability information available to your team and the behavior of your app’s fallback path.
For a specific example, OpenAI advertises a 99.9% uptime SLA for its Scale Tier. This is OpenAI’s claim for that tier, not independently measured uptime, a general market benchmark, or a commitment that applies to other plans or providers. Confirm the current terms for the exact service you would contract.
6. Plan for model changes and migration
A production integration has to remain manageable as models and services change. Ask how versions are identified, how deprecations are announced, and what migration window is provided. Check whether your team can test a replacement against the same evaluation suite before switching it into production.
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Keep an automated or repeatable test suite around the provider boundary, and monitor behavior after deployment. NIST SP 800-218A, published July 26, 2024, augments the Secure Software Development Framework (SSDF) 1.1 with AI-specific secure-development practices. It is intended to help AI model producers, AI system producers, and acquirers; it can inform how your team handles provider integrations and changes.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.7. Estimate total cost using realistic traffic
For each candidate, forecast costs using expected input and output sizes, traffic patterns, retries, long-context requests, tool use, and peak demand. Include applicable platform, storage, networking, committed-capacity, support, and migration charges rather than comparing only a headline model rate.
Current relative provider pricing is not established here, and it can vary by model, region, tier, and workload. Verify current pricing directly with each provider for the configuration you are evaluating, then run the estimate against your own expected usage. Revisit the calculation if traffic, prompt sizes, or product behavior changes materially.
8. Record the decision and its review triggers
Make the selection auditable and proportionate to the application’s risk. Record the criteria and thresholds you required, configurations tested, evidence reviewed, unresolved tradeoffs, decision owner, and approval. State what changes should reopen the decision—for example, a new data source, a new model version, longer retention, or a material change to the service.
When comparing multiple candidates, evaluate them on the same application-specific test set and consider task quality and safety alongside privacy terms, security evidence, operational fit, regional and contractual requirements, lifecycle commitments, and total cost. NIST cautions that trustworthiness characteristics can involve context-dependent tradeoffs among reliability, safety, security, transparency, privacy, and fairness; the right balance depends on the system’s context of use.
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