AI Vendor Onboarding Template: A Checklist for Integrating New AI APIs Into Your Product

Confirm you have a signed DPA that covers your jurisdiction (GDPR, CCPA, or sector-specific laws as applicable). The DPA must specify: data categories processed, retention periods, sub-processor list, breach notification timelines, and data subject request handling.

Confirm in writing that your inputs will not be used to train the vendor's models. This requires more than checking a settings toggle — get written confirmation from your account rep that opt-out is active on your API key or organization account. Screenshot or export the setting.

If your product serves regulated markets (EU, financial services, healthcare), confirm that the vendor can guarantee data residency in the required region. If not, document why this is an accepted risk and who approved it.

Request the vendor's model card and document it in your internal AI system inventory. Required fields: model name and version, training data description, intended use cases, known limitations, and evaluation metrics. This is now a regulatory requirement in several jurisdictions (EU AI Act compliant deployments).

Confirm your use case is explicitly permitted under the vendor's acceptable use policy. Edge cases to flag: generating legal advice, medical guidance, financial recommendations, content for minors, or content in regulated industries. Get written clarification from the vendor if your use case is in a gray area.

Confirm the vendor's contractual obligation to notify you in the event of a security incident affecting your data, and confirm the notification timeline is compatible with your own breach notification obligations to end users and regulators.

API keys must be stored in a secrets management system (AWS Secrets Manager, HashiCorp Vault, Vercel Environment Variables, or equivalent). Never in code, environment files checked into version control, or chat logs. Rotate keys on a schedule and define who has access.

Document the vendor's rate limits (requests per minute, tokens per minute, requests per day) and your expected peak usage. Set up monitoring that alerts your engineering team before you hit 80% of any limit. Define what the product does when rate limits are hit: queue, degrade gracefully, or surface an error to the user.

Define explicit handling for: 4xx client errors (bad request, auth failure), 5xx server errors (vendor downtime, model unavailable), and timeout errors. Implement exponential backoff with jitter for retries. Decide which errors should be surfaced to users and in what form.

For every user-facing feature that depends on the AI API, define the degraded-mode experience: what does the user see when the API is unavailable? Options: hide the AI feature entirely, show a cached response, surface a manual alternative, or show a graceful error message. This is a product design decision that cannot be deferred to launch.

Production prompts must be versioned, not edited ad hoc. Use a prompt management system (LangChain, PromptLayer, Helicone, or a simple database table) that records prompt versions, deployment dates, and A/B test results. Without this, you cannot attribute output quality changes to model updates vs. prompt changes.

Set up real-time cost tracking on the vendor account. Configure budget alerts at 50%, 80%, and 100% of monthly budget. Define who is notified and what action they take at each threshold. AI API costs can spike unexpectedly if user behavior changes or if a prompt change dramatically increases token usage.

Plan to release to 1% to 5% of users first, monitor for 48 to 72 hours, then expand. Define the metrics you will monitor during staged rollout and the thresholds that trigger a rollback. Do not launch to 100% of users on day one for any AI feature whose output quality directly affects user outcomes.

Define exactly how you would disable the AI vendor integration in under 30 minutes if something goes wrong. Is it a feature flag? A config change? A code deploy? Test the rollback in staging before launch. Know which team member owns the rollback decision and at what threshold they make it.

Before the first production request, confirm that a monitoring dashboard is live showing: API call volume, latency percentiles, error rates by type, and cost per hour. Add alerts for: error rate above 2%, p99 latency above your threshold, and cost spike above 150% of baseline. Someone on the team is on call for the first 72 hours post-launch.

Any copy that implies the AI is infallible ("our AI will always...", "guaranteed results") needs to be revised. Set appropriate expectations: "AI-generated," "AI-assisted," or "review before use." This is not just a legal concern — users who are not surprised by AI errors are more forgiving and more likely to provide useful feedback.

Define how your support team handles reports of incorrect AI outputs. Who receives the complaint? Who investigates? What is the response SLA? What constitutes a severity-1 issue that triggers an immediate review vs. a bug that goes into the normal queue? Document and train support before launch.

Schedule a 14-day and 30-day post-launch review with the full team before the launch date. Block the calendar now. At the 14-day review: look at accuracy baseline comparison, error rates, latency, and cost vs. baseline. At 30 days: add business metric impact and user feedback synthesis. Do not defer these reviews — they are when most silent regressions get caught.