AI feature launch checklist for SaaS: 20 items to copy

An AI feature launch checklist makes sure an LLM-powered feature is tested, secure, compliant, affordable and reversible before customers rely on it. The 20 items below are grouped into six areas: quality, security, data protection, transparency, cost and operations. Copy the list into your tracker; each item has a one-line explanation and, where relevant, the source it comes from.

Key takeaways

• Do not launch without written pass criteria and an evaluation set that you rerun on every change.
• Test the OWASP LLM risks that apply to you, especially prompt injection, sensitive information disclosure and excessive agency.
• GDPR and EU AI Act transparency obligations belong on the launch checklist, not on a post-launch backlog.
• Set hard spend limits and per-user rate limits, and watch scheduled provider price changes and model retirements.
• Ship behind a feature flag with monitoring, a feedback button, a human escalation route and a tested rollback.

Why does an AI feature need its own launch checklist?

Because LLM features fail differently from ordinary code. The same input can produce different outputs, the model can be manipulated through its inputs, costs scale with usage in ways a fixed server bill does not, and the provider can change or retire the model underneath you. A standard B2B product launch checklist still applies; the items below cover what it misses.

Quality: is the feature good enough?

1. Success criteria written down. Each criterion has a number, such as "grounded answers in 95% of sampled cases", and an owner.
2. Evaluation set built from real inputs. It covers typical cases, edge cases, and adversarial cases, as OpenAI's guidance recommends, and lives in your repository.
3. Thresholds met on the current version. Every criterion passes on the exact prompt, model and data you are shipping. Our practical guide to LLM evaluation explains how to set this up.
4. Known limitations documented. Support and sales know what the feature does not do, so they do not promise it.

Security: can it be abused?

5. Prompt injection tested. Include documents, emails or web pages containing hidden instructions. The OWASP Top 10 for LLM applications puts prompt injection first.
6. Data disclosure tested. Try to get the feature to reveal other customers' data or your system prompt; OWASP lists sensitive information disclosure and system prompt leakage as separate risks.
7. Least-privilege tools. If the feature can act (send, update, delete), each tool has only the permissions it needs. OWASP calls the opposite excessive agency.
8. Outputs treated as untrusted. Model output is escaped before display and validated before it reaches code, queries or other systems, to avoid OWASP's improper output handling risk.

Data protection: is customer data handled lawfully?

9. Lawful basis and minimisation confirmed. Each purpose has a basis, and the model receives only the data it needs. Article 5 GDPR requires data to be limited to what is necessary.
10. Provider terms checked. Confirm training use, retention and region. For example, OpenAI states API data is not used to train its models unless you opt in and keeps abuse monitoring logs for up to 30 days.
11. DPIA done or ruled out in writing. Ireland's DPC notes AI use may require performing a data protection impact assessment, especially for new technology or combined datasets.
12. Deletion and access requests covered. Your process reaches prompt logs, caches and vector indexes. Our guide to GDPR and LLMs covers this in detail.

Transparency: do users know what they are dealing with?

13. AI interaction disclosed. Article 50(1) of the EU AI Act requires that people interacting directly with an AI system are informed that they are interacting with an AI system unless it is obvious. Some application dates under the Act have been amended, so confirm the current date, but disclose clearly regardless.
14. AI-generated content labelled where needed. If the feature produces text, images or audio that leave your product, check the Article 50 marking and disclosure duties that apply to your case.
15. Human escalation offered. Users can reach a person, and the feature hands over when it is unsure. For decisions with legal or similarly significant effects, Article 22 GDPR gives people a right to obtain human intervention.

Cost: will the bill stay predictable?

16. Spend caps and alerts set. Configure provider budget limits and alerts at 50%, 80% and 100% of the monthly budget.
17. Per-user and per-task limits. Cap requests per user per minute, tokens per request and, for agents, steps per task. Anthropic recommends stopping conditions such as a maximum number of iterations; OWASP lists unbounded consumption as a risk.
18. Price and retirement dates tracked. Prices and models change on short notice. On 8 November 2026, Google's pricing page already listed scheduled increases for two models in December 2026 and January 2027, and Anthropic's pricing page lists retired models still available only on some platforms (Anthropic pricing). Put review dates in the calendar.

Operations: can you see problems and undo them?

19. Monitoring and feedback live. Log latency, errors, cost per request, escalation rate and thumbs-up/down feedback, with privacy-appropriate retention. Sample live outputs for human review each week.
20. Feature flag and rollback tested. Release to a small group first, keep the previous prompt and model version deployable, and rehearse switching the feature off. Agree in advance who can make that call.

How should a small team use this checklist?

Assign an owner to each item and treat any unchecked item in the security, data protection or transparency groups as a launch blocker. Quality items can be scoped: a beta behind a flag for ten friendly customers can launch with a smaller evaluation set than a general release, as long as everyone knows it is a beta. Revisit the list on every major change of model, prompt or data source, not just the first launch.

What does the copyable version look like?

• [ ] 1. Success criteria written, with numbers and owners
• [ ] 2. Evaluation set with typical, edge and adversarial cases
• [ ] 3. All thresholds met on the release version
• [ ] 4. Known limitations documented for support and sales
• [ ] 5. Prompt injection tests passed
• [ ] 6. Data disclosure and prompt leakage tests passed
• [ ] 7. Tools restricted to least privilege
• [ ] 8. Outputs escaped and validated before use
• [ ] 9. Lawful basis and minimisation confirmed
• [ ] 10. Provider training, retention and region terms checked
• [ ] 11. DPIA completed or ruled out in writing
• [ ] 12. Access and deletion process covers AI data
• [ ] 13. AI interaction disclosed to users
• [ ] 14. Generated content labelling checked
• [ ] 15. Human escalation route live
• [ ] 16. Spend caps and alerts configured
• [ ] 17. Rate, token and step limits enforced
• [ ] 18. Price change and model retirement review dates set
• [ ] 19. Monitoring, feedback and weekly sampling running
• [ ] 20. Feature flag and rollback rehearsed

Frequently asked questions

Which AI launch items are easiest to skip by mistake?

Evaluation and rollback are easy to skip: it is tempting to test a handful of prompts by hand and ship without a way to revert. Both are cheap to set up before launch and expensive to add after a public failure.

Does the EU AI Act apply to a simple AI feature in a SaaS product?

Some obligations can, even for low-risk features. Article 50 transparency duties apply to AI systems that interact directly with people or generate content, while most other obligations target high-risk systems. Check your role and risk category, and the current application dates, before launch.

Do I need a DPIA before launching an AI feature?

Not always, but often. GDPR requires one where processing is likely to result in a high risk, particularly with new technologies, and Ireland's DPC notes AI use may require one. If you decide one is not needed, record why.

How do I stop an AI feature from running up a huge bill?

Set provider spend limits and alerts, cap requests and tokens per user, cap steps per task for agents, and cache repeated prompt content. Review cost per request weekly in the first month, because real usage rarely matches estimates.

Should AI features launch as a beta?

Usually yes. Releasing behind a feature flag to a small group lets you collect real failures for your evaluation set and check costs before wider release. Make the beta status visible to users so expectations match.

If you want a second pair of eyes on an AI feature before it ships, book a free 30-minute call.

Cover photo by Moe Magners on Pexels.