The AI SaaS launch checklist: 43 checks before go-live

Before an AI SaaS goes live, make sure nobody can spend your model budget for you, and make sure a hijacked prompt can’t touch data it shouldn’t. In practice that means a hard spend limit on every model account, a usage quota per user in your own database, rate limits on every endpoint that calls a model, least-privilege tools with human approval for risky actions, and an eval set you run before every release.

Those are 5 of the 25 blockers on the list below. The full checklist has 43 checks in 7 groups. Tick them off in your browser, then download the list as markdown for your repo or tracker.

Who this is for: founders and engineers about to put an AI product in front of paying users, whether you built it yourself, with an AI app builder, or with an agency. Who should look elsewhere: if you handle health, payment card, or children’s data, you need your regulator’s checklist too. This one doesn’t cover HIPAA, PCI DSS, or SOC 2.

The same tool lives on its own page at /tools/ai-saas-launch-checklist/, with a blank markdown copy.

Why an AI product needs its own launch list

A normal SaaS costs about the same to run whether a user clicks once or a thousand times. An AI product doesn’t. Every request has a token bill, so a user, a bug, or a script can turn into a cost you can’t cap after the fact. OWASP’s 2025 list of LLM risks calls this “Unbounded Consumption.” OWASP says it happens when an app “allows users to conduct excessive and uncontrolled inferences,” and lists “economic losses” among the results.

The second difference is that the model reads text you didn’t write. A file upload, a web page, or an email can carry instructions. OWASP’s top risk is prompt injection, and its own page is honest that no fix is complete. The guidance is to limit what a hijacked model can do.

The launch checklists that rank for this question today mostly mix general SaaS items with marketing tasks, and few of them link a primary source. This list sticks to what can cost you money, data, or trust in your first weeks live, and it says where each rule comes from.

The five gates on every model call

Most of the spend and abuse checks come down to one request path. Each gate has a clear answer for the user when it says no.

The order matters. The rate limit and the quota run before the model call, so a rejected request costs you nothing. The log runs after it, so it records what the call really cost.

The checks people skip

These five come up the least in the guides we read, and each one has a cheap fix.

1. The spend-cap error is not a normal rate limit

Anthropic’s rate limit docs say that when your organization hits its monthly spend cap, the API returns HTTP 429, the same status as a rate limit. But “the response has no retry-after header. Retrying, including the SDK’s automatic retries, fails until access resumes.” Access comes back at “00:00 UTC on the first day of the next month,” unless you raise the limit.

So if your code treats every 429 as “wait and retry,” it will retry for weeks. Check error.details.error_code for enforced_spend_limit_reached, show users a clear message, and alert a person.

Set your own lower limit too. When usage reaches a limit you set, Anthropic returns HTTP 400 with invalid_request_error instead. OpenAI’s production guide says the same thing in one line: “To enforce a monthly cap, set a hard spend limit.”

2. Your quota and the provider’s limit protect different things

Provider limits protect your account from going over a number. They don’t know which of your users is spending it. Only a quota in your own database, checked before each call, stops one free account from using up what ten paying accounts pay for. How to price an AI SaaS shows how to find the usage level where a user stops paying for themselves. Set the quota below it.

3. Card testing hits AI products too

A free trial that asks for a card is a target for people checking stolen cards. Stripe’s card testing guide explains that testers can use your publishable key to try many cards, and the declines raise your decline rate “even after card testing ceases.” The fix is to use Stripe Checkout or the Payment Element, require login before payment, and limit cards per account.

4. Webhooks arrive twice and out of order

Stripe’s webhook docs say: “Webhook endpoints might occasionally receive the same event more than once,” and Stripe “doesn’t guarantee the delivery of events in the order that they’re generated.” If your handler grants credits on invoice.paid, a duplicate grants them twice. Store processed event IDs and skip repeats. Verify the signature on the raw request body, or an attacker can send you fake events. Our Stripe subscription patterns post has the handler shape.

5. A model alias can change under you

If you call a model by a moving alias, the provider can point it at a new version on a day you didn’t pick. Outputs, token counts, and cost can all change. Pin a dated model version, keep a set of real inputs with known-good answers, and run that set before you switch. Our own AI SaaS page puts “Evals and cost monitoring from day one” in what a first version usually includes, for this reason.

The full checklist

Here is every check, with the reason and the source. The tool above uses the same data.

How to use it

1. Two weeks before launch, go through the blockers with the person who wrote the code. The quota and the eval set usually take the longest, so start those first.
2. One week before, do the test-mode billing paths and a backup restore. These find real bugs.
3. On launch day, check that spend alerts fire. Set a temporary low threshold, trigger it, then set it back.
4. In the first month, work through the “should” items, starting with the uptime check on your AI feature and the runbook.

If you are adding usage-based pricing as part of launch, read usage-based billing for AI first. And if the AI feature can take actions, such as sending email or issuing refunds, the approval gates in our AI agents work matter more than anything on this page.

FAQ

What should I check before launching an AI SaaS?

Start with the 25 blockers: hard spend limits on each model account, a usage quota per user, rate limits on AI and sign-up endpoints, least-privilege tools with human approval for risky actions, an eval set run before each release, verified and deduplicated Stripe webhooks, error tracking, and a tested restore. The “should” items can follow in the first month.

How do I stop users from running up my OpenAI or Claude bill?

Use three layers. A rate limit per user and IP stops bursts. A quota per user in your own database stops slow overuse. A hard spend limit on the provider account stops everything else. Only the first two can tell one user from another.

Do I need evals before launching?

Yes, even a small set. A few dozen real inputs with known-good answers is enough to start. Run them before every release and before any model switch, and add every output a user reports as wrong.

How do I protect an AI SaaS from prompt injection?

You can’t fully prevent it, and OWASP says so. Limit the damage instead: give the model only the tools and data the current user is allowed, mark uploaded and fetched content as untrusted, enforce the tenant boundary in the database query, and require a human to approve sends, payments, and deletes.

Is this checklist free to use?

Yes. Download the markdown and put it in your own repo or tracker. Ticks you make in the tool stay in your browser.

We build AI SaaS products with these checks in the first version. See AI SaaS development.