Who are we looking for?

Who are we looking for?

Companies like you that are:

• small but growing;
• keeping signup open;
• spending real money during activation;
• starting to feel the cost of trusting every new account the same way;
• outgrowing a brittle patchwork of rules, if statements, and distributed controls that make post-auth decisions hard to change, explain, and audit before a user has put down a credit card.

Not giant fraud teams. Not mature billing teams. Not companies that already have an internal policy engine, a trust and safety function, and a finance workflow for explaining every free-user subsidy.

We are looking for the teams in between... too sophisticated for one hardcoded trial limit. Too early for enterprise fraud infrastructure. They still want product-led growth, but the first proof of value now spends tokens, compute, API calls, hosted environments, generated assets, data enrichment, support time, or some other scarce resource.

AI SaaS with no-card trials

You might be this team if you sell AI software and still want a no-card trial.

You want people to feel the product before they pay. That is the right instinct.

But "try the product" no longer means clicking through an empty dashboard. It means LLM calls. Image generation. GPU work. Vector database usage. Agent runtime. Workflow automation.

Put a card wall before the first useful moment and real customers leave.

Leave the door completely open and every signup becomes a small infrastructure bet.

The right question is not just "is this user authenticated?" It is:

How much expensive value should this new account receive right now, and what would make us increase or decrease that grant later?

That is the shape AfterAuth is built for.

Developer tools with costly sandboxes

You might be this team if your developer tool gets expensive after login.

Hosted coding environments. Preview deployments. Browser automation. API testing. Scraping workflows. CI minutes. Test data generation. Security scanning. Observability ingestion.

The abuse does not always look like payment fraud.

Sometimes it looks like an authenticated user spinning up costly resources before the product knows whether they are a real prospect, a hobbyist, a repeat trial user, or automated traffic.

Most teams start with a simple cap.

Then exceptions.

Then domain checks.

Then GitHub signals.

Then support overrides.

Then finance asks why one free account received more budget than another.

At that point, the problem is no longer just rate limiting. It is an explainable trust decision.

Usage-based API startups before billing grows up

You might be this team if you sell a usage-based API and billing has not caught up to the product.

You may already use Stripe. You may already track usage. You may already have quota columns in the database.

But you do not yet have a clean answer for:

• product-side runtime enforcement;
• starter credits;
• risk-based onboarding;
• support-facing explanations;
• finance-readable receipts.

That gap gets expensive because API products can have real marginal cost from day one. A new account might trigger paid enrichment, third-party API calls, model inference, scraping, storage, or queue work before a card exists.

If the only available choices are "block until payment" or "grant everyone the same trial," the team is missing a useful middle:

• give the user a small starter grant;
• increase it when email, domain, GitHub, company, invite, or usage signals improve;
• reduce it when signals look suspicious;
• explain the decision later without asking engineering to reconstruct it from logs.

PLG teams between hardcoded limits and fraud ops

You might be this team if you are founder-led, product-led, and somewhere between hardcoded limits and fraud ops.

Enterprise fraud platforms can be the right answer for high-scale consumer abuse, fintech risk, marketplaces, or companies with fraud operations teams.

That is not the team we are describing here.

The team we are describing wants developer-friendly defaults:

Give this new user a small amount. Increase the budget after stronger signals. Limit or challenge suspicious accounts. Preserve a receipt for support, product, and finance.

You do not need a sprawling fraud command center.

You need a deterministic policy decision after auth and before costly usage.

The cross-functional pain

The best signal is not only the infrastructure bill.

It is when the same decision starts showing up in different departments with different questions.

Product asks whether the trial is too stingy or too generous.

Engineering asks where the policy should live.

Support asks why a specific user was blocked or limited.

Finance asks why a no-card account received a certain amount of value.

The founder asks whether open signup is still worth it.

If each team needs a different explanation, but the underlying decision is the same, that decision should probably become a first-class object.

That is what AfterAuth is trying to provide:

One runtime decision, with a receipt, that says what was requested, what signals existed, what policy matched, what was granted, and why.

Who this is probably not for

AfterAuth is not trying to replace mature fraud teams.

If you are a high-scale consumer company, fintech, marketplace, or enterprise SaaS company with an internal risk engine, you may already have the people and tooling to own this yourself.

If your free tier is cheap to serve, you may not need this yet. A hardcoded limit, rate limiter, feature flag, or simple billing rule may be enough.

If your team wants to block every unproven user until payment, this is probably too much machinery. The product only matters when you want to preserve low-friction signup while controlling what happens next.

The fit is strongest when you believe both things at once:

• real users need to see value before they pay;
• not every new account deserves the same amount of expensive value.

The simple test

Here is the question I would ask.

When a new user signs up, can your team answer these without digging through code and logs?

• What did this user get?
• Why did they get that amount?
• What signals affected the decision?
• What would have increased or reduced their grant?
• Can support explain it?
• Can finance audit it?
• Can product change the policy without scattering logic across the app?

If those questions are still easy, you probably do not need us.

If those questions are starting to hurt, that is who we are looking for.