What is free trial abuse -- and how can you stop it?
What would you do if every new “user” signing up for your app cost you money?
Free trials are supposed to spark adoption—not send you a bill. Yet for countless SaaS platforms and developer tools, each new signup quietly drains compute credits and API spend before revenue ever appears.
That's because many of today’s most popular AI apps use this pattern: they proxy access to shared API credentials, typically to services like OpenAI, Anthropic, or proprietary machine learning models, behind the scenes.
Instead of asking users to bring their own API key, the app authenticates once with the upstream provider and makes requests on behalf of its users.
It’s an ideal architecture when everyone plays by the rules. Users get a seamless onboarding experience, and the platform maintains control over usage limits.
Everyone wins — until they don’t.
Abuse starts when opportunistic users — or more often, automated botnets — create fake accounts en masse to farm these backend resources. They aren’t exploring your product.
And because these users never intend to pay, they drain your resources without ever contributing to revenue, turning your free trial into a loss center.
The new playbook for abuse
Free trial abuse has evolved from simple “sign up again with a different email” tactics into something more industrialized. Abusers now operate with greater sophistication and lower friction than ever.
Many start by automating account creation at scale. Scripts can generate unlimited email addresses or register custom domains that haven’t yet been flagged by spam filters. Others rely on trusted providers, such as Hotmail or Outlook, to appear legitimate, often paired with GitHub OAuth for instant sign-up.
These attackers don’t stop at account creation. They script interactions to solve CAPTCHAs and automatically follow email verification links, bypassing the very protections designed to gate access. Once inside, they hijack session tokens to call APIs directly, often skipping the UI altogether.
And the attacks don’t end there. Some craft clever prompt injections, designed to strip away any product-provided context and gain raw access to the underlying model.
In one recent case, we observed attackers targeting a product that had just integrated Deepseek R1 — one of the newest and most expensive open-weight models on the market.
These aren’t casual users misbehaving. This is programmatic exploitation of generous onboarding flows and developer-friendly abstractions.
And without real-time behavioral analysis, it’s nearly impossible to spot.
Why the problem is accelerating
The dynamics of free trial abuse have gotten worse, not just more frequent, but more damaging, because the economics of modern SaaS have undergone significant changes.
Products now embed high-value inference, search, and compute operations right into the core user flow. Providing a new user with five free messages may result in five expensive OpenAI completions, which will be billed directly to your account.
Second, the tools available to abusers have leveled up.
It’s trivial to simulate clean browser environments, rotate through IPs, and evade naive bot detection. The rise of open-source “abuse kits” means attackers don’t even need technical skill — they just need the right script.
Third, traditional defenses haven’t kept up. IP-based rate limiting, CAPTCHA, and email verification — all of them provide a false sense of security. A well-resourced attacker can solve or bypass all three.
What doesn’t work anymore
A lot of abuse still looks like normal usage. Teams that rely solely on surface-level indicators often miss early signs.
You can’t trust email validation when every attacker has access to inbox automation and fresh domains.
CAPTCHA won’t help if you’re dealing with human-solving farms or tools that replay user input.
Even IP reputation is becoming unreliable, as residential proxies can mask the origin.
What teams really need is visibility into behavior — the ability to observe intent, not just input.
How WorkOS Radar changes the equation
WorkOS Radar was built specifically to combat these threats. It hooks directly into your authentication flow, operating in real-time, and provides both fine-grained detection and actionable signals.
Understanding behavior in real time
Radar evaluates every authentication attempt using a rich set of contextual signals.
That includes device fingerprinting, location consistency, timing patterns, and cross-account replay detection.
Instead of just asking “Did they log in?”, Radar asks “Does this login make sense?”
Detecting the undetectable
Automated abuse is often wrapped in well-formed requests. Radar doesn’t rely on obvious tells.
It identifies headless browsers, inconsistent rendering stacks, and subtle timing discrepancies that indicate automation, even when the traffic looks human at first glance.
Catching abuse across accounts
When abusers create dozens or hundreds of accounts, they’re often using the same tools, browsers, or environments.
Radar sees those connections. It identifies shared device fingerprints, reused IP pools, and identical behavior patterns across seemingly distinct users.
Customizing defenses with precision
Radar doesn’t force a single response. Through its Actions system, you can define exactly how to respond to each signal — whether that’s a silent block, a CAPTCHA challenge, a 2FA request, or an escalation to manual review. This provides flexibility without compromising automation.
Try WorkOS Radar
Drop Radar into your auth flow and watch live signals surface within minutes. You’ll know—without guessing—which users are exploring your product and which are just farming your API. Give it a week and judge for yourself.