We won about $1,000 in AI credits from a hackathon.
Then lost almost all of it to trial abuse over the weekend.
The painful part?
Our bot noticed.
It just could not act yet.
That is the story. Not a story about why free trials are bad, but a story about why detection needs a path to action.
The $1,000 Weekend
A few days earlier, we made our trial easier to start: no credit card upfront, less friction, faster path to the product.
That was intentional. For us, free trials are part of the marketing budget: a way for real teams, especially companies, to evaluate the product without budget or procurement blocking day one.
The risk was obvious in hindsight. Free access to expensive AI models became easier to try, which was the point. It also became easier to farm, which was not.
Then sign-ups jumped to about 30x normal.
For a few minutes, the dashboard looked beautiful. Sign-ups up, usage up, growth apparently working.
Then we looked closer. This was not demand. It was farming.
Free Trials Were Not the Problem
After digging around, we found the source.
A link had been dropped in a community, framed less like “try this product” and more like “farm these AI credits.”
That distinction matters. We were not upset that people used a promotion. The trial existed for that. The issue was coordinated abuse of a budget meant for legitimate evaluation.
Some users created fake business identities, worked around basic checks, and ran prompts that were clearly outside the spirit of the trial.
Once that spread, the traffic came in waves.
The useful lesson was not “make trials painful again.” It was “keep the happy path open for real buyers while making coordinated farming hard to scale.”
That also made the response tricky. We needed to stop clear abuse quickly, without blocking legitimate teams.
Detection Was Not Enough
We were not completely blind.
We already had an internal guardrail bot watching for suspicious new users. It noticed odd signup patterns, summarized what looked wrong, and put the evidence in front of us.
