Dear founder,
As new users join Podscan and start their 10-day trial, I've been working on determining the right limitations to set.
This challenge isn't unique to my business—it's something every software company offering a free trial struggles with.
The goal is to strike a perfect balance: showing enough value to convert users while protecting your business from excessive usage or abuse.
🎧 Listen to this on my podcast.
This balance has become particularly crucial in the age of AI-powered SaaS. AI features always come with associated costs that can scale significantly if users find ways to abuse them — or even if they just use them too much in legitimate ways.
Take Pieter Levels' recent experience with PhotoAI, his AI-powered photo generation service. He's been fighting an ongoing battle with creative exploiters who found a particularly clever loophole: they would upgrade their plan, use all their credits, then downgrade to get a prorated refund—while keeping the already-used credits.
Rinse and repeat. This kind of exploitation can quickly drain resources and threaten a service's sustainability because GPU compute is still very expensive.
Even benign and legitimate use can be problematic: I had to quickly build a limitation system this week because several users signed up after I went on Greg Isenberg's podcast to talk about AI business ideas, and it looks like a lot of crafty founders listen to that show. A couple of them set up VERY general alerts for extremely common search terms, and they turned on context-aware filtering, which incurs an AI processing step for every matched mention. This is still manageable, but I did the math, and it would easily cost me hundreds of dollars to just run this step for all of their results — and that felt over the top. So I capped trial AI processing limit to a reasonable hourly number, which, when that ceiling is hit, informs the trial user that all they need to do to get the full experience, is to sign up.
When you offer something for free, you need to carefully consider the balance between what you're willing to give away and what users need to see to evaluate your product.
Because, In essence, a free trial is a form of customer acquisition cost (CAC). It's not like an ad, where the cost is paid the moment someone clicks an a link and lands on your product's website. Unlike a one-time advertising expense, your CAC continues to accumulate throughout the trial period until the user either converts or leaves.
Understanding your trial as an ongoing cost makes it more manageable. It makes it projectable! If you know your average customer pays $20 monthly with a typical lifetime value of $200, it wouldn't make sense to let them consume $500 worth of AI credits during their trial—or even $50. You need to carefully consider your cost structure and set limits accordingly.
Different services face different constraints. Every business is unique in the exact makeup of their "at-risk" expenses. Video platforms need to manage storage and bandwidth. Number-crunching applications must handle computation costs. AI-based software has to monitor API usage.
And then there is the size of the trial opportunity. Some customers are just more promising than others, and their LTV is significantly higher. So their acquisition cost might also need to be adjusted.
In my case with Podscan, I've had to consider scenarios like large news agencies wanting to trial our platform by analyzing all political podcasts. While this could create significant server load, it might be worth accommodating for a potential enterprise customer—which is why building in flexibility is crucial. You might even want to think of pre-scoring your customers for how likely they are to be one of those larger opportunities and have multiple "trial limit groups" to accomodate for those different limits.
The key is identifying which actions in your business incur costs when repeated and then protecting yourself from overuse without making the product worthless to users. Rate limits, like those used by YouTube's API (10,000 requests per day) or photo upload services (5 simultaneous uploads, 100 per hour), serve as familiar examples.
Implementation Tips
- Set clear baseline limits that protect your business — this is the maximum a normal user would and should use the "expensive" features.
- Make limits configurable at the account level—this flexibility becomes crucial when working with enterprise prospects
- Log limit-related events meticulously. Hitting a limit is a non-happy-path event. It informs you about a usage pattern you might not have foreseen.
- Track how trial users interact with these limitations
This logging and tracking provide invaluable insights into user behavior. I've seen trial users at Podscan set up the most unexpected integrations that accidentally spam our servers or create massive database loads simply because they're trying to figure things out. These moments create perfect opportunities for meaningful customer interactions.
For instance, when I notice a trial user repeatedly hitting certain limits, I reach out with a message like: "Hey, I see you've set up an alert for a super-generic keyword. That causes a notification every few seconds. This might not give you the results you're looking for, but here's a more effective approach… how about these more specific keywords?" This not only helps the user succeed but also gives me crucial insights into the job they're trying to accomplish.
These user interactions have led to numerous improvements in our onboarding documentation, UI clarity, and example workflows. Sometimes, they even reveal potential enterprise customers who need custom limits for their use cases. At other times, they help identify users who might not align with our business model—and that's okay too.
Don't feel pressured to implement every feature request from trial users. Recently, I had a trial user demand a significant chunk processing capability without any commitment to even our essential plan. They wanted to see "what the system can do."
While it might feel counterintuitive —as it's leaving potential future money on the table—, saying no to such requests is often the right move for your business's long-term health.
Remember: a trial's purpose isn't to provide unlimited free access—it's to demonstrate value and allow users to make an informed decision about committing to your product. While trials should showcase your software's full capabilities when feasible, some features (like expensive customizations or integrations) might need to be demonstrated through case studies, demos, or videos instead.
So set clear limits, make them configurable, and carefully monitor usage patterns, and you will create a trial system that effectively demonstrates your product's value without risking your business's financial health. The goal is to front-load value while maintaining sustainable operations—and sometimes that means saying no to users who don't align with your business model. Or at least saying "not too much."
If you want to track your brand mentions on podcasts, please check out podscan.fm — and tell your friends!
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