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How to Evaluate an AI Tool Before You Pay for It

frameworksbuying-advice

Every AI tool demos well. That is the whole problem.

The demo is built from the tool’s best-case inputs, run by someone who knows exactly which prompts work, and edited down to the moments it succeeded. Your job, your data, and your patience are none of those things. So the question is never “is this tool impressive?” — it always is. The question is whether it beats what you’re doing now, on your work, by enough to justify a recurring charge you’ll forget to cancel.

Here is the framework I use before paying for anything. It takes about an hour and it has saved me from at least a dozen subscriptions.

1. Name the job before you open the tool

Write one sentence: “I will use this to ____, which currently takes me ____ per week.” If you can’t fill in both blanks, you don’t have a use case — you have curiosity. Curiosity is fine, but it’s a free-tier activity.

The sharper the sentence, the better the evaluation. “Write marketing copy” is too vague to test. “Turn a product changelog into a customer-facing announcement email” is testable in ten minutes.

2. Build a five-task test set from your real work

Before touching the tool, collect five real, recent examples of the job: five emails you actually wrote, five reports you actually summarized, five bugs you actually fixed. Not hypotheticals — things where you know what a good output looks like because you already produced one.

This is the single highest-leverage step. It turns “playing with the tool” into a measurement, and it defeats the demo effect: the tool no longer gets to choose its own exam.

3. Measure time-to-usable, not quality-of-first-draft

Run your five tasks and time the whole loop: writing the prompt, reading the output, fixing what’s wrong, and getting to something you would actually ship. Compare that to your current time per task from step 1.

This is where most AI tools quietly fail. A first draft that’s 80% right sounds great until you notice that finding and fixing the wrong 20% takes as long as writing from scratch — because you have to read everything to find it. I wrote more about this trap in Where AI actually saves time.

4. Check what happens to your data

Before you paste anything real into a tool, find the answer to three questions, in the vendor’s own documentation, not a blog summary:

  • Is my input used to train models, and can I turn that off?
  • Where does my data live, and for how long?
  • What tier do those answers change at? (Data terms are often better on business plans than free ones.)

If you can’t find clear answers in fifteen minutes, that is itself the answer. Tools that handle data well say so loudly.

5. Price the exit, not just the entry

Ask what leaving looks like after a year of use:

  • Can you export what you’ve built — prompts, projects, documents, fine-tuned anything — in a format another tool can read?
  • Does the workflow you’re building live in the tool, or in files and habits you’d keep anyway?

A tool you can leave easily can be adopted casually. A tool that locks you in should have to clear a much higher bar to get in the door.

6. Compare against your real alternative, not against nothing

The alternative to a paid AI tool is rarely “no AI.” It’s usually a general-purpose chatbot you already pay for, or the free tier of the same tool. So the fair question is marginal: what does this specific subscription give me that my current setup doesn’t?

Specialized AI tools earn their keep in exactly two ways: they wrap a workflow (so you stop re-explaining context every session), or they integrate where you work (so output lands in the right place without copy-paste). If a tool does neither, it’s a chatbot with a markup.

7. Do the arithmetic out loud

Take your measured time saved per week from step 3, multiply by what your time is worth, and put it next to the monthly price. Not in your head — written down. Two honest notes on this arithmetic:

  • Time saved only counts if you’d spend it on something better. Saving twenty minutes you then spend scrolling is worth nothing.
  • Recurring costs compound in attention, not just money. Every subscription is one more thing to re-evaluate when it changes its pricing, its model, or its terms — and AI tools change all three constantly.

The one-page version

  1. One sentence: the job and its current cost.
  2. Five real tasks as a test set.
  3. Time the full loop to a shippable result.
  4. Data terms, from the source.
  5. Price the exit.
  6. Compare against your real alternative, marginally.
  7. Written arithmetic, or it didn’t happen.

If a tool survives all seven, pay for it without guilt — genuinely good tools exist and are worth real money. Most don’t survive step 3. That’s not cynicism; that’s the free trial doing its job.