How to Evaluate an AI Tool Before You Pay: A 7-Step Framework

Most AI tool evaluations go wrong at the start. The team demos the product, the salesperson shows the impressive feature, someone says "we should try this", and three months later no one can remember who decided to spend $20/month per seat. This article gives you a 7-step framework that runs in a single afternoon and produces a defensible decision memo.

Why evaluations fail

Most AI tool evaluations fail for the same reason: they measure what the vendor is good at demonstrating, not what the team will actually use. The vendor shows the strongest feature. The team tries that feature on the vendor's prepared example. The team gets excited. Six weeks later, the team is using 10% of the feature and frustrated by 90% of the workflow.

The fix is to evaluate against your real work, not the vendor's demo, with criteria you decided before the call. The 7 steps below are the framework we use internally and that we recommend to anyone evaluating any AI tool.

Step 1: Pick 5-10 real tasks

Before the vendor call, write down 5-10 specific tasks from your real backlog. A "task" is something you would do this week if you had the tool. Examples:

• "Rewrite this customer email in a more formal tone."

• "Summarise this 30-page report into 5 bullet points."

• "Generate a SQL query joining these three tables with this filter."

• "Find the bug in this React component by reading the code."

Each task should be specific enough that you can tell success from failure. "Be more productive" is not a task. "Find the bug in this 50-line component in under 5 minutes" is a task.

Step 2: Try the tasks without the tool

Yes, this is the contrarian step. Before the vendor call, try the 5-10 tasks with the tools you already have. Time yourself. Note the friction. This is the baseline that the new tool will be measured against.

If your current workflow takes 20 minutes per task and the new tool takes 5, that is a 75% reduction worth paying for. If the current workflow takes 4 minutes and the new tool takes 3, that is a 25% reduction that may or may not be worth the integration tax.

Step 3: Try the tasks with the tool (on real data)

Now the vendor call, or the free trial. Use the tool on the same 5-10 tasks, with the same data. Do not use the vendor's prepared demo data. Vendor demos are designed to make the tool look good; your real data is designed to expose where it falls down.

Measure for each task:

• Time to first usable result

• Number of attempts / regenerations needed

• Quality of the final output (subjective but rank on a 1-5 scale)

• Whether you would use the output as-is, edit it lightly, or rewrite it entirely

Step 4: Read the pricing page carefully

Vendor pricing pages are written to make the cheap tier look sufficient. Read the actual line items:

• Is the price per user per month, per active user, per seat, or per API call?

• What happens at the next tier — does the price jump 5x or 50%?

• Are there metered components (per token, per page, per render) that the headline price ignores?

• Is there a free tier that is actually useful, or is it a marketing demo?

Most AI tools hide their real cost in metered components. A tool that costs $20/seat/month is a $240/year budget; a tool that costs $0.01 per API call and your team makes 5,000 calls per day is a $1,825/year line item per engineer. The second looks free on the pricing page. Both are real.

Step 5: Test the failure modes

Every tool fails. The question is whether it fails gracefully. Test the edge cases:

• What happens when the input is empty, malformed, or huge?

• What happens when the API is down — does the tool degrade, fail hard, or pretend to work?

• What happens when the model is uncertain — does it say so, or hallucinate confidently?

• What happens when the data is sensitive — is it logged, retained, used for training?

A tool that fails loudly is easier to operate than a tool that fails silently. The cost of a confident hallucination can be much higher than the cost of an error message.

Step 6: Check the exit cost

This is the most under-asked question. What does it cost to stop using the tool?

• Can you export your data in a portable format?

• Is there a workflow lock-in (proprietary formats, API-only access)?

• If you cancel, do you lose the model that's been trained on your data?

• Are there contractual minimums or annual commitments?

A tool with low entry cost but high exit cost is more expensive than it looks. Read the terms of service for auto-renewal, data retention, and termination clauses.

Step 7: Write a one-page memo

After the afternoon of evaluation, write a one-page memo with:

• The 5-10 tasks and which the new tool won/lost on each

• The all-in cost projection (per team per month, including meters)

• The exit cost and how to mitigate it

• A clear recommendation: buy, don't buy, or extend the trial

The memo's job is to be the artifact that survives the demo. In six months when someone asks "why did we buy this?", the memo is the answer.

Verdict

The framework above is vendor-agnostic. It works for any tool in our directory, from Claude to ElevenLabs to Notion AI. The 7 steps fit in an afternoon. The memo is the artifact. Use both.