How we score AI tools

Last updated: October 7, 2026

Our scores (out of 10) summarise how well a tool does its main job for its target user. We look at:

1. Output quality — how good the results are for the core use case.
2. Ease of use — onboarding, interface, learning curve.
3. Pricing and value — free plan, entry price, usage limits, what you get per dollar.
4. Features and integrations — exports, API, team features, the tools it connects to.
5. Trust — privacy terms, data handling, company track record, support.

Where the information comes from

We combine the tool's official documentation and pricing pages, release notes, our own trials when we have run them, and the overall pattern of public user feedback. Each review says what it is based on.

Keeping scores current

AI tools change fast. We revisit scores when a tool ships a major update or changes its pricing, and every review shows the date it was last updated.

What a score is not

A score is not a guarantee. The "best" tool depends on your use case, budget and team — which is why every review and comparison says who each tool is best for.