Two researchers, six months a year at the keyboard, zero vendor briefings. We pay for every subscription at retail and only write about what we'd use a second time.
14 tools and subscriptions we'd actually want — tested, paid for, and used in production. A short, opinionated list — the only AI tools we'd actually pay for and recommend.
Read the guide →It's the simplest way to keep a review honest. We pay for every tool at retail, and we use the same affiliate accounts you do — which means we have a strong incentive to recommend things that actually convert. And things that convert are usually things that are good.
Read the methodology →We ran both on the same real codebase for two months. The gap is larger than we expected.
After processing 1.2M tokens on both APIs with identical workloads, here's what the bill actually looks like.
We ran identical inference workloads across six providers for three days straight. Two throttled. One crashed. Three held.
The setup that finally made local inference practical — and the three things we got wrong the first two times.
After two years and 20+ integrations, one SDK keeps showing up in every project regardless of the use case.
A full audit of what we were paying for, what we actually used, and what we replaced with a single better tool.