How We Review AI Tools
Hands-on testing, aggregated ratings, and total transparency about how we make money.
A recommendation is only worth as much as the process behind it. Here's exactly how we evaluate tools and build the stacks we publish.
1. We test hands-on
We use tools on real tasks — writing an article, generating a voiceover, building an outreach campaign — rather than repeating marketing claims. That's how we form an opinion on quality, ease of use, and where a tool actually fits.
2. We cross-check with aggregated ratings
No single opinion is the whole story. We compare our hands-on take against ratings and reviews aggregated from across the web, so our verdicts reflect a broad consensus — not just one experience.
3. We build stacks, not just lists
For a stack, we choose tools that complement each other and cover a complete workflow — script, voice, video, and editing, for example. Each tool earns its place by doing one job well and working with the others.
4. We're transparent about money
StackAIHub is reader-supported. When you buy through some of our links, we may earn a commission at no extra cost to you. This neverchanges our rankings or what we recommend — we include tools with no affiliate program (and say so) whenever they're the right answer. Full details are on our affiliate disclosure page.
5. We keep it current
AI tools change fast — pricing, features, and quality all move. We revisit our reviews and stacks and update them, noting when each was last reviewed.
Think we got something wrong? Tell us— we'd rather fix it than be right.