We asked ChatGPT and Gemini to recommend B2B SaaS tools. They barely agreed.

What we did

For 8 common B2B SaaS categories, we asked ChatGPT and Gemini the kind of question a real buyer types — “what are the best [category] tools?” — with live web search turned on. Then we pulled out every product each engine actually recommended and compared them. No cherry-picking: the same prompt, both engines, on the same day.

Finding 1: one engine is not all engines

The headline number is the gap. Each engine named roughly 24.4 tools per category — but only 5 of them showed up in both. That’s about 20% overlap. In other words, ~80% of the tools an AI “recommends” depend entirely on which AI you ask. Winning ChatGPT tells you almost nothing about whether you win Gemini. You have to earn each engine separately.

Finding 2: ChatGPT casts a wide net, Gemini doesn’t

ChatGPT was far more expansive — naming about 18.6 tools per category on average, versus roughly 10.8 for Gemini. If you’re a smaller or newer product, ChatGPT’s longer list is your better shot at getting mentioned at all; Gemini is closer to winner-take-few, so cracking it means displacing an incumbent.

Finding 3: the tools both engines agree on are the incumbents

The “consensus” picks — the handful named by both engines — were almost always the entrenched leaders: Zendesk, Intercom and Freshdesk in support; HubSpot, Pipedrive and Zoho in CRM; Mixpanel, Amplitude and PostHog in analytics; Asana, Monday and ClickUp in project management. New and smaller tools, when they appeared at all, lived in a single engine’s long tail. Consistent presence across the web is what earns a spot in both answers — and that consistency is exactly what AEO engineers.

Finding 4: some categories are wide open

Agreement wasn’t uniform. helpdesk and customer support software was the most locked-down — the engines agreed on 9 tools. But API monitoring tools was wide open: out of 28 distinct tools named, the engines agreed on only 2. Fragmented categories like that are where a well-optimized challenger can break in fastest — there’s no settled consensus to dislodge yet.

Methodology & limits

We tested 8 categories across ChatGPT and Gemini on 2026-06-29, with web search enabled, using gpt-5.1 and gemini-2.5-flash. Recommended tools were extracted from each answer with a structured-output model. One honest caveat: API answers approximate, but aren’t identical to, the consumer apps (which add their own search and personalization). The value here is the pattern — the gap between engines, and how concentrated recommendations are — which holds regardless of small per-answer differences. We re-run this on a schedule, so the numbers update over time.

What this means if you sell software

Three takeaways. First, “are we recommended by AI?” has no single answer — you need to measure each engine. Second, being named once isn’t durable; the tools that show up consistently have a clear, structured, well-cited presence across the web. Third, the earlier and more fragmented your category, the bigger the opening. That whole gap — measuring it, then closing it — is the work we do.