Which authentication services do AI engines recommend?
We asked ChatGPT, Perplexity, Gemini, Claude, DeepSeek, and Mistral: "What's the best authentication service for web apps?" Here's the complete ranking.
The Full Ranking
- Auth0 — 5/6 (not mentioned by Mistral)
- Firebase Auth — 5/6 (not mentioned by Mistral)
- Clerk — 4/6 (not mentioned by ChatGPT, Mistral)
- Supabase Auth — 4/6 (not mentioned by ChatGPT, Mistral)
- Stytch — 1/6 (not mentioned by ChatGPT, Gemini, DeepSeek, Claude, Mistral)
- WorkOS — 1/6 (not mentioned by ChatGPT, Perplexity, Gemini, Claude, Mistral)
- Descope — 1/6 (not mentioned by ChatGPT, Gemini, DeepSeek, Claude, Mistral)
- Kinde — 0/6 (invisible to all engines)
Auth0 and Firebase Lead, Clerk Is Close Behind
Auth0 and Firebase Auth both score 5/6 — nearly universal recommendation. Clerk and Supabase Auth follow at 4/6, showing that newer auth solutions CAN break through if they build enough web presence.
The Invisible Auth Providers
Kinde scores 0/6 despite being a well-funded, growing auth platform. Stytch, WorkOS, and Descope each manage just 1/6.
For authentication — a critical infrastructure choice — being invisible to AI recommendations means developers may never consider these platforms when building their auth stack.
Auth0 has years of documentation, tutorials, and community content. Firebase benefits from Google's massive content ecosystem. The platforms scoring well have deep, structured web presences that AI engines can easily surface.
Newer platforms need to build this content layer — comparison pages, migration guides, integration tutorials — to become visible to AI-powered discovery.
See All Rankings
Full leaderboard with 204 SaaS tools at eurekanav.com/leaderboard. Free AI visibility audit at eurekanav.com.
Methodology
Same query to all 6 engines. Score = mention count (0-6). Temperature 0.3, max 1200 tokens. April 2026.
Methodology Disclosure
Rankings and citations in this post are based on EurekaNav's internal audit dataset (4 published deep teardowns: Fireflies.ai, Linear, Otter.ai, Notta.ai — all available at /case-studies) plus broader audit work across additional SaaS targets. Sample sizes are small. Where we cite specific patterns, they are qualitative observations from a limited sample, not measured industry-wide statistics.
Each audit queries 6 AI engines (ChatGPT, Perplexity, Gemini, Claude, DeepSeek, Mistral) with high-intent buying prompts and reads every engine response. Methodology details: eurekanav.com/methodology.
AI engine outputs are non-deterministic. The same prompt can return different answers across sessions and time. Specific rankings or claims in this post reflect what we observed at the time of audit, not a permanent state. Re-audit dates and any material updates are reflected in this post's visible last-updated tag.
If you spot a claim in this post that you cannot trace to a source or methodology, email don@eurekanav.com — we will provide a source or correct the claim within 24 hours.
Sources & references
Each external claim in this post links to a primary source. Where we cite our own observations, we disclose sample size (currently n=4 published audit teardowns plus broader audit work). For methodology details and our 6-engine scoring approach, see eurekanav.com/methodology.
If you spot a claim in this post that you cannot trace to a source above or to our methodology, email don@eurekanav.com — we will provide one or correct the claim within 24 hours.
