Adyen processes payments for Uber, Spotify, McDonald's, eBay, and Microsoft. Its 2023 processed volume was north of €970 billion. It's a public company with a market cap larger than most enterprise SaaS names you can list off the top of your head.
And when we asked ChatGPT, Claude, and Gemini "what are the best payment processors to buy right now," Adyen either tied Stripe or lost to it in every single model.
Not by a little. In one model, Adyen wasn't even mentioned in some of the answers Stripe headlined. The final score across our five buyer-shaped prompts was Stripe 3, Adyen 0. Verdict phrase from the scan: STRIPE — DOMINATION. You can see the full fight at foxish.ai/vs/adyen-vs-stripe.
This is the kind of result that makes CFOs at Adyen twitchy and makes AEO strategists lean forward. Because the products are genuinely close on capability. The AI verdict isn't.
The Matchup Nobody Asks About but Everyone Should
Adyen and Stripe are the two most important names in modern online payments — and they've spent a decade building for opposite ends of the same market.Stripe grew up powering internet startups. Its API was the first one that didn't make developers cry. It became the default at Y Combinator, then at Series A companies, then at every SaaS billing page you've ever seen. Its brand association is: "we make payments easy for builders."
Adyen went the other way. It was built to serve the enterprise from day one — one platform, one contract, unified acquiring across regions, direct connections to card networks. Its logos are the ones that show up in Super Bowl ads. Its brand association is: "we run payments for companies whose payments matter at scale."
So when a mid-market brand is choosing between them — say, a $50M/year DTC company evaluating who should handle checkout as they expand into Europe — this is a real decision. Both processors would compete for that business. Both would make credible pitches.
But AI doesn't see it as a real decision. AI sees a lopsided fight.
The Result, Unvarnished
Here's how the five prompts broke down across each model:
| Model | Adyen mentions | Stripe mentions | Winner |
|---|---|---|---|
| ChatGPT | 5 | 5 | Stripe |
| Claude | 5 | 5 | Stripe |
| Gemini | 3 | 5 | Stripe |
That distinction is the whole game in AEO. Stripe is being recommended. Adyen is being listed. Those are different products.
Why the AI Picked This Way
Nobody at OpenAI decided Stripe should beat Adyen. The models don't have opinions, they have training signals. So the question is: what does Stripe's footprint look like in the corpus these models learned from, and what does Adyen's look like?
Four signals jump out.
Stripe writes for the internet. Adyen writes for the boardroom.Stripe's documentation, blog, and engineering content are a huge, well-indexed, heavily-linked body of text. Every developer who's ever integrated a payment flow has hit stripe.com/docs at some point. That content is embedded in Stack Overflow answers, Reddit threads, Hacker News comments, tutorial videos, and course material. LLMs trained on the open web absorbed all of that as neutral-seeming educational material — but every sentence of it reinforces "Stripe" as the reference answer to "how do I take a payment online."
Adyen's public content is mostly investor relations, enterprise whitepapers, and press releases about signing large customers. High-quality content, but structurally the wrong shape. It doesn't answer the questions a buyer or a developer would actually type into ChatGPT.
Stripe's brand shows up in the questions themselves.Look at how people ask about payment processors online. "Alternatives to Stripe." "Is Stripe worth it." "Stripe vs [everything]." The internet has already framed Stripe as the incumbent. Adyen shows up as an alternative to Stripe, which is a subtly losing position — you're defined by the comparison to the leader. LLMs learn that framing and reproduce it. When we asked for a recommendation, Stripe was the noun and Adyen was one of several adjectives.
Third-party review corpora skew hard toward Stripe.G2, Capterra, TrustRadius, and the long tail of "best payment processor 2024" listicles are the review substrate LLMs lean on for buying advice. Stripe has orders of magnitude more reviews than Adyen on most of these platforms because self-serve SMBs are much more likely to leave a G2 review than an enterprise procurement team. The models see 8,000 Stripe reviews and 400 Adyen reviews and, absent other context, weight accordingly.
Product breadth is now a brand story, not a feature list.Over the last three years Stripe has expanded into billing, tax, identity, financial connections, and business banking. Each launch was a coordinated content push with clear positioning. Adyen has expanded too — issuing, embedded finance, unified commerce — but the launches were shaped for enterprise audiences and enterprise press. The Stripe version of "we do more than payments now" saturated the internet. The Adyen version got covered in Payments Dive.
Stripe didn't win because it's better. It won because it's more legible to the systems answering the question.What Adyen Could Actually Do About It
The instinct is to say "Adyen needs more content." That's not wrong, but it's not specific enough to be useful. Here's what would actually move the numbers.
Publish a buyer-shaped answer layer.Adyen's site is built around what Adyen sells. It needs a parallel content layer built around what buyers ask. "Best payment processor for marketplaces." "Payment processor for global expansion." "Adyen vs Stripe for enterprise." These aren't dumb SEO plays anymore — they're the exact prompts LLMs use to retrieve context when a user asks a similar question. Missing pages equal missing mentions.
Get on the listicles.Every "top 10 payment processors" article that ranks on Google is training data. Adyen is often absent from these lists or buried at position 8 because the SMB-focused writers who create them don't know Adyen well. A concerted, transparent PR effort to get included and positioned correctly across the top 50 listicle domains in the payments category would shift model recall inside a training cycle.
Own a set of specific verticals in the corpus.Adyen can't out-content Stripe on "payments in general." It can absolutely own "payments for airlines," "payments for global retail chains," "payments for platforms with regulated tenants." Narrower topical authority beats broader vague presence in retrieval. If a user asks "best payment processor for a food delivery marketplace," Adyen should be the model's first thought, not its third.
Use the customers.Uber, Spotify, McDonald's, eBay — these are the most valuable case study inputs any payments brand has ever assembled. But the case studies live in PDF form on adyen.com/customers, which is exactly the wrong format for AI retrieval. They should be reformatted as long, indexable, question-answering web pages with structured data, direct quotes, and specific problem-solution framings. Every one of those becomes a citation surface.
Fix the entity graph.When ChatGPT is uncertain, it falls back on structured entity data — the Wikipedia-shaped consensus about what a company is, does, and is known for. Adyen's public entity summary is thinner and drier than Stripe's. Investing in Wikipedia, Wikidata, and Crunchbase entity quality is boring, unsexy AEO work that shifts the baseline for every future answer.
None of this is fast. All of it is compoundable.
The Broader Lesson for Payment Processors
Adyen and Stripe are the marquee names, but this pattern shows up across the category. Braintree, Worldpay, Checkout.com, Adyen, Nuvei, dLocal, Rapyd — every one of them is competing for the same finite space in a five-item AI recommendation list, and every one of them is losing that competition to Stripe by default.
The AI answer is a zero-sum surface. Search results have 10 blue links. AI answers have three names, maybe five.If you're a payment processor and your name doesn't show up in the top three when someone asks ChatGPT for a recommendation, you are functionally invisible to the fastest-growing top-of-funnel channel in B2B software right now. It doesn't matter how big your processed volume is or how many logos you have. The buyer's first shortlist is being built without you in the room.
The takeaway isn't "content marketing is important." Every payments company already knows that. The takeaway is that the shape of your content and its legibility to LLMs are now more important than its polish or its brand alignment. A slightly ugly, buyer-shaped Q&A page will outperform a beautiful whitepaper every time in this channel.
Three specific moves apply to everyone in the category:
- Audit which prompts you show up in. Not just "am I recommended," but "am I recommended for the specific use cases where I actually win." Stripe wins on general; nobody's winning on specific yet.
- Fix your comparison pages. If someone asks "X vs Stripe," you want your own well-reasoned comparison page to be the substrate the model uses. Cede that ground and Stripe writes the story.
- Measure the delta over time. AI visibility isn't a snapshot — it's a moving average. The brands that will win the next 24 months are the ones tracking week-over-week changes and responding to them.
What Foxish Does With This
The Adyen vs Stripe fight is one of the recurring scans we run to show what AI-visibility data actually looks like at the top of a category. You can see the full result, including the exact prompts and per-model breakdown, at foxish.ai/vs/adyen-vs-stripe. You can run the same head-to-head for any two brands in any category at foxish.ai/vs.If you want the full picture on your own brand — which prompts you show up in, which competitors are eating your recommendations, and how the numbers are trending across ChatGPT, Claude, Gemini, and Perplexity — that's the core Foxish product. First scan is free, no card required.
Start a free scan →Stripe won this one 3-0. The interesting question isn't why. The interesting question is which brand in your category is quietly winning the same way, and whether you have the data to notice before it matters.