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I don’t promote tools lightly. This one earned its place.
If you have ever played around with “no-code tools” like Webflow, Zappier, Make etc, you’ll enjoy this one on n8n, the ai agent + workflow automation platform of this “no-code” tool 2.0. wave.
There’s a few positive anomalies that make this company special. In May of this year SAP (europe’s largest software firm) took a stake in n8n at a $5.2 billion valuation and agreed to embed it inside its own AI product.
Which is double the $2.5 billion the company was worth in October 2025, which was already close to 9x the ~$270 million it was worth in March 2025:
The tool is pronounced “n-eight-n” (I miss-pronounced it like 7x on this podcast). If you’ve built anything with AI agents in the last year you’ve probably used it. But when I ask around almost nobody knows how it grew (the founder Jan Oberhauser barely does interviews).
For its first 5 years n8n was in Jan’s own words“growing okay. Not outstanding. But it felt solid.” Then it rebuilt itself in 6 weeks and 4x’d in 8 months. I spent the past week pulling apart every founder interview, funding round, blog post, and filings I could find on how they grew.
What you’ll learn
How they grew from a side project to $100M+ ARR and a $5.2B valuation
The 6-week rebuild that 4x’d their revenue in 8 months
Why they stayed neutral on every AI model and how that became a growth engine
How giving the product away for free is what won the enterprise
Why they deliberately made it harder to learn than Zapier
The “fair-code” license fight that kept developers building on it
📐 Quick note on editorial and methodology: this is me surfacing the 80/20 positive anomalies that explain n8n’s growth (my subjective read, not a comprehensive profile, and not an endorsement or investment advice). It draws on the founder interviews that exist (Sequoia, Accel, etc.) plus n8n’s own posts, Sacra, and reporting from Bloomberg, TechCrunch etc. n8n does not disclose revenue, so the ARR figures here are Sacra estimates or the founder’s own growth multiples. Treat directional estimates as directional.
A little about how this market evolved before we get to the 8 growth levers:
Where we come from
For most of the last decade automating work between apps meant one of 2 things:
You paid Zapier by the task to connect your SaaS tools with simple if-this-then-that rules
You paid an enterprise integration platform like Workato or UiPath a lot more to wire your systems together with the help of a consultant.
Both worked but they both also had a ceiling because simple tools were easy to start with and fell apart the moment your workflow got complicated. The enterprise tools were powerful and priced for a procurement cycle (left curious devs out).
n8n started in the gap between them. Jan’s framing of why those simpler tools kept disappointing people is interesting:
“The problem with these no-code low-code tools was always that they seem amazing in the beginning, you get a lot of value out of them, but when you want to bring it in production, you’re at 80% and you think ‘I need one or two more things’ and then you’re suddenly stuck. And then all the time you saved, you pay back 5 times to actually get it to production.”
Where we are
Then ChatGPT happened and the market split:
On one side are the model labs and agent builders that want to own the intelligence.
On the other side a fast-growing pile of vertical AI apps (each solving typically one narrow job)
And sitting between them is a question nobody had a clean answer for in 2023 which is, once you have 10 AI tools and 5 models and a dozen data sources:
what actually connects them and runsthe thing in production?
Which is the “connective layer” where n8n positioned itself. According to Sacra, the company today in 2026 serves 1,400+ enterprise customers and supports a community of 1.7 million monthly active builders, with a revenue mix estimated at roughly 55% cloud subscriptions, 30% enterprise licenses, and 15% embedded partnerships. More than 80% of workflows built on the platform now involve AI agents.
Where the market is going
3 things will shape the next couple of years:
The work splits in 2: Jan’s distinction is that some enterprise work is deterministic where there is one correct outcome and anything else is a mistake (compliance checks, billing, data updates). The rest is non-deterministic where judgment and context shape the answer (triaging a ticket, drafting copy, deciding what to do with an anomaly etc). Most AI tools handle one or the other but n8n is supposedly built to mix code, rules, and agents in the same workflow.
Protocols standardize the plumbing: they call Model Context Protocol “the HTTP of AI workflows,” and sees n8n as the orchestration layer between MCP-connected services, agents, and tools.
The hyperscalers circle: Sacra flags the risks here that I tend to agree as AWS, Google, and Microsoft can bundle workflow automation into their AI services and subsidize it. The counter-move as we’ll see is the SAP deal and a self-hosting story the hyperscalers might struggle to copy.
Now lets dive into how they grew.
Act 1: The Lego Box
Oct 2019 → 2022 · $0 → “growing okay, not outstanding”
n8n website in early 2020
n8n started with a visual effects artist who was bored of doing the same thing twice.
Jan was a compositor (the person who combines the computer-generated 3D with the filmed footage and makes it look real). He got bored, moved into a more technical role as a pipeline technical director and spent his days building tools to make the artists around him faster.
Which is the job-to-be-done the company seems to comes from:
“Those people [were] very smart, very well paid and quite technical, but they were always reliant on me or other people like myself to actually do the things they wanted to be done. They could have had so much more impact if they would have been empowered, and they weren’t. I think n8n is doing exactly that.”
At a later startup he realized he was spending most of his own time rebuilding things that already existed:
“I spent probably 90% of my time reimplementing things that have been implemented before. Get something on GitHub, send a message to Slack. Each of those pieces have been implemented millions of times before by almost every developer out there, and this is never the most fun thing to do.”
So he set out to build a box of reusable Lego blocks for that plumbing, and free himself to spend time on the part that was actually specific to his problem.
He built n8n nights and weekends across roughly a year while working at a second startup to pay rent because as he puts it plainly, “I already had wife and one child back then, so I obviously needed some food and some shelter as well.”
He’d started coding at the beginning of 2018, a soft launch came in June 2019, the proper one in October. The name is actually a numeronym for “nodemation” (node plus automation), the 8 standing in for the 8 letters between the two n’s.
He’d been invited to Y Combinator and turned it down because he didn’t want to leave Berlin or incorporate in Delaware. Sequoia and firstminute co-led a $1.5M seed in March 2020 anyway, with Felicis leading a $12M Series A a year later. By that point the community was around 16,000 developers.
Growth Lever 1: They gave the product away and treated support as the product.
“I knew the product was very imperfect when I launched. The only way you can still make sure people are happy is if you make sure they have the best experience possible when they run into one of those issues.”
The product was free and self-hostable from the start.
One of the first people Jan he hired was a community person, before it made any obvious financial sense, purely to write documentation, run the forum, answer questions fast etc. He chose a forum over a Slack or Discord on purpose:
“These people have a problem, I answer that question once, maybe 3 times. At least I don’t have to answer it 50 times. That allowed us to scale.”
The bet was that a free product with rough edges plus genuinely fast, genuinely caring support produces users who leave a problem feeling better than if they’d never hit it because they now trust that someone will unblock them, and those users tell other people, they write tutorials, build integrations etc (similar flywheel to the one we ran at Facebook, explained in this playbook).
The clearest example is a developer named Ricardo, based in the US, who found n8n on Product Hunt at launch, and built one integration for fun. Jan reviewed it and gave feedback, he built another, and another. In the end he contributed 56 integrations before n8n had the money to pay him. As soon as it did, Jan hired him and he’s still there, and was for a long time the only n8n engineer in the US.
Growth Lever 2: They made the product harder to learn (on purpose)
“We have a steeper learning curve. You can only make the product so simple, because if you make it even simpler, you have to give something up for it, which is flexibility and power. And that was always at the center of n8n.” (Jan, Accel)
Everyone else in the last automation wave chased the same goal which was to get a beginner to their first working automation in 5 minutes. But n8n targeted the other end which is the moment a workflow gets complicated.
An example of this, under the drag-and-drop canvas there’s a real code editor, so when you hit something the buttons can’t do, you write a line of JavaScript or Python and keep going, whereas Zapier and Make lock the code away and cap you at what their menus allow (so n8n leaves the door open lets say).
It matters for growth because every easy tool tends to have the same leak, which is it wins you in week one, you build something real, you hit its ceiling (the 80% trap), and you move to a “proper” platform, so the tool that got you loses you right when you technically became “valuable”.
I think their goal was to set the ceiling is high enough that you never need to leave, so:
A solo builder and a 500-person enterprise can technically use the same product.
The people outgrowing Zapier and Make now move to n8n because there’s nothing left to “outgrow”.
And the same canvas that sends for example your first Slack alerts later can run your company’s AI agents (or at least that’s their plan now).
As an example, imagine a developer finds n8n on GitHub, runs it for free, builds something real, then pushes their company to pay for it as usage grows. The harder learning curve filters for these people who are technical enough to build serious things and at the end of the day the ones who can get a tool into a Fortune 500.
Growth Lever 3: They never changed the rules on the community.
"I'm not building n8n and giving it away for free because I'm a good person. I actually want to build a business around it. I want to make sure I can get paid, and all the other people can get paid as well." (Jan, Sequoia)
n8n is one of the most-starred projects on GitHub (194,000 stars, roughly a top-40 project globally), and Jan is careful to never call it open source.
It didn’t start clean though as n8n launched under Apache 2.0 with a Commons Clause modifier, which broke the technical definition of open source and made some people unhappy. But instead of fudging it, the founder dropped the words “open source” and coined his own term, “fair code.”
What fair code actually means:
The code is visible and free to use, even in production, even inside a Fortune 500.
The one thing you can’t do is take it and sell a hosted version.
It is a growth lever because a developer won’t pour months into building on a free tool, contributing integrations, and getting their company to standardize on it unless they trust the rules won’t change once they’re locked in.
And by 2023 developers had every reason to expect the opposite because a run of open-source companies (i.e. MongoDB, HashiCorp, Redis and others) got big then quietly changed their license to block cloud providers from reselling them and their communities turned on them.
“People didn’t hate it because of the license the companies chose. People were mainly angry because the company changed the rules. So I thought, I’m just very honest and upfront about it from the very beginning.” (Jan, Sequoia)
I imagine that honesty was an unlock because the founder said out loud that n8n was a business and would stay free under clear terms, so developers built, contributed, and standardized without watching their backs. So the free tier stayed genuinely free, the commercial license funded everything, and there was no betrayal in between.
Act 2: Six Weeks
2023 → Dec 2024 · the rebuild that changed everything
By early 2023 n8n was a solid, growing, community-loved dev tool.
Then Jan got scared:
“When AI came up the first time, I honestly was a bit scared, because what was clear is it’s changing the whole game. Things I just months before thought would be impossible suddenly became very easy.” (Jan, Accel)
What tipped him off was seeing Pinecone raise $30M, which made them ask why a vector database was suddenly so interesting.
“What they were in the past, they were the vector database. What they became, they became a deep database for AI. That’s where I said we have to do something very similar within n8n.”
Growth Lever 4: They spent 6 weeks turning n8n into the place you build AI.
![Seed $1.5M (Sequoia + firstminute, Mar 2020) · Series A $12M (Felicis, Apr 2021) · Series B €55M (Highland Europe, Mar 2025) · Series C $180M (Accel, Oct 2025, $2.5B) · SAP strategic (May 2026, $5.2B). Total raised $240M. — source: Crunchbase / n8n]](https://substackcdn.com/image/fetch/$s_!_0EW!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F03dabaee-1550-4574-816c-39221f8ac288_1688x882.png "Seed $1.5M (Sequoia + firstminute, Mar 2020) · Series A $12M (Felicis, Apr 2021) · Series B €55M (Highland Europe, Mar 2025) · Series C $180M (Accel, Oct 2025, $2.5B) · SAP strategic (May 2026, $5.2B). Total raised $240M. — source: Crunchbase / n8n]")
