Jan Oberhauser, the founder of n8n, used a car metaphor at the Raise Summit in Paris to explain where his company sits in the artificial‑intelligence stack. The engines, he said, are the large language models; the vehicle, the streets and the rules that let the engine take you somewhere are what n8n builds. In a market where the models dominate headlines, Oberhauser’s focus on the surrounding infrastructure is a deliberate counter‑point.
n8n describes itself as an orchestration layer for AI – the “Excel of AI,” as the founder has put it. The platform stitches together large language models, deterministic code and human sign‑off into production‑ready workflows. Crucially, n8n does not develop its own model. Instead, it remains model‑agnostic, allowing customers to plug any vendor’s model—or a self‑hosted one—into their processes. That flexibility is baked into the company’s licensing model. Since 2022, n8n runs under a Sustainable Use License, a “fair‑code” approach that keeps the source open, self‑hostable and restricts commercial use, while avoiding the tighter constraints of Apache 2.0 with a Commons Clause.
The past year turned the theoretical appeal of such freedom into concrete demand. As OpenAI, Anthropic and DeepSeek raced to release new models, enterprises scrambled to integrate them into existing operations. n8n reported more than 1,400 enterprise customers and roughly 1.7 million monthly active builders. Among the roster are Meta, Vodafone and Mercedes‑Benz, each drawn to the platform’s ability to keep sensitive data on‑premises and avoid vendor lock‑in.
Boardrooms are now asking uncomfortable questions: What happens if a model provider hikes prices, gets acquired, or loses access in a specific geography? Oberhauser warned that such dependency “could literally kill your company.” The answer, he said, lies in the ability to switch models quickly and safely. While most customers do not change models daily, they want the option. Switching can break workflows, requiring thorough evaluation before a new model is trusted. Cost at scale, latency, quality and the prospect of fine‑tuning an open‑source model all drive the desire for a portable architecture.
That narrative resonated with SAP, which invested in n8n in May 2026 at a reported $5.2 billion valuation. The deal includes embedding n8n’s visual workflow canvas inside SAP’s Joule Studio, the company’s agent‑building environment. The partnership signals a mainstream endorsement of model‑agnostic orchestration as a core component of enterprise AI strategies.
Mercedes‑Benz has also rolled out n8n as a global automation platform. The automaker cited the platform’s self‑hosted, cloud‑agnostic deployment as a key factor, ensuring that sensitive vehicle data remains within its own infrastructure while still leveraging the latest AI models.
Oberhauser’s go‑to‑market strategy hinges on patience. n8n offers the product freely, lets workloads accumulate, and waits for the moment a large organization needs single sign‑on, enterprise‑grade support and other advanced features. He pointed to research indicating that only about 5 % of corporate AI projects deliver measurable profit‑and‑loss impact, echoing MIT’s “GenAI Divide” findings. Winners, according to Oberhauser, treat AI as one component of a broader system, pairing it with deterministic logic and a final human check. The unglamorous, reliable parts of the stack, he argued, are where real value emerges.
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