Companies that use N8N

Source: Analysis of 100 job postings that mention N8N

My analysis shows that N8N buyers are predominantly technical leaders in data, operations, and engineering roles. Directors of Data/AI represent 18% of hiring, Revenue Operations Managers make up 15%, and Heads of Engineering account for 12%. These leaders are driving automation and AI transformation across their organizations, prioritizing what one posting calls building "the automation, data, and AI initiatives to transform agency operations." They're hiring for roles that bridge business needs with technical execution, focusing on scalable growth infrastructure.

The day-to-day users span automation specialists, full-stack developers, and business engineers who implement workflows connecting multiple systems. I noticed practitioners are building solutions that one company describes as using "Make.com, n8n" alongside traditional development stacks. They're creating what another posting calls "automated, AI-enhanced engagement" across CRM systems, marketing platforms, and internal tools. The hands-on work involves API integrations, webhook configurations, and designing workflows that eliminate manual processes across sales, customer success, and operations teams.

The driving pain point is clear: organizations want to "automate routine tasks" and achieve what multiple postings describe as reducing "manual work" while scaling operations. One role specifically seeks someone to "turn manual, repetitive workflows into reliable automations," while another emphasizes building "scalable, intelligent capabilities that directly improve business outcomes." Companies are trying to make "AIが動ける環境" (environments where AI can operate) and create systems where, as one posting states, "what used to take months or years to build now takes just days."

Source: Analysis of tech stacks from 12,270 companies that use N8N

I noticed that N8N users are deeply technical companies that prioritize self-hosted, open-source infrastructure over commercial SaaS alternatives. The presence of Portainer, GitLab, and Grafana at such high correlation rates tells me these are teams that want full control over their tooling and aren't afraid to run their own infrastructure. They're building custom solutions rather than buying off-the-shelf products.

The pairing of Portainer with N8N is particularly revealing. Portainer is a container management tool, which means these companies are running Docker environments and likely managing multiple services. They're using N8N to automate workflows between these self-hosted services. The Grafana correlation makes perfect sense too because if you're running your own infrastructure, you need robust monitoring. These companies are connecting N8N to their observability stack to automate alerts and responses. The GitLab presence suggests they're treating automation workflows as code, version controlling their N8N configurations just like they would application code.

The full stack reveals engineering-led companies, likely in the Series A to Series B range or even bootstrapped. They're product-led in their growth motion because they value tools that integrate deeply with developer workflows. The appearance of HuggingFace suggests many are in AI or data-intensive spaces where they need flexible automation between ML models and other systems. These aren't companies buying enterprise sales automation platforms. They're technical teams building their own operational infrastructure.

Source: Analysis of Linkedin bios of 12,270 companies that use N8N

I noticed that N8N users span an incredibly wide range of industries, but they share a common thread: they're operational businesses dealing with real-world complexity. These aren't pure software companies building a single product. They're IT consultancies managing client projects, healthcare providers coordinating patient care, financial services firms processing applications, manufacturing companies running supply chains, and marketing agencies juggling multiple campaigns. What unites them is the need to connect disparate systems and automate workflows across their operations.

The employee counts tell an important story. The majority fall between 11-200 employees, with a sweet spot around 50-. These are established businesses past the survival stage but not yet enterprise scale. When funding information appears, it's typically Series A or seed stage, suggesting growing companies with validated business models. Very few are early startups or massive corporations. They're in that challenging middle phase where manual processes start breaking down but enterprise software feels too rigid or expensive.