Side-by-Side Comparison
When to choose n8n
- ▸Data sovereignty is a requirement — n8n's self-hosted deployment keeps all data within your own infrastructure.
- ▸Native AI Agent nodes with LLM integration allow you to build genuinely intelligent, decision-making workflows.
- ▸Custom code nodes let developers write JavaScript or Python inline when visual nodes cannot express the required logic.
- ▸Cost at scale is a concern — n8n's self-hosted model has no per-operation pricing, making it significantly cheaper at high workflow volumes.
- ▸You need deeper API flexibility, webhook customisation, or integration with niche or internal systems not covered by SaaS connectors.
When to choose Make
- ▸No infrastructure management is required — Make is fully cloud-hosted with no setup, maintenance, or DevOps overhead.
- ▸Make's broader no-code connector library covers more SaaS tools out of the box, particularly for marketing and sales stacks.
- ▸Non-technical operators need to maintain and modify workflows without developer involvement.
- ▸Faster setup is the priority for simpler integrations where the visual builder and pre-built modules are sufficient.
- ▸Your workflows are primarily marketing automation, CRM sync, or lead routing where Make's ecosystem is particularly strong.
Find n8n and Make Agencies
When evaluating an automation agency's n8n versus Make recommendation, be cautious of agencies that default to one platform without asking about your team's technical capacity and data sensitivity. A good agency will recommend n8n when your workflows need AI agent logic, custom code, or data sovereignty, and Make when your team is non-technical and needs fast no-code automation. If they don't ask these questions first, that's a red flag.
Which has more agencies?
In our directory, there are currently 94 n8n agencies and 0 Make agencies. n8n leads the directory — reflecting its longer history and broader ecosystem adoption. However, Make agency numbers are growing as the framework matures.
Bottom line
n8n is the stronger choice for AI-native, developer-friendly workflows where customisation, data control, and cost at scale matter — particularly when AI Agent nodes or custom code logic are required. Make is better suited for non-technical teams needing fast, no-code automation with broad SaaS integrations where infrastructure management would be a barrier. The decision often maps directly to your team's technical capacity and your data sensitivity requirements.