n8n vs Make: which one to choose in 2026?
The comparison in one table
Overview of the differences. The values below stay at the conceptual level: for exact prices, which change often, refer to the current official pricing pages (n8n.io/pricing and make.com/pricing).
| Criterion | n8n | Make |
|---|---|---|
| Pricing model | Per full workflow execution (one run = 1 unit, regardless of the number of steps) on the Cloud offering | Per executed module unit (historical unit "operation", billed unit "credit" since August 2025) |
| Hosting | Managed cloud or self-hosting (Docker or npm), free Community edition | Cloud only (SaaS on AWS, EU and North America regions), no self-hosting of the platform |
| AI agents / AI capabilities | AI Agent node based on LangChain, memory, tools, MCP support in both directions | Simpler visual AI agents, fast to build without development |
| Customization / code | Designed around code: Code node (JavaScript), universal HTTP Request, custom nodes | Visual no-code first, custom code possible depending on the plan, positioned second |
| Ecosystem / integrations | Catalog of native integrations plus community nodes | Larger advertised catalog of ready-to-use apps |
| Learning curve | More demanding (logic, data, sometimes code) | More accessible to non-technical profiles |
| Open-source / license | Sustainable Use License v1.0 (fair-code, source-available), some features reserved for paid editions | Proprietary SaaS software |
The app-count figures cited by each vendor come from biased marketing pages and are not reproduced here as a neutral reference.
The pricing model, the real differentiator
This is probably the point that will decide it for most projects, and it is often misunderstood.
Make bills per unit of work. Each module that runs to process a piece of data counts. The historical unit of measure is called "operation" (one executed module = one operation); since August 27, 2025, the displayed billing unit is the "credit" (for a standard module, one operation equals one credit). The direct consequence: the more steps a scenario chains together, the more it consumes. A twelve-module scenario that runs a thousand times consumes far more than a three-module scenario on the same input volume. Trigger modules only count once, and some AI or code functions may consume more than one credit per execution.
n8n Cloud bills per execution. A full workflow run counts as a single execution unit, regardless of the number of nodes traversed. A three-node workflow and a thirty-node workflow cost the same per trigger. For rich automations with many steps, this model is significantly more predictable and often more economical at equal volume.
The trade-off is therefore less a question of advertised price than a question of the shape of your workflows. Many steps per execution favor n8n. Few steps but many distinct simple scenarios can stay comfortable on Make. For exact amounts, check n8n.io/pricing and make.com/pricing at the current date, because the pricing tables change and vary depending on monthly or annual billing.
Can you self-host n8n and Make?
n8n self-hosts (Docker or npm), Make does not: Make is a cloud-only SaaS, hosted on AWS. Make is cloud only. The platform is a SaaS hosted on AWS (EU and North America regions). You do not install it on your own infrastructure. There is an "On-prem agent" for Enterprise customers, but its role is limited to reaching local applications or networks: it is not self-hosting of the platform. The advantage is the complete absence of infrastructure maintenance.
n8n can be self-hosted. In addition to the managed Cloud offering, n8n deploys on your own server via Docker (the method recommended by n8n) or via npm (Node.js 20.19 to 24.x). Without a license key, you get the Community edition, free of charge. The real cost is therefore not zero: you need a server and maintenance (updates, backups, security), but you keep your data on your own infrastructure and you are not capped by an execution counter.
Note: the Community edition does not give access to everything. Some functions (SSO, environments, external secrets, multi-main scaling, Git versioning) are reserved for paid editions under the Sustainable Use License v1.0, a fair-code source-available license that is not a classic OSI open-source license. If data confidentiality or infrastructure control is a hard constraint, n8n is the only one of the two to meet the need.
AI and agents
Both tools can call AI models. The depth differs.
n8n provides an AI Agent node based on LangChain. Since version 1.82.0, it works as a single "Tools Agent" (LangChain tool calling). It is assembled with sub-nodes: a Chat Model (OpenAI, Anthropic, Groq, Mistral, Azure, etc.), one or more Tools (at least one is required, chosen from over a hundred available tools and integrations) and, optionally, a Memory. Paired with the Chat Trigger node, it handles multi-turn dialogue. An important point to know: memory does not persist between sessions.
n8n also supports the MCP protocol (Model Context Protocol) in both directions. The "MCP Server Trigger" node turns n8n into an MCP server that exposes its tools via a URL (SSE and streamable HTTP transports, no stdio). Conversely, the "MCP Client Tool" sub-node lets an n8n AI Agent consume an external MCP server (Bearer authentication, header, or OAuth2). This opens up fairly advanced architectures of interconnected agents.
Make provides simpler visual AI agents, designed to be built quickly and without development. For a lightweight AI assistant or enrichment integrated into a no-code scenario, it is effective. For multi-step agents, RAG, or more advanced tool orchestration, n8n offers more latitude. To go further on the n8n side, see my guide to building an AI agent in n8n and the article AI agent vs chatbot.
When should you choose n8n over Make?
The choice hinges on the shape of your workflows: many steps per execution favor n8n, many simple scenarios stay comfortable on Make. There is no universal winner; the right choice depends on your profile and the shape of your automations.
Choose Make if:
- You want to get started fast, without managing infrastructure or writing code.
- Your scenarios are relatively simple (few steps per execution) and you appreciate the visual editor.
- You need a rare native integration and it is in the Make catalog.
- The complete absence of maintenance outweighs data control.
Choose n8n if:
- Your workflows have many steps: per-execution billing becomes more predictable and often cheaper at equal volume.
- You want to self-host to keep your data or control costs at high volume.
- You are comfortable with a bit of code (Code node, universal HTTP Request) or custom nodes.
- You are building advanced AI agents, RAG, or MCP architectures.
A pragmatic approach is to test a real case on both: reproduce one of your typical workflows and look at what it consumes, what it costs you in build time, and how comfortable it is to use. If you are exploring the topic more broadly, my AI automation page gathers the related resources. Make remains a credible alternative to n8n, and vice versa: the choice hinges on your concrete constraints, not on an absolute ranking.