n8n vs Zapier vs Make: Workflow Automation 2026

TL;DR

Zapier wins on integrations: 7,000+ app connectors and the most beginner-friendly no-code experience. You pay for convenience — $19.99/month gets you 750 tasks, and costs compound fast. Make (formerly Integromat) sits in the middle: a powerful visual scenario builder with 1,000+ apps and granular control over data routing at lower prices than Zapier. n8n is the open-source option: self-host for free, run unlimited workflows, and write actual JavaScript at every node. The 2026 AI workflow story strongly favors n8n — it ships native LLM agent nodes, MCP tool support, and full LangChain integration that neither Zapier nor Make can match for developer use cases.

Quick Comparison

	n8n	Zapier	Make
License	Sustainable Use (fair-code)	Proprietary	Proprietary
Self-Host	Yes (free)	No	No
GitHub Stars	183K+	—	—
Integrations	1,200+ nodes	7,000+ apps	3,000+ apps
Starting Price	€24/mo cloud (self-host free)	$19.99/mo	$10.59/mo
Task-Based Pricing	No	Yes	Yes (operations)
AI Agent Nodes	Native (LangChain, MCP)	Limited (AI builder)	Limited
Code Support	JavaScript/Python at every node	None	Limited
Visual Builder	Yes (canvas)	Yes (linear)	Yes (canvas)
Best For	Developers, technical teams	Non-technical users	Power users needing value

Pricing: The Real Cost at Scale

Zapier and Make both use execution-based pricing — every API call, data transformation, and webhook trigger counts against your monthly limit. This model creates an invisible tax on automation: the more useful your workflows become, the more they cost.

Zapier

• Free: 100 tasks/month, single-step Zaps only
• Professional: $19.99/month (billed annually) for 750 tasks, or $29.99/month billed monthly
• Team: $69/month (billed annually) for 2,000 tasks
• Enterprise: Custom pricing, starting north of $799/month

A workflow that polls an API every 15 minutes burns 2,880 tasks per month alone. At that rate, the Professional plan ($19.99/month) runs out in a single workflow. Three active polling workflows push you into the Team tier. Zapier's 2025 price increase — up 20-40% across tiers — made the math even less forgiving.

Make

• Free: 1,000 operations/month, 2 active scenarios
• Core: $10.59/month for 10,000 operations, unlimited active scenarios
• Pro: $18.82/month for 10,000 operations + priority execution and full-text execution search
• Teams: $34.12/month with multi-user access and shared scenarios

Make offers genuinely better value than Zapier at comparable usage levels. The $10.59/month Core plan's 10,000 operations give you roughly 13x more headroom than Zapier Professional at a lower price. For teams running complex multi-step scenarios, Make's operation counting is also more granular — it doesn't count every sub-operation the same way Zapier tasks work.

n8n

• Self-hosted Community: Free, unlimited workflows, unlimited executions
• Cloud Starter: €24/month (~$26), 2,500 workflow executions
• Cloud Pro: €60/month (~$65), 10,000 executions, version control
• Enterprise: Custom (SSO, audit logs, dedicated support)

For self-hosting, a $6/month Hetzner VPS or $12/month DigitalOcean Droplet runs n8n comfortably for small teams. Infrastructure cost replaces subscription cost — and that infrastructure cost doesn't scale with workflow executions. A team running 100,000 workflow executions per month pays the same server bill as a team running 10,000.

Integrations and Workflow Power

Zapier: Unmatched Ecosystem

Zapier's 7,000+ app integrations remain its strongest selling point. If you need to connect two SaaS tools — any two SaaS tools — Zapier almost certainly has a pre-built connector. The no-code builder works through a simple linear model: one trigger, one or more actions. Multi-step Zaps, conditional paths (Filters), and data manipulation (Formatter) are available on paid plans.

Zapier's integration quality is also consistent. Because Zapier works directly with SaaS vendors to build and maintain connectors, the Gmail integration works reliably, the Salesforce connector is certified, and new apps ship quickly when vendors want distribution.

The limitation is the same as the strength: Zapier is optimized for connecting SaaS tools. It's not a general-purpose automation platform. Running custom code inside a Zap is limited (Code by Zapier runs JavaScript/Python in a sandboxed environment but with tight constraints). Complex data transformations get awkward fast.

Make: Visual Power for Complex Scenarios

Make's canvas-based visual builder is more powerful than Zapier's linear model. Scenarios can include parallel branches, error handlers, iterators, and aggregators — all visualized on a diagram you can see and modify. For teams that need to build multi-path conditional logic, Make's interface makes the complexity manageable.

Make's 3,000+ app connectors cover the major tools: Google Workspace, Slack, HubSpot, Shopify, Notion, Airtable, and hundreds more. The HTTP module lets you connect any REST API not covered by a native connector. The Data Store feature (a lightweight built-in database) enables stateful workflows without needing an external database.

Where Make falls short: no self-hosting option, limited custom code support (Basic JS in a single node), and an interface that takes longer to learn than Zapier's linear builder. New users often find the canvas overwhelming before they find it empowering.

n8n: 1,000 Nodes and Custom Code Everywhere

n8n's 1,200+ nodes include both pre-built integrations and infrastructure-level primitives: HTTP Request, Code (JavaScript/Python), Split In Batches, Merge, Set, Filter, and more. Every workflow is a visual canvas, but unlike Zapier, you can drop a Code node anywhere in the flow and write full JavaScript or Python — complete with npm packages and Python libraries.

The HTTP Request node effectively gives n8n the same reach as Zapier's full ecosystem. Any REST API, any webhook, any authentication flow is achievable without waiting for an official connector. For developer teams that regularly need to talk to internal APIs, custom data pipelines, or services without official Zapier connectors, this is a qualitative difference.

n8n 2.0 (released early 2026) added a redesigned canvas, improved version control via git, and native debugging tools that let you step through executions node by node. The community template library has grown to 2,000+ workflow templates covering common integration patterns.

AI and Agentic Workflows in 2026

This is where the gap between n8n and its competitors widened most in 2026.

n8n: Native AI Agent Nodes

n8n ships a full AI agent subsystem built on LangChain. The AI Agent node supports:

• LLM connectors: OpenAI, Anthropic Claude, Google Gemini, Mistral, Ollama (local models), and any OpenAI-compatible API
• Memory: conversation memory, window buffer memory, vector store memory (Pinecone, Qdrant, Supabase Vector, PGVector)
• Tools: any n8n node can become a tool that an AI agent calls — including HTTP requests, database queries, and custom code
• MCP support: n8n 2026 added Model Context Protocol client support, letting agents call external MCP servers directly from the workflow

A typical n8n AI workflow might look like: webhook triggers → AI agent reads a Slack message → queries a PostgreSQL database as a tool → summarizes with Claude Sonnet → posts reply to Slack. All built visually, with the ability to inspect every node's input/output during debugging.

Zapier: AI Builder and Canvas

Zapier launched its AI-powered Zap builder in 2025 — you describe what you want in plain English and Zapier suggests a workflow. The Interfaces product (form/app builder), Tables (lightweight database), and Canvases (flow diagram) round out what Zapier calls its "platform for AI-powered apps."

The AI story is stronger for non-technical users who want to use AI to build automations rather than build AI agents. Zapier's OpenAI and Anthropic integrations exist but are action-based (send a prompt, get a response) rather than the agentic loop pattern n8n enables natively.

Make: AI Assistance, Limited Depth

Make added AI scenario suggestions and an AI assistant that helps you find the right modules for your use case. The OpenAI and Claude integrations work as single-step modules within scenarios. For teams that want to add AI responses to their existing Make workflows, this works fine. For teams that want to build autonomous AI agents with memory, tool use, and multi-step reasoning, Make's architecture isn't designed for it.

Self-Hosting n8n vs Paying for Zapier or Make

Self-hosting is a key reason technical teams choose n8n. See our full guide to self-hosting n8n for a complete Docker Compose setup.

A minimal production deployment runs on:

version: "3.8"
services:
  n8n:
    image: n8nio/n8n
    restart: always
    ports:
      - "5678:5678"
    environment:
      - N8N_ENCRYPTION_KEY=your-encryption-key
      - DB_TYPE=postgresdb
      - DB_POSTGRESDB_HOST=postgres
      - DB_POSTGRESDB_DATABASE=n8n
      - DB_POSTGRESDB_USER=n8n
      - DB_POSTGRESDB_PASSWORD=your-password
      - WEBHOOK_URL=https://n8n.yourdomain.com
    volumes:
      - n8n_data:/home/node/.n8n
    depends_on:
      - postgres

  postgres:
    image: postgres:15
    restart: always
    environment:
      - POSTGRES_USER=n8n
      - POSTGRES_PASSWORD=your-password
      - POSTGRES_DB=n8n
    volumes:
      - postgres_data:/var/lib/postgresql/data

volumes:
  n8n_data:
  postgres_data:

With a reverse proxy and SSL, n8n is production-ready on a $12/month VPS. All workflow history, credentials, and executions stay on your infrastructure.

Neither Zapier nor Make offers a self-hosted option. For teams with compliance requirements — HIPAA, GDPR, SOC 2 — that need sensitive data to stay inside their infrastructure, this is a disqualifying constraint for cloud-only tools. n8n's self-hosted Community Edition stores all workflow data on your own servers, and credentials are encrypted at rest with a key you control.

If migrating from Zapier to n8n, see our step-by-step migration guide.

When to Use Which

Choose Zapier if:

• You're connecting well-known SaaS apps and need the broadest connector library
• Your team is non-technical and needs a simple trigger/action model
• You're building automations for clients using mainstream tools (Gmail, Salesforce, HubSpot, Slack)
• You need a Zap running in 10 minutes with zero infrastructure setup
• Budget isn't a primary constraint at your workflow volume

Choose Make if:

• You need visual multi-branch conditional logic without writing code
• You're between Zapier's pricing and n8n's learning curve
• You need more than single-step workflows but don't want to manage servers
• Your use case involves complex data routing, iterators, or aggregations
• The $10.59/month Core plan covers your operation volume

Choose n8n if:

• You're building AI agent workflows with LLM reasoning, memory, and tool use
• Your team writes JavaScript/Python and wants code-level control inside workflows
• You need to connect internal APIs, databases, or services without official connectors
• Data sovereignty matters — HIPAA, GDPR, or internal compliance requirements
• You're running high execution volumes where per-task pricing gets expensive
• You want to self-host on your own infrastructure at no per-execution cost

For teams already using other open-source automation tools, see our n8n vs Activepieces comparison and our roundup of the best Zapier alternatives in 2026 for a broader view of the ecosystem.

Developer Experience and Debugging

The tools diverge significantly in how they handle errors and debugging — a practical concern when automations break in production.

Zapier shows task history with a pass/fail status per step. Replaying failed tasks is straightforward. Error handling is limited to stop-on-error or continue-on-error — there's no native catch/fallback branch model. Monitoring is via email alerts and the Task History panel. For simple automations that rarely fail, this is adequate.

Make has the best error-handling model of the three. Every scenario supports dedicated error-handling routes: you can define what happens when any specific module fails, route failures to a separate branch, and use the Ignore/Break/Rollback options to control transaction semantics. The scenario execution log shows a full diagram replay with each module's input and output data, making it significantly easier to trace data transformation bugs than Zapier's linear log.

n8n added step-through debugging in 2026 — you can pin data to a node, run the workflow up to that point, and inspect live input/output at each node. Error workflows are a built-in concept: define a dedicated workflow that fires when any other workflow fails, with full access to the failure context. This makes it straightforward to route errors to PagerDuty, post to a Slack ops channel, or log structured errors to a database. For engineering teams running production automation pipelines, n8n's error handling is the most composable of the three.

License Considerations

n8n's Sustainable Use License (also called fair-code) is not fully open source by the OSI definition. The key restriction: you cannot use n8n to build a commercial automation-as-a-service product that you sell to others. Internal use, self-hosting for your own team, and building workflows for your own products are all unrestricted.

If you need a fully permissive license for a commercial automation product, consider Activepieces (MIT) or Windmill (AGPL-3.0). For the vast majority of teams — using n8n to automate their own operations — the Sustainable Use License imposes no practical constraints.

Zapier and Make are proprietary with no source-available components.