Make vs. Zapier vs. n8n: Which Automation Platform for AI Workflows?

Quick Verdict: Make wins on value for complex workflows. Zapier wins on simplicity and app coverage. n8n wins on cost at scale and AI depth — but you need technical chops to unlock it.

Choosing between Make vs. Zapier vs. n8n is one of the most consequential tooling decisions a team can make — and it's gotten significantly more complicated now that all three platforms have added AI agent capabilities. What used to be a simple "power vs. ease" tradeoff is now a three-way choice across pricing model, AI philosophy, data sovereignty, and infrastructure overhead.

This comparison cuts through the marketing to give you the actual numbers, the hidden gotchas, and a clear decision guide based on your specific situation. Whether you're building AI-powered workflow automation for a team or running complex data pipelines solo, there's a right answer here — and it's probably not the one you'd guess.

The 60-Second Comparison

Before diving deep, here's where each platform stands:

Make (formerly Integromat)

Make is the visual powerhouse of the three. Built on a canvas-based interface, it lets you map data flows, add branching logic, and handle parallel processing in a way that's genuinely fun to diagram out — once you get past the initial learning curve.

Pricing Model

Make charges by operations — each module action inside a scenario counts as one operation. A 10-step scenario run once uses 10 operations. This makes pricing far more predictable for complex workflows than Zapier's task-per-step model.

• Free: 1,000 ops/month, 2 active scenarios, 15-minute minimum scheduling
• Core ($9/mo): 10,000 ops, unlimited scenarios, 1-minute scheduling, API access
• Pro ($16/mo): Priority execution, custom variables, full execution log search
• Teams ($29/mo): Team management, shared templates, user roles
• Enterprise: Custom pricing with SLA and dedicated support

Unused operations roll over on paid plans — a thoughtful touch for teams with variable workflow volumes.

AI Capabilities

Make's AI story is built around Maia, its natural-language scenario builder that lets you describe what you want to automate and generates the workflow structure. Beyond that, Make launched a dedicated AI Agent builder (currently in beta) that shows every agent decision step in its visual debugger — giving non-technical teams actual insight into what the AI is doing.

The visual transparency is Make's genuine differentiator in AI workflows. You can see exactly where an agent branched, what data it processed, and where it failed. Zapier's agents are more of a black box; n8n's are more powerful but require JavaScript comfort to configure.

Zapier

Zapier invented the "Zap" — trigger + action — and has dominated no-code automation for over a decade. Its 8,000+ integrations are unmatched, and its linear wizard interface is genuinely the fastest way to connect two apps for a non-technical user.

Pricing Model

Zapier charges per task, where every individual action step in a Zap counts as one task. This is the critical gotcha: a 5-step Zap running 100 times = 500 tasks. AI workflows tend to be 10-20 steps, which means Zapier's pricing scales brutally with complexity.

• Free: 100 tasks/month, 2-step Zaps only
• Professional ($19.99/mo): 750 tasks/month, unlimited multi-step Zaps
• Team ($69/mo): 2,000 tasks/month, SSO, shared folders, user roles
• Enterprise: Custom — governance tools, usage analytics, dedicated support

The task model is fine for simple 2-3 step automations. For AI-heavy workflows, the math gets painful fast (more on this below).

AI Capabilities

Zapier Agents is the platform's bet on AI — autonomous agents that can reason and act across its 8,000+ app catalog. You can connect an agent to Gmail, Slack, Salesforce, and 7,997 other apps with minimal setup. AI steps using GPT-4o are available on all paid plans.

The tradeoff is transparency. Zapier's AI agent reasoning is largely opaque — you get the output but limited visibility into the decision chain. For teams that need audit trails or want to tune agent behavior at the prompt level, this is a real limitation.

Zapier also includes MCP (Model Context Protocol) server access on all plans — a useful bridge for teams integrating Zapier tools into Claude or other AI clients.

n8n

n8n is the automation platform for developers who want total control. Its node-based canvas is as expressive as Make's but adds full JavaScript/Python code execution, native LangChain integration for AI agents, and — crucially — complete self-hosting capability.

Pricing Model

n8n charges by execution on its cloud plans — one complete workflow run = one execution, regardless of how many steps that workflow contains. A 20-node AI agent workflow running 1,000 times costs 1,000 executions, not 20,000 tasks.

• Self-hosted (Community Edition): Completely free. Unlimited executions, unlimited workflows, unlimited users. You pay only for infrastructure (~$10–20/month for a VPS with Postgres).
• Cloud Starter ($24/mo): 2,500 executions/month, 5 concurrent executions
• Cloud Pro ($60/mo): 10,000 executions/month, role-based access, global variables
• Cloud Business ($800/mo): 50,000 executions/month, Git version control, SSO
• Enterprise: Custom — on-premises, HIPAA/SOC2, SLA

The execution-based pricing makes n8n dramatically cheaper than Zapier for complex workflows. See our n8n AI agents guide for a deep dive into the platform's AI stack.

AI Capabilities

n8n's AI capabilities are the deepest of the three. Version 2.0 (launched January 2026) added 70+ dedicated AI nodes, including:

• LangChain integration — first-class, not bolt-on. Agent nodes, chain nodes, tool-calling
• Vector Store nodes — Pinecone, Qdrant, Supabase for RAG workflows
• Tool Nodes — let agents call n8n workflows as tools (powerful for multi-agent orchestration)
• Persistent memory — Redis or Postgres backends for long-running agents
• Self-hosted LLM support — Ollama, LM Studio, any OpenAI-compatible API
• Human-in-the-loop nodes — pause workflows for human approval before critical actions

For teams building serious AI automation workflows, n8n's LangChain-native approach is the most flexible and cost-effective option — if you have the technical team to wield it.

The Fair-Code Licensing Trap

This is the gotcha most articles skip: n8n is not truly open source.

n8n uses a "Sustainable Use License" — visible source code you can fork and self-host, but with restrictions. You cannot embed n8n in a SaaS product or resell it as an automation service without an enterprise license. For internal business use, you're completely fine. But if you're an agency building client automation portals, or a startup planning to offer n8n-powered workflows to paying customers, you'll need the enterprise tier.

The Pricing Reality for AI Workflows

Here's the math that most comparisons skip. Let's price out a real AI workflow: a 15-step customer support automation that runs 10,000 times per month.

The gap is not subtle. Zapier costs 10–40× more than the alternatives for multi-step AI workflows. The task-per-step pricing model was designed for simple 2-3 step Zaps — it becomes punishing at AI workflow complexity levels.

Ease of Use: Honest Assessment

Zapier is genuinely the easiest. If your team has never used an automation tool, start here. The trigger-action wizard holds your hand through the process, error messages are readable, and the app library means the app you need is almost certainly already there.

Make has a moderate learning curve. The canvas is intuitive once you get the mental model, but configuring data mapping and branching logic for the first time takes a few hours. The payoff is a much clearer view of how data flows through your scenario — especially useful for diagnosing problems.

n8n requires technical comfort. Self-hosting alone involves Docker, Nginx reverse proxy, SSL certificates, Postgres setup, and ongoing maintenance. The visual editor is powerful but the terminology (nodes, credentials, expressions) assumes familiarity with APIs and data structures. Budget 1-2 days to get comfortable, and more for complex AI agent setups.

Self-Hosting & Data Sovereignty

This is n8n's most distinctive advantage. If your data cannot leave your infrastructure — healthcare (HIPAA), finance, legal, or EU compliance (GDPR strict interpretation) — n8n self-hosted is the only option among the three.

Make and Zapier are cloud-only. Both process your workflow data on their servers, which raises compliance questions for sensitive use cases. Both have SOC 2 Type II certifications, but the data still transits through their infrastructure.

For teams working with customer PII, medical records, financial data, or proprietary internal data, n8n self-hosted eliminates this exposure entirely. Pair it with a self-hosted LLM (via Ollama) and you have an AI workflow system where literally nothing leaves your network.

This is the same reason AI agents vs. traditional automation discussions increasingly focus on data residency — AI workflows process more sensitive data than simple trigger-action automations ever did.

Which One Should You Choose?

Choose Zapier if:

• You need to connect to an obscure SaaS app quickly (8,000+ integrations)
• Your team is non-technical and needs to be productive same day
• Workflows are simple (2-5 steps) and run infrequently
• The budget isn't a concern at your scale

Choose Make if:

• You need visual workflow complexity without DevOps overhead
• You want AI capabilities without requiring a developer to configure them
• You're on a budget but can't self-host
• Workflows have branching logic, data transformation, or parallel paths

Choose n8n if:

• You have technical staff comfortable with Docker and APIs
• Data sovereignty is a requirement (self-hosted only)
• You're building serious AI agent pipelines with LangChain, RAG, or local LLMs
• You're running high-volume workflows where per-step pricing would be costly
• You need Git-based version control for workflow definitions

For AI-Specific Workflows: A Deeper Look

If your primary use case is AI agent orchestration — not just connecting apps — the comparison looks different. The question isn't just "which tool can call an LLM?" but "which tool gives you the control, transparency, and cost structure to run AI workflows reliably at scale?"

For teams building AI workflow automation pipelines with memory, tool-calling, RAG, and human-in-the-loop checkpoints:

• n8n is the clear technical leader — LangChain-native, vector stores, persistent memory, self-hosted LLMs
• Make is the best non-technical option — visual transparency, step-by-step AI agent debugging
• Zapier is the easiest entry point — but loses transparency and cost control as AI workflows grow

The convergence is real: all three platforms are racing toward AI-native features. But for now, n8n's 18-month head start on LangChain integration and its execution-based pricing model give it a structural advantage for serious AI workflow work. According to G2's Make vs. Zapier comparison, user satisfaction skews toward Make for complex workflows even before AI enters the picture — a trend that accelerates as workflow complexity increases.

Get Started

Automation platform choice is a long-term commitment — migration costs are real and switching friction is high. Pick deliberately.

If you're a solo developer who wants a self-hosted AI agent without the workflow overhead, GoGogot is worth evaluating — one Docker command, 27 built-in tools, open-source MIT license, and about $0.02 per session with DeepSeek.

If you're on a team that needs shared AI agent context, collaborative workflows, and no infrastructure to manage, cowork.ink is built for exactly that — a shared AI workspace where your whole engineering team works with the same agents and context, without prompt gymnastics in personal chats.

For a broader view of the automation landscape beyond these three platforms, see our comparison of the best AI automation tools.

Frequently Asked Questions

Which is cheapest at scale — Make, Zapier, or n8n?

n8n is cheapest at scale, especially self-hosted (infrastructure cost only, ~$10–20/month). Make's operation-based pricing is dramatically cheaper than Zapier for complex workflows. A 15-step workflow running 10,000 times costs roughly $30–50/month on Make, $60/month on n8n Cloud, and $1,200+/month on Zapier. See our [workflow automation software guide](/blog/workflow-automation-software/) for a broader comparison.

Which automation tool is best for building AI agents?

n8n leads for technical teams with native LangChain integration, 70+ AI nodes, vector store support, and self-hosted LLM compatibility. Make offers the best AI experience for non-technical users via its Maia AI builder and visual agent debugging. Zapier Agents works best when you need AI across its 8,000+ app catalog with minimal setup. Our [n8n AI agents guide](/blog/n8n-ai-agents/) covers n8n's AI stack in depth.

Is n8n really open source?

n8n uses a "Sustainable Use License" (fair-code), not a standard OSI open-source license. You can self-host it freely for internal business use, but you cannot embed n8n in a SaaS product or resell it as an automation service without an enterprise license. The source code is visible and forkable, but it is not truly open source in the OSI sense.

Which is best for beginners — Make, Zapier, or n8n?

Zapier is the easiest for absolute beginners — trigger-action simplicity with the largest app library. Make requires a bit more setup but rewards with far more flexibility. n8n has the steepest learning curve and benefits from basic JavaScript knowledge and Docker familiarity for self-hosting.

Can I migrate from Zapier to n8n or Make?

Yes, but there is no automatic migration tool. Rebuilding a simple 3-step Zap takes 30–60 minutes in n8n or Make. Complex multi-path workflows can take 2–4 hours. Budget migration time accordingly — especially for large Zapier accounts with dozens of Zaps.