n8n vs Make vs Zapier: Automation Platform Comparison 2026

n8n, Make, and Zapier are the three dominant workflow automation platforms used by teams to connect apps, automate repetitive tasks, and increasingly, to orchestrate AI-powered workflows. They share a common category but differ substantially in their technical model, pricing structure, flexibility ceiling, and how they position against native AI agents.

This guide gives you a direct three-way comparison with a feature matrix, cost analysis, and clear recommendations based on team type and use case.

For context on where automation platforms sit relative to dedicated AI frameworks, see Zapier vs AI Agents and What Is an Agentic Workflow?.

Decision Snapshot#

• Pick Zapier when your team is non-technical, you need the broadest possible app library, and simplicity matters more than cost or flexibility ceiling.
• Pick Make when your workflows involve complex data transformation, multi-path branching, and you want a visual builder with better pricing than Zapier.
• Pick n8n when your team has technical capability, you need self-hosting for data privacy, you want cost efficiency at scale, or you are building AI agent workflows visually.

Feature Matrix#

| Dimension | n8n | Make | Zapier | |---|---|---|---| | Primary interface | Visual canvas (node-graph) | Visual canvas (scenario builder) | Linear step-by-step builder | | Learning curve | Moderate — technical users | Low to moderate | Low — most accessible | | Self-hosting | Yes — open-source, Docker | No (cloud only) | No (cloud only) | | Number of integrations | 400+ built-in, custom HTTP/code | 1,500+ | 6,000+ | | Free plan | Yes (cloud); unlimited (self-hosted) | Yes — 1,000 ops/month | Yes — 100 tasks/month | | Pricing at scale | Lowest — self-hosted unlimited | Mid-tier | Most expensive | | AI agent support | Native AI agent node, LangChain | AI steps, OpenAI connector | AI steps, ChatGPT connector | | Code execution | JavaScript / Python nodes | No native code execution | No native code execution | | Error handling | Granular per-node | Good | Basic | | Data transformation | Full JavaScript expressions | Advanced formula editor | Basic field mapping | | Multi-step branching | Yes — complex routing | Yes — advanced scenarios | Limited | | Team collaboration | Yes | Yes | Yes (paid plans) | | Execution logs | Detailed | Detailed | Basic | | Community | Active open-source community | Active user community | Large enterprise community |

Deep Dive: n8n#

n8n (pronounced "nodemation") is an open-source workflow automation platform built on a node-graph visual canvas. Its distinguishing characteristic is that you can self-host it completely for free, with no limits imposed by the platform on workflow count or execution volume — only your infrastructure limits you.

For technical teams, n8n is the most powerful of the three. JavaScript and Python code execution nodes allow custom logic inline with automation workflows. The expression engine supports complex data transformations without leaving the visual builder. HTTP request nodes support any REST API, authenticated via OAuth, API keys, or custom headers.

In 2024 and 2025, n8n significantly expanded its AI capabilities. It now includes a native AI agent node backed by LangChain integrations, built-in vector store connectors for RAG workflows (Pinecone, Supabase pgvector, Qdrant), and memory nodes for multi-turn conversation context. This positions n8n as a genuine alternative to code-first frameworks for teams that want visual-first agent building with real AI orchestration primitives.

Typical n8n strengths:

• Self-hosting for data residency and compliance requirements
• Lowest total cost at scale for teams managing high automation volumes
• Code nodes for custom logic that visual mapping cannot express
• Growing AI agent capabilities without requiring Python expertise
• Active open-source community with a wide template library

Typical n8n constraints:

• Self-hosting requires server management, updates, and backups
• Steeper initial setup compared to Zapier
• Smaller native integration library than Zapier (though custom HTTP covers most gaps)

For teams considering n8n as an AI agent platform, compare with Flowise vs Langflow which represents the dedicated visual AI builder category.

Deep Dive: Make#

Make (formerly Integromat) is a cloud-hosted visual automation platform known for its powerful scenario builder. Where Zapier uses a linear step-by-step model, Make uses a canvas where you can build complex branching, looping, error routes, and data aggregation scenarios visually.

Make's pricing is its clearest competitive advantage over Zapier at mid-tier volume. The same automation tasks that cost $49/month on Zapier often cost $16/month on Make. This makes Make the natural migration destination for teams that have outgrown Zapier's free tier but find Zapier's per-task pricing expensive.

Make's data transformation capabilities exceed Zapier's. The formula editor supports complex operations on arrays, objects, and strings. Iterator and aggregator modules allow you to process lists of items without code, covering many use cases that would require custom code in other platforms.

Typical Make strengths:

• Best price-to-capability ratio in the mid-market
• Visually expressive for complex branching scenarios
• Strong data transformation without code
• Over 1,500 built-in connectors
• Detailed execution history with per-step inspection

Typical Make constraints:

• Cloud-only — no self-hosting option for data privacy requirements
• AI agent capabilities are more basic than n8n's dedicated agent node
• No code execution nodes for truly custom logic

Deep Dive: Zapier#

Zapier is the original no-code automation platform and still leads on integration breadth with over 6,000 connected apps. Its design philosophy prioritizes ease of use: a new user can build and activate their first automation (a "Zap") in minutes without any setup or configuration.

Zapier's strength is its app library. For popular consumer and SMB SaaS tools — HubSpot, Slack, Gmail, Notion, Airtable, Typeform, and thousands of others — Zapier likely has a pre-built connector with supported trigger and action events. Teams building simple linear automation for common tools will find it faster to launch on Zapier than on any other platform.

Zapier Agents is the company's AI agent product, allowing users to describe tasks in natural language and have the agent decide which Zapier-connected tools to use. As of 2026, this positions Zapier in the no-code AI agent space alongside tools like Lindy.ai, though with the advantage of Zapier's deep app integration library.

Typical Zapier strengths:

• Fastest time-to-first-automation for non-technical users
• Largest integration library — 6,000+ apps
• Best enterprise support and SLA guarantees
• Strong documentation and help resources

Typical Zapier constraints:

• Most expensive per task at scale
• Less capable for complex branching and data transformation
• Limited flexibility ceiling — cannot execute custom code
• Cloud-only, no self-hosting

AI Agent Capabilities Compared#

AI integration is increasingly central to all three platforms, but with different maturity levels.

n8n has the most mature AI agent architecture. The native AI agent node integrates LangChain under the hood, supports tool definition, memory, and iterative reasoning loops. Teams can build multi-step RAG agents visually in n8n with vector store integrations for document retrieval.

Make supports AI steps primarily through API connectors to OpenAI, Anthropic, and similar providers. AI is one action type among many rather than a first-class architectural concept. Complex AI agent patterns require workarounds.

Zapier offers Zapier Agents for natural language task automation and ChatGPT/AI actions within Zaps. The agent product is evolving but sits behind n8n's native agent capabilities in architectural depth.

For teams where AI orchestration is the primary goal, dedicated frameworks (LangChain, CrewAI, n8n) will outperform Make or Zapier. For teams where AI is one step in a broader automation workflow, all three platforms handle it adequately. See Best AI Agent Platforms 2026 for a fuller landscape view.

Use-Case Recommendations#

Choose n8n when:#

• Your team can manage self-hosted infrastructure and cost control at scale matters
• You need code execution for custom business logic
• AI agent workflows are a priority and you want visual building with real agent primitives
• Data residency requirements prevent use of US-hosted cloud services

Choose Make when:#

• You want visual complexity (branching, looping, aggregation) without cloud cost of Zapier
• You are migrating from Zapier to reduce automation spend
• Your workflows involve complex data transformation but not custom code

Choose Zapier when:#

• Non-technical users need to own and maintain automations
• You need the broadest possible app library, especially for consumer SaaS
• Simplicity, support quality, and time-to-value outweigh cost considerations

Verdict Summary#

For technical teams, n8n offers the best flexibility ceiling, lowest cost at scale, and the strongest AI agent architecture of the three. For non-technical teams where speed and simplicity matter most, Zapier remains the most accessible entry point. Make wins the middle ground: more powerful than Zapier for complex data scenarios, more affordable, though without n8n's self-hosting or AI depth.

None of these platforms fully replaces a dedicated AI agent framework for complex agentic workflows — for that, see LangChain vs LlamaIndex and Build AI Agents with LangChain. But for teams integrating AI into existing workflow automation, n8n's trajectory makes it the most compelling choice in 2026.

Frequently Asked Questions#

Is n8n truly free to self-host?#

Yes. n8n is open-source with no artificial limits on self-hosted deployments. You pay for your own infrastructure.

How does Zapier compare on pricing at scale?#

Zapier is the most expensive per-task at medium and high volumes. Teams at 10,000+ tasks/month frequently find Make or n8n significantly cheaper.

Can these platforms replace AI agents?#

For structured deterministic workflows, yes — they often handle tasks without AI. But they lack autonomous decision-making and dynamic tool selection that define true AI agents.

Does Make have a free plan?#

Yes. Make's free plan includes 1,000 operations per month with access to most connectors.

Which platform is best for AI agent workflows in 2026?#

n8n, with its native AI agent node and LangChain integrations. Make and Zapier support AI steps but lack n8n's dedicated agent primitives.

Is n8n harder to use than Zapier?#

Yes. n8n has a steeper initial learning curve and self-hosting adds operational overhead. Zapier is optimized for non-technical users.