Make vs n8n in 2026: Which Automation Platform Should You Build On?

Make (formerly Integromat) and n8n have been competing for the same market for three years, and in 2026 the gap between them has shifted significantly. Make moved upmarket and raised prices. n8n matured its AI capabilities and self-hosted options substantially. The right choice now depends on factors that weren't relevant two years ago.

This is a direct comparison - no hedging, no "it depends on your needs" cop-outs. You'll know which one to pick by the end.

Quick Verdict

Choose Make if: You're a non-technical team that needs fast automation with minimal code, you need enterprise SLA and support contracts, or you're building automations for clients who need a polished visual interface.

Choose n8n if: You need self-hosted deployment, you're building AI-powered workflows with custom logic, you have developers on the team, or you're running high-volume automations where per-operation pricing would be expensive.

What Both Platforms Do

Both Make and n8n are workflow automation platforms. You connect apps, define triggers and actions, add conditional logic, and run automations on schedules or events. Both have:

• Visual workflow editors
• 1000+ app integrations
• Webhook support
• HTTP request nodes for custom API calls
• Conditional branching and loops
• Error handling and retry logic
• Multi-step workflow support

The differences are in execution model, pricing, AI capabilities, and technical depth.

Pricing: Where Make Lost Ground

This is the most important practical difference in 2026.

Make Pricing

Make charges per operation - each action in a workflow is one operation. A workflow that pulls data from a spreadsheet, transforms it, and writes to a CRM uses 3 operations per run.

Current plans:

• Free: 1,000 operations/month, 2 active scenarios
• Core: $10.59/month - 10,000 operations
• Pro: $18.82/month - 10,000 operations + advanced features
• Teams: $34.12/month - 10,000 operations + collaboration
• Additional operations: roughly $9 per 10,000

The problem: complex workflows burn operations fast. A workflow with 15 steps that runs 1,000 times/month uses 15,000 operations. You're already above the Core plan's limit.

n8n Pricing

n8n charges per workflow execution - regardless of how many nodes (steps) are in the workflow.

Current plans:

• Free (cloud): 2,500 executions/month
• Starter: $24/month - 2,500 executions
• Pro: $60/month - 10,000 executions
• Enterprise: Custom
• Self-hosted: Free (community edition), paid for enterprise features

The key difference: n8n's execution-based pricing makes complex workflows economical. A 50-node workflow costs the same as a 3-node workflow per execution. For AI workflows with many transformation steps, this is a significant cost advantage.

At scale: A business running 50,000 workflow executions per month would pay roughly $450/month on n8n (Pro tier overage) versus potentially $4,500+ on Make depending on operations per workflow.

Visual Editor Comparison

Make's Canvas

Make uses a circular/node graph layout. Modules (their term for nodes) connect with lines, and the visual representation is polished and intuitive for non-technical users. The error visualization is excellent - failed runs show exactly which module failed with the error highlighted in red.

Learning curve: low. A non-developer can build a functional automation in 30 minutes.

n8n's Canvas

n8n uses a left-to-right flow layout that feels more like a programming environment. Nodes are arranged horizontally, sub-workflows are collapsed into single nodes, and the expression editor (for data transformation) is more code-like.

Learning curve: medium. Technical users find it intuitive immediately. Non-technical users typically need 1-2 hours of orientation.

The trade-off is power vs. accessibility. n8n's expression editor can handle complex data transformations that would require multiple Make modules. But it requires understanding JavaScript-like syntax.

AI Capabilities: Where n8n Pulled Ahead

This is where the platforms have diverged most significantly in 2026.

Make's AI Features

Make has added AI integrations through standard app connectors - OpenAI, Anthropic, Google Gemini, and others. You can call these APIs like any other HTTP request, or use the dedicated AI modules.

What's missing: native AI agent support. Make treats AI as another API to call, not as an orchestration primitive. Building a multi-step AI agent workflow in Make requires significant workarounds - using multiple scenarios chained together, storing state in external databases, handling retries manually.

n8n's AI Agent Nodes

n8n built native AI agent support into the platform with dedicated node types:

• AI Agent node: An autonomous agent that uses tools (other n8n nodes) to accomplish tasks. Give it a goal, define which tools it can use, and it runs a ReAct loop until it either completes the task or hits a defined limit.
• LLM Chain node: Standard LLM call with prompt templating and structured output parsing
• Memory nodes: Short-term (conversation buffer) and long-term (vector store) memory for persistent agent state
• Tool nodes: HTTP requests, code execution, database queries - anything can become an agent tool
• Vector Store nodes: Direct integration with Pinecone, Qdrant, Weaviate, and others for RAG workflows

The practical result: you can build a fully functional AI agent workflow in n8n that would take weeks to build from scratch in Make. An agent that receives a customer email, queries your CRM, looks up order history, checks a knowledge base for relevant policies, drafts a response, and routes for approval - that's a standard n8n workflow, not a custom engineering project.

Self-Hosting

Make: Not Available

Make is a cloud-only product. There is no self-hosted option. Your data flows through Make's servers. For regulated industries (healthcare, financial services, legal) with data residency requirements, this is often a hard no.

n8n: First-Class Self-Hosting

n8n was originally designed as a self-hosted tool. The community edition is free, MIT-licensed, and actively maintained. Deployment options:

• Docker Compose (most common - a few commands and it's running)
• Kubernetes (for high-availability enterprise deployments)
• Single binary (for simple setups)
• n8n cloud (managed option if you don't want to operate infrastructure)

Self-hosting gives you:

• Complete data sovereignty - no data leaves your environment
• No per-execution costs (only infrastructure costs)
• Ability to run air-gapped (for classified environments)
• Customization - add custom nodes, modify the UI, integrate with internal auth systems

For enterprise teams in regulated industries, n8n self-hosted is the default choice.

Integrations and App Coverage

Both platforms have 1000+ integrations. The specific app coverage is nearly identical for common business tools - Google Workspace, Microsoft 365, Slack, Salesforce, HubSpot, Stripe, Shopify, and most mainstream SaaS products are covered by both.

Differences:

• Make has some specialized integrations built by Make's team that n8n doesn't have native nodes for (though n8n's HTTP node can call any API)
• n8n has stronger developer-oriented integrations - Git, CI/CD tools, infrastructure APIs
• Custom integrations: n8n wins clearly. Building a custom node in n8n is well-documented and follows a consistent pattern. Make's custom module development is less accessible.

For any integration that doesn't exist natively, both platforms support HTTP requests to any REST API. n8n's HTTP node with expression support is more capable for complex API interactions.

Error Handling and Reliability

Make

Make's error handling is visual and intuitive. You can set up error routes - when a module fails, the workflow can take a different path (notify via Slack, log to a spreadsheet, retry). The execution history is detailed and shows exactly which module failed.

Limitation: Make has execution time limits (40 minutes on most plans) that can bite long-running workflows.

n8n

n8n's error handling is more programmatic. Error workflows (separate workflows that trigger on failure) give you full control. The Wait node lets you pause workflows indefinitely (useful for human-in-the-loop approval steps). No execution time limits on self-hosted deployments.

For production-grade workflows that need to be bulletproof, n8n's approach is more flexible - but it requires more setup.

When Make Is the Right Choice

1. Non-technical teams building their own automations - Make's visual interface and lower learning curve make it the right choice for marketing, operations, or sales teams who don't have developer support.

2. Client work where the client maintains automations - If you're an agency building automations that clients will operate themselves, Make's polished UI reduces support burden.

3. Lower-volume, simple workflows - If you're running a few hundred executions per month with simple step counts, Make's pricing is fine and the simplicity is an advantage.

4. Enterprise with existing Make contracts - Switching costs are real. If your team is already proficient in Make and the economics work, don't switch for the sake of it.

When n8n Is the Right Choice

1. AI-powered workflows - The native AI Agent, LLM Chain, and Memory nodes make n8n the clear choice for anything involving LLMs as reasoning engines, not just API calls.

2. High-volume automations - Execution-based pricing versus operation-based pricing. The math favors n8n at scale almost always.

3. Data residency requirements - Self-hosted n8n for healthcare, finance, legal, and government use cases.

4. Developer teams - n8n's expression editor, custom node development, and code execution nodes make it dramatically more capable in the hands of developers.

5. Complex multi-step workflows - When your workflow has 20+ nodes with conditional branching, loops, and data transformations, n8n's architecture handles it more cleanly.

The Migration Path

If you're on Make and considering n8n: the platforms are conceptually similar enough that migration is feasible, but it's not automatic. Scenarios don't export to n8n workflows. You'd rebuild workflows manually.

The most practical approach: identify your 5 highest-value automations, rebuild them in n8n, run both in parallel for 30 days, then migrate the rest once you're confident.

Most teams that migrate report the rebuild takes less time than expected - partly because they redesign the workflow during the process, removing unnecessary steps they'd accumulated over time.

Bottom Line

Make is the better product for non-technical teams that need fast, simple automations with minimal engineering support.

n8n is the better product for anything involving AI, high volume, data sovereignty, or developer involvement.

In 2026, most serious automation use cases involve at least one of those four factors. Which is why n8n's growth has significantly outpaced Make's among technical teams, while Make has retained its position as the go-to for business users building their own workflows.

The platforms aren't converging - they're diverging. Make is getting more polished and enterprise-ready in the traditional sense. n8n is getting more powerful as an AI orchestration platform. Pick based on which axis matters more for your use case.

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At Seven Labs, we build production automation systems on both platforms - choosing the right tool for the right client context. If you're architecting an automation stack and want an expert opinion on which direction to go, our automation engineering team is available for a consultation.

Related reading: AI automation ROI: real numbers from 50+ B2B deployments | AI agent use cases with proven production ROI | AI agent security risks to design for