n8n vs Integromat: What Changed and Which Is Better?

Integromat rebranded to Make in 2022. If you are searching for "n8n vs Integromat," you are effectively asking about n8n vs Make. This article addresses both the historical context and the current comparison.

For teams evaluating these platforms today, the relevant comparison is n8n versus Make (formerly Integromat). Here is how they differ and which fits your situation better.

Key Takeaways

• Integromat is now Make: The platform rebranded in 2022. All current features and pricing are under the Make brand.
• n8n is more flexible: Custom code, self-hosting, and AI workflow depth exceed what Make offers.
• Make kept its visual edge: The scenario builder and data flow visualization are still Make's strongest features.
• Pricing models differ: Make charges per operation. n8n self-hosted removes per-execution billing entirely.
• Make has more integrations: Over 1,000 native connectors versus n8n's 600+, though n8n's HTTP node closes the gap.
• Your team's technical level decides: Make suits non-technical teams. n8n rewards developers and technical operators.

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What Happened to Integromat?

Integromat was acquired by Celonis in 2020 and rebranded as Make in 2022. The core platform, visual scenario builder, and most integrations carried over. Existing Integromat workflows migrated to Make automatically.

If you built workflows on Integromat, they are now running on Make. The interface evolved, new features were added, and pricing adjusted, but the fundamental approach of visual data flow scenarios remained consistent.

• Rebranded in 2022: Integromat became Make.co after Celonis acquired the platform.
• Workflow continuity: Existing Integromat scenarios were migrated to Make without requiring manual rebuilds.
• Expanded integrations: Make grew the integration library significantly beyond the original Integromat catalog.
• Pricing changes: Make updated pricing tiers and operation limits compared to the original Integromat plans.
• Continued visual approach: The data flow scenario builder that Integromat was known for remains Make's core interface.

Understanding what n8n is designed to do and the types of teams it is built for provides useful context for comparing it to Make's evolved platform.

How Does n8n Compare to Make (formerly Integromat) at a Glance?

Which Is Cheaper: n8n or Make?

Make is cheaper at very low volumes due to its free tier. n8n self-hosted is dramatically cheaper at moderate to high volumes because it has no per-execution billing.

Make's operation count model means every module execution counts. A five-module scenario running 2,000 times per month consumes 10,000 operations. On n8n self-hosted, that same volume costs nothing beyond hosting.

• Make Free tier: 1,000 operations per month. Adequate for testing, not for real business automation.
• Make Core: $9/month for 10,000 operations. Works for simple, low-frequency automations.
• Make Pro: $16/month for 10,000 operations with additional features and custom variables.
• n8n Cloud Starter: Around $20/month for 2,500 executions with managed infrastructure.
• n8n self-hosted: $5 to $20/month VPS cost with unlimited workflow executions.
• Cost crossover: At 15,000 or more operations per month, self-hosted n8n typically wins on total cost.

Reviewing what n8n actually costs when you factor in plan tiers, execution limits, and infrastructure is useful context before you finalize a platform decision.

Which Has Better Integrations?

Make has more native integrations than n8n, with over 1,000 versus n8n's 600+. For common business tools, both cover the major platforms. The gap matters most for niche or legacy tools.

n8n's HTTP Request node closes the practical gap significantly. Any tool with an API can be connected through a custom HTTP node, even without a dedicated integration. This approach requires more setup but removes the ceiling on what n8n can connect to.

• Make coverage: 1,000+ native integrations including strong coverage of marketing, finance, and CRM tools.
• n8n native nodes: 600+ integrations covering the most common developer and business tools.
• Custom API access: n8n's HTTP Request node handles any REST API including private and internal systems.
• Webhook handling: Both platforms handle incoming webhooks well. n8n provides more control over processing logic.
• Enterprise tools: Make generally has stronger coverage of enterprise-grade platform integrations out of the box.

For teams that need to connect niche tools without custom API work, Make is often the faster choice. For teams comfortable with HTTP requests, n8n's gap matters less.

For teams that want to understand what building on n8n actually looks like before committing, how n8n handles data routing, branching, and transformation across connected apps gives you a clear picture of the platform in practice.

Which Handles Complex Workflows Better?

n8n handles genuinely complex workflows better than Make. Custom JavaScript, Python nodes, sub-workflows, and first-class error handling give n8n a clear advantage for workflows that go beyond standard data routing.

Make is strong for visual logic and moderate complexity. It becomes limiting when your workflow requires custom data transformations, recursive logic, or behaviors that its built-in modules do not support.

• Custom code: n8n runs JavaScript and Python natively inside workflow nodes. Make has no equivalent.
• Sub-workflows: n8n supports reusable workflow modules that reduce duplication across complex systems. Make has no equivalent.
• Error handling: n8n defines specific error paths, retry logic, and fallback behavior per node. Make's error handling is simpler.
• Aggregators and iterators: Both have these, but n8n's are more configurable for complex data manipulation.
• Merge logic: n8n provides multiple merge strategies for combining data from parallel workflow branches.

For workflows that stay within standard business patterns, Make covers most requirements well. When logic requires programming-level control, n8n is the better choice.

For teams evaluating platform depth, what n8n includes beyond the basics, including credential management, error handling, and version control shows where n8n's technical depth gives it a meaningful edge over Make for complex automation builds.

Which Is Better for AI Automation?

n8n has more capable AI automation than Make. Native LLM nodes for OpenAI, Anthropic, and other providers with full parameter control, agent loop support, and vector database integration make n8n the stronger platform for AI-powered workflows.

Make supports AI automation through HTTP requests to AI APIs and some built-in AI modules. These work for basic use cases but lack the depth of n8n's dedicated AI workflow infrastructure.

• Native LLM nodes: n8n has dedicated nodes for GPT-4, Claude, Gemini, and Mistral with configurable parameters.
• AI agent support: n8n enables multi-step AI agent workflows with tool calling, memory, and loop logic.
• Vector store integration: n8n connects natively to Pinecone, Qdrant, and Chroma for RAG workflows.
• Make AI options: HTTP-based calls to AI APIs work but require more manual configuration and offer less control.
• Document processing: n8n handles structured data extraction from PDFs and text through LLM nodes.

How n8n's AI automation capabilities compare to other platforms is worth reviewing if AI workflow capability is a primary requirement.

When Should You Choose n8n Over Make?

Choose n8n when you need self-hosting, custom code nodes, AI agent workflows, or cost efficiency at scale. Choose Make when you want a cleaner visual interface, no infrastructure work, and better coverage of niche integrations.

The practical decision often comes down to one question: does your team have someone comfortable with technical setup? If yes, n8n's advantages are accessible. If no, Make reduces friction even if it costs more at scale.

• Choose n8n: Technical team, need for custom logic, self-hosting for data control or cost.
• Choose n8n: Building AI-powered workflows that require LLM configuration and agent behavior.
• Choose n8n: Running high automation volumes where per-operation pricing becomes expensive.
• Choose Make: Non-technical team that needs automations running quickly without infrastructure work.
• Choose Make: Need specific niche integrations that n8n does not have native nodes for.
• Choose Make: Visual clarity and team collaboration on scenario building is a priority.

For teams evaluating beyond just n8n and Make, how n8n compares to its main alternatives on pricing, flexibility, and use case fit provides additional context to guide the decision.

Conclusion

Integromat evolved into Make and is now one of n8n's primary competitors. The comparison remains consistent: Make for visual clarity and ease of use, n8n for flexibility, cost efficiency, and AI workflow depth.

For most growing businesses with technical resources, n8n delivers more value per dollar at scale. For non-technical teams or those prioritizing speed of setup, Make remains a strong choice.

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Want Help Building Automation on n8n or Make?

Choosing the right platform is one decision. Building reliable automation that handles real production complexity is another challenge entirely.

At LowCode Agency, we build n8n and Make automation systems for growing businesses. We are a strategic product team, not a dev shop. We have delivered 350+ automation and software projects for clients including Medtronic, American Express, and Zapier.

• Platform evaluation: We assess your workflow requirements and team profile before recommending n8n or Make.
• Full system build: We design, configure, test, and deploy automation systems that handle real-world complexity.
• AI integration: We build LLM-powered workflows for classification, generation, and agent automation.
• Make to n8n migration: We manage full migrations for teams outgrowing per-operation pricing on Make.
• Post-launch support: We monitor and maintain your automation system as your tools and processes change.

If you are comparing n8n to Make and want help making the right call for your team, let's review your requirements and map the right platform to your actual workflow complexity.