n8n vs Make (Integromat) for AI Workflows
Visual Builders with Different Strengths
n8n and Make (formerly Integromat) are both visual workflow automation platforms with canvas-based editors. They look similar on the surface, but their approaches to AI, pricing, and deployment differ significantly. Make targets the middle ground between Zapier's simplicity and n8n's technical depth, offering more visual power than Zapier at a lower price point, but without n8n's self-hosting capability or AI-native features.
AI Capabilities
n8n's AI integration is built on LangChain with over 70 dedicated nodes for agents, chains, memory, vector stores, embeddings, and output parsers. You can build complete AI agent systems with tool use, conversational memory, and RAG pipelines entirely within the visual canvas.
Make's AI support is more functional than strategic. It offers modules for OpenAI, Anthropic, and other LLM providers that handle text generation, image analysis, and audio transcription. These modules work well for simple AI tasks (summarize this text, classify this email, generate a response), but Make lacks the agentic capabilities that n8n provides. There are no agent nodes with tool use, no vector store integrations for RAG, no memory nodes for multi-turn conversations, and no LangChain integration.
For workflows where AI is a processing step in a larger automation (send text to GPT, use the result in the next step), Make and n8n are roughly comparable. For workflows where AI is the central intelligence (agents reasoning about tool use, querying knowledge bases, maintaining conversation state), n8n is significantly more capable.
Pricing Comparison
Make charges per operation, where each module execution counts as one operation. This is similar to Zapier's per-task model but at significantly lower prices. The Free plan includes 1,000 operations per month. The Core plan costs $9/month for 10,000 operations. The Pro plan costs $16/month for 10,000 operations with additional features. Higher tiers offer more operations at decreasing per-unit costs.
n8n's execution-based model is more favorable for multi-step workflows. A 10-module Make scenario run 1,000 times consumes 10,000 operations. The same workflow on n8n consumes 1,000 executions. For simple 2 to 3 step workflows, the difference is minimal. For complex AI workflows with 10+ steps, n8n's model is substantially cheaper at cloud pricing, and free for self-hosted deployments.
Integrations and Visual Builder
Make offers approximately 1,500 integrations, positioning it between Zapier (6,000+) and n8n (500+). For most mainstream business tools (Google Workspace, Microsoft 365, Slack, HubSpot, Salesforce, Shopify), all three platforms have coverage. Make's edge is in the depth of its module configurations, which often expose more API options than Zapier's equivalent integrations.
Make's visual builder handles parallel branches and complex routing more elegantly than Zapier's linear interface. It supports modules (scenarios) running in parallel paths that rejoin later, conditional routers that send data down different paths, and iterators that loop through arrays. n8n has similar capabilities with its Split and Merge nodes, but Make's visual representation of parallel execution is more intuitive.
Deployment and Self-Hosting
Make is cloud-only with no self-hosting option. n8n can be self-hosted for free with unlimited executions. For AI workloads, this is a decisive differentiator. Self-hosted n8n can run local models through Ollama, keep all data on your infrastructure, and eliminate per-execution costs. Make cannot offer any of these advantages.
Which to Choose
Choose Make when your AI needs are limited to simple LLM calls, you want more integrations than n8n provides, you prefer cloud-managed infrastructure, and budget is a primary concern (Make's pricing is competitive for moderate usage). Choose n8n when you need deep AI capabilities (agents, RAG, memory), you want to self-host for cost savings or privacy, or your AI workflows require the flexibility of inline code execution.