n8n vs Make (Integromat) for AI Workflows

Visual Builders with Different Philosophies

n8n and Make both offer canvas-based workflow editors where you drag nodes onto a screen and connect them with data-carrying wires. The visual similarity masks fundamental differences in philosophy. Make is designed to be the most powerful no-code automation platform available, targeting business users who need complex workflows without writing any code. n8n is designed to be the most flexible automation platform, targeting technical users who want visual workflow design combined with the option to drop into code (JavaScript or Python) whenever the visual builder is not enough.

This philosophical difference shows up everywhere. Make's modules are polished, well-documented, and constrained to specific operations. You choose a module, fill in the fields, and it works. n8n's nodes are more configurable but sometimes require understanding how the underlying APIs or libraries work. For standard business automation (sync data between CRM and email, trigger Slack notifications from form submissions), both platforms work well. For AI-heavy workflows where you need to manage context windows, configure embedding models, or tune retrieval parameters, n8n's flexibility is essential and Make's simplicity becomes a limitation.

The developer experience differs accordingly. Make's learning curve is genuinely gentler, and a non-technical user can build useful automations within their first hour. n8n's learning curve is steeper, especially for AI features, but the ceiling is much higher. Teams that invest in learning n8n's AI nodes can build systems comparable to what a custom Python codebase would produce, without writing and maintaining that codebase. Make cannot reach that level of capability regardless of how much you invest in learning it, because the building blocks simply do not exist.

AI Capabilities: Where the Gap Is Widest

n8n's AI integration is built on LangChain with over 70 dedicated nodes covering agents, chains, memory, vector stores, embeddings, output parsers, and document loaders. You can construct a complete AI agent system inside n8n's canvas: a conversational agent with access to multiple tools, pulling context from a vector store, maintaining session memory in Redis or Postgres, and using chain-of-thought reasoning to decide which actions to take. The visual interface makes the agent's decision flow visible and debuggable in a way that code-only implementations struggle to match.

The specific AI components n8n provides include dedicated agent nodes (ReAct and Plan-and-Execute patterns), tool nodes (allowing agents to call any other workflow or API as a tool), memory nodes (buffer, window, and summary memory backed by persistent stores), vector store nodes (Pinecone, Weaviate, Qdrant, Supabase, and others), embedding nodes (OpenAI, Cohere, HuggingFace), document loader nodes (PDF, CSV, web scraping, Google Drive), text splitter nodes for chunking, and output parser nodes for structured extraction. This is a complete agentic AI toolkit, not a collection of API wrappers.

Make's AI support is functional but limited to direct model calls. It offers modules for OpenAI, Anthropic, Google AI, Stability AI, and several other providers. These modules handle text generation, image generation, image analysis, audio transcription, and embeddings. They work well for workflows where AI is a processing step: "take this customer email, send it to GPT-4 for classification, route the result to the appropriate Slack channel." For these use cases, Make's modules are straightforward and reliable.

Where Make falls short is anything agentic. There are no agent nodes with tool-use loops, no vector store integrations for retrieval-augmented generation, no conversation memory nodes for multi-turn interactions, and no chain-of-thought or planning nodes. If you need an agent that reasons about which tool to call, queries a knowledge base, remembers previous interactions, and adapts its behavior based on context, Make cannot build it. You would need to implement all of that logic manually by chaining modules together with complex branching and external storage, which defeats the purpose of a visual builder.

The gap extends to local AI support. n8n integrates with Ollama, allowing self-hosted workflows to use locally running models with zero API costs. Make has no local model support because it runs exclusively in the cloud and cannot connect to services on your infrastructure. For organizations that need to process sensitive data without sending it to third-party APIs, this is a dealbreaker for Make.

Pricing Model Comparison

Make charges per operation, where each module execution in a workflow counts as one operation. A scenario (Make's term for a workflow) with ten modules consumes ten operations every time it runs. The pricing tiers as of mid-2026: Free plan includes 1,000 operations per month with two active scenarios. Core plan costs $9 per month for 10,000 operations. Pro plan costs $16 per month for 10,000 operations with priority execution, custom variables, and more advanced features. Teams plan at $29 per month adds role-based access and team collaboration features. Enterprise pricing is custom.

n8n's cloud pricing charges per execution, where one workflow run counts as one execution regardless of how many nodes it contains. The Starter plan costs $20 per month for 2,500 executions with five active workflows. The Pro plan costs $50 per month for 10,000 executions with unlimited workflows. Enterprise pricing is custom. The crucial differentiator is the self-hosted Community Edition, which is free for unlimited executions and unlimited workflows. You pay only for the server to run it.

The per-operation versus per-execution model creates dramatically different economics for complex workflows. Consider an AI workflow that processes a customer inquiry: it receives the message (1 operation/execution), classifies it (1 operation), retrieves relevant knowledge base articles (1 operation), generates a response (1 operation), logs the interaction (1 operation), and sends a reply (1 operation). On Make, that is 6 operations per run. On n8n Cloud, it is 1 execution. Run this 10,000 times per month, and Make consumes 60,000 operations (requiring the Pro plan at minimum plus overage charges), while n8n Cloud consumes 10,000 executions (fitting within the Pro plan). Self-hosted n8n costs nothing beyond the $5 to $20 per month VPS.

Make does offer advantages in the mid-range. For simple two to three step workflows at moderate volumes, Make's Core plan at $9 per month is cheaper than n8n's Cloud Starter at $20 per month. Teams that run many simple automations rather than a few complex ones may find Make more cost-effective on a per-workflow basis. The break-even point depends on average workflow complexity: for workflows under four steps, Make is usually cheaper; for workflows over six steps, n8n's per-execution model wins clearly.

Integrations and Ecosystem

Make offers approximately 1,500 pre-built integrations, positioning it between Zapier's 6,000+ and n8n's 500+. For mainstream business tools (Google Workspace, Microsoft 365, Slack, HubSpot, Salesforce, Shopify, Notion, Airtable), all three platforms have mature integrations. Make's strength is the depth of its module configurations: the Shopify module, for example, exposes nearly every API endpoint, while simpler platforms only cover the basics.

n8n compensates for fewer pre-built integrations with its HTTP Request node and Code node. Any API that is accessible over HTTP can be used in an n8n workflow without waiting for a pre-built integration. The Code node allows arbitrary JavaScript or Python, which means you can implement custom logic, transform data in complex ways, or call libraries that no automation platform has native support for. Make has a similar HTTP module but lacks the code execution flexibility that n8n provides.

Make's visual handling of parallel branches and complex routing is notably polished. Its router module elegantly splits workflows into multiple conditional paths that can run in parallel and rejoin later. The visual representation of parallel execution is more intuitive than n8n's approach, which uses Split and Merge nodes that require more manual configuration. For non-AI workflows with complex conditional logic, Make's visual builder is genuinely easier to work with.

Deployment and Data Privacy

Make is cloud-only with no self-hosting option. All data passes through Make's servers, which are hosted on AWS in the US and EU regions. Make is SOC 2 Type II certified and GDPR compliant, and enterprise plans offer dedicated infrastructure. For most business use cases, Make's security posture is adequate.

n8n offers both cloud hosting and self-hosted deployment. Self-hosted n8n runs on your own infrastructure, whether that is a VPS, an on-premises server, or a Kubernetes cluster. Self-hosting means all data stays within your network, no workflow data is transmitted to third parties, and you have complete control over access, logging, and encryption. For AI workflows that process customer PII, financial data, health records, or other sensitive information, self-hosting removes an entire category of data handling concerns.

Self-hosted n8n can also connect to local services that cloud platforms cannot reach. Ollama for local LLM inference, local Postgres for vector storage, internal APIs behind firewalls, and local file systems for document processing are all accessible from a self-hosted n8n instance. This is particularly relevant for AI workflows in regulated industries (healthcare, finance, legal) where data residency requirements make cloud-based AI processing infeasible.

When to Choose Each Platform

Choose Make when your AI needs are limited to straightforward LLM calls (classification, summarization, generation), you want the broadest possible integration catalog without writing custom code, you prefer a cloud-managed platform with no infrastructure to maintain, and your workflows are moderate in complexity (under six steps on average). Make is the better choice for marketing teams, operations teams, and business analysts who need AI-enhanced automations without technical depth.

Choose n8n when you need agentic AI capabilities (tool use, RAG, memory, reasoning loops), you want to self-host for cost savings or data privacy, your workflows are complex enough that per-operation pricing becomes expensive, or you need the flexibility to write custom code within your visual workflows. n8n is the better choice for technical teams, AI engineers, and organizations building AI-powered products rather than just automating internal tasks.

The decision often comes down to one question: is AI a feature in your workflows, or is AI the core of your workflows? If AI is a feature (you add a summarization step to an existing process), Make handles it well at a competitive price. If AI is the core (you are building agent systems that reason, remember, and act autonomously), n8n is the only viable choice between these two platforms.