Make vs n8n for AI Automation: Which Workflow Engine for Operators

The automation platform question used to be boring: pick the one with the connector you need. In 2025–2026, it got complicated. Both Make and n8n now ship native AI agent steps, LLM routing, and tool-calling inside their visual builders. The real decision is about hosting control, how billing math works at volume, and whether your team can hold the ops burden that comes with real customization.

What Each Platform Actually Is

Make is a cloud-only, visual-first scenario builder. It offers over 1,200 pre-built integrations and predictable pricing starting at $9/month for 10,000 credits.

Make cannot be self-hosted—it is exclusively a cloud SaaS product, meaning all your workflow data, execution history, and credentials are stored on Make’s infrastructure.

n8n is a fair-code, open-source automation platform that can be self-hosted or run on n8n’s cloud. n8n blends the speed of no-code with the flexibility of full-code development.

It’s a platform you can install and run on your own infrastructure—AWS, Google Cloud, DigitalOcean—giving you complete control over the system.

AI Agent Capabilities: Where They Diverge Most

This is the sharpest split between the two tools right now.

n8n has built a native AI agent architecture over the past two years. AI-native automation is where n8n has pulled decisively ahead of Make as of 2025–2026, with a native AI Agent node that lets you build autonomous agents using LLMs like GPT-4, Claude, or Llama to decide which tools to call based on incoming data, plus LangChain integration for chains, memory, vector stores, and RAG.

The platform now ships 70+ AI nodes across LLMs, embeddings, vector databases, speech, OCR, and image models. Crucially, n8n lets you build workflows with hundreds of nodes, loop through thousands of records, and write custom JavaScript—all for the price of a single execution. n8n also supports MCP natively: you can call your n8n workflows from other AI systems using its MCP Server.

Make entered the agentic layer in April 2025 and has iterated quickly. In October 2025, Make announced its vision for next-generation Make AI Agents, and in February 2026 they shipped the update to general availability.

Make AI Agents let you share agents across teams and workflows, orchestrating processes across 3,000+ apps, combining adaptive AI decision-making with transparent, reliable automation in one visual platform. Make’s differentiator here is transparency: the new Reasoning Panel gives you a clear real-time view into how your agent thinks, which tools it calls, and why it takes each path.

Make now supports direct connections to 350+ AI applications including ChatGPT, Grok, Gemini, Claude, and specialized AI services.

The honest read: n8n’s agent architecture is deeper and more composable for technically complex pipelines. Make’s agents are shinier and easier to debug visually, but the underlying agent primitives are newer and less battle-tested. Teams building RAG pipelines, multi-agent orchestration, or anything that requires custom JS alongside LLM logic should look at the /tools/n8n/ profile first. Teams wanting clean visual auditability and fast iteration on simpler AI-in-the-loop workflows will find Make’s canvas easier to share across a non-technical team.

Hosting: The Biggest Structural Difference

When handling sensitive or regulated data, hosting becomes a critical factor. n8n’s self-hosted setup ensures all data remains within your infrastructure, which can be essential for industries with strict compliance requirements such as healthcare (HIPAA) or financial services (PCI DSS).

Make, while SOC 2 Type II certified, processes data through its cloud infrastructure, which may raise concerns for organizations that need absolute control over their data storage and processing.

n8n offers a fully supported self-hosted deployment model, giving engineering teams complete control over where automation runs and how it’s secured—instead of sending workflows, credentials, or payloads through a third-party cloud, everything stays within your own environment.

Self-hosting isn’t free of cost or complexity, though. Self-hosting n8n can look appealing at first, but real-world use brings hidden costs: setting up and securing servers, hiring or dedicating DevOps resources, managing custom updates, and building missing integrations. The ops burden is real. If your team doesn’t have someone who can own infra, a managed cloud option—n8n Cloud or Make—is the honest call.

Pricing: The Math Operators Actually Need

These two platforms charge in fundamentally different units, which matters a lot at scale.

Make switched its billing unit from operations to credits in August 2025. The main reason for this shift was to allow for dynamic pricing, where more complex or resource-intensive modules—especially those with AI—consume a different number of credits based on factors like token usage, file size, or processing time.

Make’s pricing tiers include a Free plan with 1,000 credits per month indefinitely and paid plans starting at $9 for 10,000 credits per month. Watch for AI module credit burn: credits can escalate quickly with polling triggers (checking for changes consumes credits even when nothing happens), and AI module usage costs extra credits.

n8n charges per workflow execution—one complete run from trigger to finish, regardless of how many nodes it contains. n8n counts a full workflow run as one execution regardless of how many nodes it passes through—a 20-step workflow costs the same as a 2-step one.

On n8n Cloud: the Starter plan costs €24/month for 2,500 executions, Pro is €60/month for 10,000 executions, and Business is €800/month with SSO and 40,000 executions. Note that the Business plan is self-hosted only, so that €800 is a license fee on top of your server costs.

The Starter plan execution ceiling is a real gotcha: a single polling workflow checking email every 10 minutes uses 4,320 executions per month—already 73% over the Starter plan’s 2,500 cap. Self-hosted Community Edition remains free with unlimited executions, which is why teams with even moderate volume tend to move there quickly.

At complexity, n8n’s model wins: n8n’s advantage grows with complexity—simple 2-step automations are comparable across platforms, but once workflows have 5–10 steps with branching logic, n8n’s per-workflow counting and full code access pull ahead.

Scalability and Maintenance Overhead

Make scales automatically—as workflow and execution volumes increase, the platform adjusts resources seamlessly, and while you’ll pay for higher usage, you avoid the technical complexities of capacity planning.

With n8n, scaling involves planning your infrastructure, optimizing databases, and managing load balancing—putting the responsibility for growth on your organization.

For teams moving toward multi-agent orchestration, n8n’s architecture is more composable: n8n is increasingly used to orchestrate AI agents, where each agent can be a node performing tasks like data retrieval or API calls, agents can communicate and pass outputs to other agents forming complex multi-agent systems, and conditional execution allows branching based on agent outputs for dynamic orchestration. Teams further down the agentic path should also look at dedicated orchestration layers—see our /tools/crewai/ and /tools/relevance-ai/ profiles for where purpose-built agent frameworks sit relative to general automation platforms.

When to Pick Make

• Your team has no DevOps muscle and data residency isn’t a hard constraint
• Workflows connect mainstream SaaS tools and don’t require custom JavaScript
• You need non-technical teammates to build, debug, and iterate on flows independently
• Visual explainability for AI agent decisions is a requirement for stakeholder trust
• You’re doing lighter AI augmentation—routing, summarization, classification—rather than complex agentic loops

When to Pick n8n

• Data sovereignty or compliance rules mean cloud processing is a non-starter
• You’re running high-volume automation where per-step billing would be punishing
• Your workflows involve deep branching, custom code logic, or LLM tool-calling at multiple decision points
• You want to build RAG pipelines, vector search, or multi-agent orchestration directly in the workflow layer
• You have a developer or ops lead who can own the infra and wants the full n8n composability advantage

Also worth noting: if you’re evaluating these platforms alongside more purpose-built automation layers, the /compare/lindy-vs-zapier-agents/ and /compare/lindy-vs-relevance-ai/ comparisons cover adjacent territory worth reading.

Bottom Line

Make is the right call for ops-forward teams that want a managed, polished visual layer with solid AI agent support and zero infrastructure responsibility. n8n wins on cost at volume, data control, and composability depth—but it carries real DevOps overhead that Make simply doesn’t. The billing model difference alone (per-step credits vs. per-execution) can swing the cost decision by an order of magnitude once workflows grow beyond five nodes; run the math on your actual polling frequency before you commit.