LAST UPDATED: FEBRUARY 8, 2026
Both platforms let you build AI-powered automations without writing code from scratch. n8n gives you self-hosting and full code access. Make gives you a polished visual builder and managed infrastructure. Here's how to choose.
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You want full control over your data and infrastructure. You need to self-host for compliance, security, or cost reasons. Your workflows involve custom code alongside visual building. You're comfortable managing your own deployment and want the flexibility to extend the platform with JavaScript or Python nodes when the visual builder isn't enough.
You want the fastest path from idea to running automation. Your team includes non-technical users who need to build and modify workflows. You prefer a fully managed cloud platform with no infrastructure to maintain. You need deep integrations with business tools out of the box and a visual interface that's intuitive enough for operations teams to own.
n8n is an open-source automation platform that lets you connect APIs, services, and AI models through a visual workflow builder — with the option to drop into code whenever you need more control. Workflows are built by connecting nodes: each node represents an action like calling an API, transforming data, making a decision, or invoking an AI model. The key differentiator is that n8n can be self-hosted on your own infrastructure, giving you full control over data residency and execution.
For AI agent workflows specifically, n8n provides native nodes for OpenAI, Anthropic, Google AI, and other LLM providers, plus an "AI Agent" node that combines LLM reasoning with tool use. You can build agents that read emails, query databases, call APIs, and take actions based on AI decisions — all within the visual builder. When the visual nodes aren't sufficient, you can add JavaScript or Python code nodes inline without leaving the workflow editor.
Make is a cloud-native automation platform built around a distinctive visual builder where workflows appear as connected circles on a canvas. Each circle represents a module — a specific action within a specific service — and data flows between modules along connecting lines. Make's strength is the breadth and polish of its integrations: over 1,800 pre-built connections to SaaS tools, databases, and services, each with a clean interface that exposes the right options without overwhelming complexity.
Make's AI capabilities include native modules for major LLM providers, image generation services, and transcription tools. You can build AI-powered workflows that process incoming data through an LLM, make decisions based on the output, and trigger downstream actions across connected services. Make handles all infrastructure — hosting, scaling, monitoring, and error handling — so you focus entirely on workflow logic rather than deployment.
| Feature | n8n | Make |
|---|---|---|
| Hosting | Self-hosted or n8n Cloud | Cloud only (managed) |
| Open source | Yes — fair-code license | No — proprietary |
| Visual builder | Node-based canvas | Circular module canvas |
| Custom code | JavaScript and Python nodes inline | Limited — basic code modules |
| AI agent support | Native AI Agent node with tool use | LLM modules for major providers |
| Integrations | 400+ built-in nodes | 1,800+ pre-built modules |
| Data residency | Full control (self-hosted) | Make's cloud regions (US, EU) |
| Error handling | Retry, fallback paths, custom logic | Error handlers, break/resume modules |
| Branching logic | If/else, switch, merge nodes | Router and filter modules |
| Webhooks | Native webhook triggers | Native webhook triggers |
| Scheduling | Cron-based, interval, or trigger | Interval-based scheduling |
| Version control | Git integration (self-hosted) | Scenario versioning (limited) |
| Team collaboration | Self-managed or n8n Cloud teams | Built-in team features |
| API access | Full REST API | Full REST API |
| Learning curve | Moderate — technical users thrive | Low — designed for non-developers |
Self-hosted n8n is free with no execution limits — you pay only for your own infrastructure (a basic VPS starts around $5-20/month). n8n Cloud starts at $24/month for 2,500 executions and scales through Starter, Pro, and Enterprise tiers. The self-hosted option makes n8n dramatically cheaper at scale: once you're running thousands of workflows daily, the cost difference between self-hosted infrastructure and per-execution cloud pricing becomes significant.
Make offers a free tier with 1,000 operations per month — enough to test workflows but not enough for production use. Paid plans start at $10.59/month (Core) for 10,000 operations and scale through Pro ($18.82/month for 10,000 operations with more features) and Teams ($34.12/month) tiers. Enterprise pricing is custom. Make counts individual operations within a workflow, so a single workflow run that touches 5 modules counts as 5 operations. This per-operation model means complex workflows with many steps consume your allocation faster.
Cost at scale: For teams running 50,000+ workflow executions monthly, self-hosted n8n typically costs 60-80% less than equivalent Make plans. For teams running fewer than 5,000 executions monthly, Make's managed infrastructure and free tier often make it the more cost-effective choice when you factor in the time you'd spend managing n8n infrastructure.
n8n's AI Agent node is purpose-built for creating agents that reason and act. You configure an LLM, attach tools (API calls, database queries, code execution), and the agent decides which tools to use based on the task. This is genuine agent behavior — not just calling an LLM and routing the output. You can chain multiple agents together, have them share context through workflow variables, and add human-in-the-loop approval steps at any point.
The code node capability is particularly powerful for AI workflows. When an LLM produces output that needs custom parsing, validation, or transformation before the next step, you can add a JavaScript or Python node that handles it precisely — no workarounds, no limitations of a visual-only builder.
Make's approach to AI is integration-focused: native modules for OpenAI, Anthropic, Stability AI, Whisper, and others slot into workflows like any other service. You call an LLM, process its output through Make's data transformation tools, and trigger actions in downstream services. Make's visual router module lets you branch workflows based on LLM output — sending positive sentiment to one path and negative to another, for example.
Where Make shines is connecting AI capabilities to business tools. Building a workflow that takes a customer email, runs it through an LLM for classification, creates a ticket in your helpdesk, and sends a personalized response takes minutes in Make because all the service integrations already exist. The tradeoff is less flexibility for complex agent behavior — Make is better at "AI-enhanced automation" than at building autonomous agents that reason across multiple steps.
Recommendation: Make
Make's polished Gmail, Outlook, and helpdesk integrations make email processing workflows fast to build. Classify incoming emails with an LLM, route by category, draft responses, and update your CRM — all through pre-built modules with clean interfaces.
Recommendation: n8n
Research agents that search multiple sources, synthesize findings, and produce structured reports benefit from n8n's code nodes and AI Agent capabilities. The ability to write custom parsing logic between steps is essential when working with diverse data formats.
Recommendation: Make
Take a new lead from a form, enrich it with Clearbit or Apollo, score it with an LLM based on your ICP criteria, and route it to the right sales rep in your CRM. Make's extensive SaaS integrations make this a drag-and-drop build.
Recommendation: n8n
Workflows that pull data from APIs, transform it with custom logic, run it through AI models for classification or extraction, and load it into databases play to n8n's strengths — especially when data formats are messy and need code-level manipulation.
Recommendation: Make
Make's pre-built modules for Instagram, Twitter/X, LinkedIn, and TikTok make social media workflows straightforward. Generate content with an LLM, create images with AI, schedule posts, and monitor engagement — all through visual modules.
Recommendation: n8n (self-hosted)
When data can't leave your infrastructure — healthcare, finance, legal — self-hosted n8n is the clear choice. Full control over data residency, no third-party cloud processing, and the ability to audit every data flow through your own logging infrastructure.
Make has the easier onboarding — sign up, open the visual builder, and start connecting modules. Most users have a working workflow within 30 minutes. n8n requires either setting up a cloud account or deploying a self-hosted instance (Docker is the fastest path, roughly 15 minutes for someone comfortable with the command line). Once running, n8n's workflow builder is similarly intuitive, though its interface is denser and more developer-oriented.
Make scales automatically — you pay more as you use more, but you never manage infrastructure. n8n Cloud works similarly. Self-hosted n8n requires you to handle scaling yourself: adding workers for parallel execution, configuring queues for high-volume workflows, and monitoring resource usage. For teams without DevOps capacity, Make's managed approach removes a meaningful operational burden. For teams with infrastructure expertise, self-hosted n8n provides better economics and more control at scale.
Neither platform offers a direct export-to-import migration path. Moving workflows from Make to n8n (or vice versa) means rebuilding them in the new platform. The logic transfers — the same sequence of steps, same API calls, same decision points — but the visual builder configurations don't. For small teams with a few workflows, migration takes a day or two. For organizations with dozens of production workflows, budget one to two weeks and prioritize migrating the most critical workflows first.
n8n and Make both make it easy to build AI-powered automations that act on behalf of users — sending emails, modifying data, placing orders, interacting with external services. The harder question isn't whether the automation works, but whether the agent behind it is accountable.
When an AI agent built on any platform interacts with external users or services, those counterparties need a way to verify who they're dealing with. RNWY provides that verification layer — a permanent, non-transferable identity that stays with the agent's wallet regardless of which automation platform runs its workflows. The platform handles the plumbing. The identity proves who's responsible.
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