n8n vs Zapier vs Make: which is best for AI automation?

What are n8n, Zapier and Make?

All three connect apps and run automated workflows triggered by events, schedules or webhooks. Each now offers AI steps, from calling a model to running an agent that chooses tools on its own.

Zapier is a hosted SaaS built around simple trigger-and-action Zaps and the largest catalogue of app integrations. It is designed so that someone with no coding background can automate their work.

Make, formerly Integromat, is a hosted SaaS with a visual canvas where scenarios branch, loop and transform data between modules. It sits between Zapier’s simplicity and a developer tool.

n8n is a source-available workflow tool you can self-host or use as a cloud service. It combines a node-based visual editor with JavaScript and Python code steps and a set of AI agent nodes built on LangChain concepts.

The practical difference for AI work is where the logic lives and who can change it. On Zapier and Make, the vendor runs everything and you configure it. On self-hosted n8n, you own the runtime, the data and the upgrades, which brings both freedom and responsibility.

That is why the choice often splits by team rather than by feature list: operations-led teams lean toward hosted tools, engineering-led teams toward n8n.

How do they compare side by side?

Why n8n is popular for AI automation

n8n became a favourite in AI circles because it lets you build agents visually and still drop into code when a node falls short. An agent node can use a chat model, memory and tools, where tools can be other workflows, HTTP calls or code.

Self-hosting changes the economics and the privacy story. Data can stay on your server, you can call a local model through Ollama, and executions are not metered by a vendor. For workflows that process customer documents, that control is often decisive.

The licence matters. The Sustainable Use License allows internal business use and self-hosting, but restricts things such as offering n8n itself as a commercial hosted service. Read the licence file before building a product on top of it.

Developers also like that workflows export as JSON, so they can live in Git, be reviewed and be promoted between environments. Custom nodes let you wrap an internal API once and reuse it across every workflow.

The weak spots are the ones that come with running software yourself: queue mode and worker setup for heavy loads, database choice, and keeping up with frequent releases. None is hard, but each needs an owner.

Why teams stay on Zapier

Zapier’s strength is breadth and approachability. If a SaaS tool has an integration anywhere, it probably has one on Zapier, and the setup screens guide people step by step through authentication and field mapping.

For operations, sales and marketing teams that automate their own work, that removes the need for an engineer. Its AI features let the same people add summarising, classifying or drafting steps without learning prompt engineering tools.

The main downsides are cost at high volume, because billing follows tasks, and the lack of self-hosting. Complex logic with many branches is possible but less comfortable than on a canvas.

Zapier also offers adjacent products such as tables, interfaces and chatbots, so a small team can build a lightweight internal tool without another vendor. That convenience is real, but it deepens lock-in over time.

Why teams pick Make

Make’s canvas shows data flowing between modules, which suits workflows with routers, filters, iterators and aggregators. People who think visually often find it easier to reason about than a list of steps.

Its data mapping and built-in functions handle a lot of transformation without code. That makes it good for syncing records between tools, enriching leads or building multi-step content pipelines with AI modules in the middle.

Large scenarios can become hard to follow, and error handling needs deliberate design. Like Zapier, it is hosted only, so sensitive data passes through its cloud.

Make is often the pragmatic middle ground for agencies building automations for clients: visual enough to hand over, powerful enough for real data work, and without the burden of running servers.

How much does each cost to run?

Pricing changes often, so check each vendor’s current page. What is durable is the model: Zapier bills by tasks, Make by operations or credits, and n8n cloud by executions, while self-hosted n8n costs you a server and your time.

AI workflows add a second bill: model tokens. That cost is the same whichever platform calls the model, unless you use a local model on your own hardware, which only self-hosting makes practical.

Count maintenance honestly. A self-hosted n8n needs backups, updates, monitoring and secure credential storage. For a small team without an engineer, a hosted plan may be cheaper in total even when the invoice is larger.

Which should you choose?

• AI agents over sensitive or regulated data, or local models: self-hosted n8n.
• Developers who want code steps, Git-friendly exports and custom nodes: n8n.
• Non-technical teams connecting many SaaS tools quickly: Zapier.
• Visual builders handling complex branching and data transformation: Make.
• High execution volume with simple logic: self-hosted n8n usually wins on cost; compare plans carefully.
• Building a product that resells automation to customers: check n8n’s licence terms, or consider fully open-source alternatives.

How to choose in five steps

Weight the answers. If data residency is a hard requirement, it outranks convenience and narrows the field to self-hosting at once. If nobody on the team can maintain a server, it does the opposite.

• List your ten most important apps and confirm each has a solid integration or a usable API.
• Classify your data: can it legally and contractually leave your infrastructure?
• Estimate monthly runs and how many steps each run has, then price each platform.
• Decide who maintains workflows: operations staff, developers or both.
• Build one real AI workflow on your top two candidates, including error handling.

Common mistakes with AI automations

RepoLoot’s catalog includes n8n templates and self-hosted automation projects, useful as a starting point when you want to see AI workflows built by others.

• Letting an AI step write directly to a CRM or send emails without a review queue at first.
• Ignoring retries and error branches, so a single API timeout silently drops a lead.
• Hard-coding credentials in code nodes instead of using the platform’s credential store.
• Running self-hosted n8n on an open port without authentication, TLS and updates.
• Building one enormous workflow instead of small sub-workflows that can be tested alone.

Frequently asked questions

Is n8n really free?

Self-hosting n8n is free under its Sustainable Use License, which permits internal business use but restricts reselling n8n as a service. Some enterprise features require a paid licence, and the managed cloud is paid. You still pay for your server and any model tokens.

Can Zapier or Make run local LLMs?

Not directly, because they run in their vendors’ clouds. You can call a self-hosted model if you expose it through a secure public API, but that adds work and risk. If local models are a requirement, self-hosted n8n is the more natural fit.

Which is best for building AI agents?

For technical teams, n8n offers the most control, with agent nodes, memory, vector stores and tools that can be whole workflows. Zapier and Make have added agent features that are easier to start with but offer less low-level control. Test with your own tools.

Can I migrate workflows between them?

Not automatically in any reliable way. Each platform has its own workflow format and integration behaviour, so migration means rebuilding. Document what each workflow does, its triggers and edge cases, and rebuild the most valuable ones first.