n8n vs Zapier vs Make: picking the right automation stack

If you're building anything more than a single automation, the platform you pick matters. Switching costs are real — re-implementing 20 Zaps as n8n flows is a weekend you do not want to spend. The right choice depends on where you intend to go in 12 months, not on which is easiest to start with today.

This article compares the three main options — Zapier, Make, n8n — on what they're actually good at, where they break, and which is right for which kind of user. By the end you will have a clear answer for your situation.

The shape of the market

All three tools do the same thing at a high level: trigger an action when something happens. New email → summarise → post to Slack. New customer signup → enrich → add to CRM → notify the sales team. New form submission → categorise → route to the right person.

The differences are in:

• How visual the building experience is. From "fill in forms" (Zapier) to "drag boxes and lines" (Make) to "node-based programming" (n8n).
• How many integrations they offer and how mature each is. Zapier has the most; n8n is the most flexible per integration.
• How they handle AI steps. All three support AI; n8n offers the most control.
• Pricing model. Per-task (Zapier), per-operation (Make), self-hosted-or-cloud (n8n).
• Where you can run them. Cloud only (Zapier), cloud only (Make), cloud or self-hosted (n8n).

Zapier

The category leader. Around since 2011. Thousands of integrations. Excellent docs. Designed for non-developers.

Strengths:

• Massive integration catalogue. If a service exists, Zapier probably has an integration.
• Highest-quality integrations on average. Battle-tested, well-maintained.
• AI by Zapier built-in. Adding AI to a workflow is one click.
• Excellent error handling and notifications. Zaps that fail send you a clear notification with debug info.
• Easy to share within teams. Team accounts make collaboration painless.
• The fastest "first useful automation." Twenty minutes from sign-up to a working AI Zap.

Weaknesses:

• Cost. Per-task pricing adds up. A complex workflow with 5 steps that runs 1000x/month is 5000 tasks — that's on the expensive paid tier.
• Limited flexibility. Complex conditional logic, loops, and data transformations are harder than they should be.
• Vendor lock-in. Your Zaps live in Zapier. Migrating them out is manual.
• Latency. Zaps can take 1-2 minutes from trigger to action on the free and basic tiers.

Right for: non-developers, individuals or small teams, simple-to-medium workflows, "I want this working by Friday."

Pricing: Free tier with limited Zaps and limited tasks. Paid tiers from ~$20/month up through enterprise. Tasks are the main metering unit.

Make (formerly Integromat)

The visual middle ground. More flexible than Zapier, more accessible than n8n. The visual interface — drag boxes, connect them with lines — appeals to people who think visually.

Strengths:

• Visual workflow design. Genuinely the most pleasant interface of the three. Easy to see the flow at a glance.
• More flexible than Zapier. Conditional logic, iteration over arrays, data transformations all feel natural.
• Cheaper per operation than Zapier. Operations (each step in a workflow) are cheaper than Zapier's tasks for equivalent work.
• Good integration library. Smaller than Zapier's but covers all the major services.
• Easier debugging than Zapier. You can see the data at each step in a run.

Weaknesses:

• Smaller integration catalogue. Some niche services that Zapier has, Make doesn't.
• Cloud only. No self-hosting option.
• Slightly steeper learning curve than Zapier. Not difficult, but the first hour is harder.
• Less polished error handling than Zapier. Notifications and debugging are functional but less refined.

Right for: visually-oriented users, complex workflows that need conditional logic and data manipulation, teams who want power without committing to n8n, mid-size automation needs.

Pricing: Free tier (1000 operations/month). Paid tiers from ~$10/month. Operations meter usage.

n8n

The serious tool. Open-source. Self-hostable or cloud. Node-based programming model. The right answer for users who outgrow Zapier or Make, and for any team that wants to run automation in-house.

Strengths:

• Maximum flexibility. Custom code nodes, full control over data transformations, complex branching and looping.
• Self-hostable. Run on your own infrastructure. Critical for compliance, cost at scale, or data sensitivity.
• Best AI tooling. Native support for OpenAI, Anthropic, local models via Ollama, custom inference endpoints. Best for AI-heavy workflows.
• Better cost economics at scale. Self-hosted means flat infrastructure cost, not per-task billing.
• The most modern feature set. AI agent nodes, MCP support, vector store integrations, all built-in.
• Active open-source community. Custom nodes, templates, troubleshooting.

Weaknesses:

• Steeper learning curve. The interface assumes more technical comfort. Not a tool to give your non-technical CEO.
• Setup and maintenance for self-hosted. Running n8n yourself means managing the infrastructure, updates, security.
• Smaller mainstream integration list than Zapier. Most integrations exist, but coverage is less complete on long-tail services.
• Cloud version is fine but not the main draw. Most n8n users self-host.
• Less polished error handling than Zapier. Functional, not elegant.

Right for: technical users, teams running AI workflows at scale, anyone needing self-hosting for compliance, anyone whose Zapier or Make bill has grown beyond what makes sense, builders constructing AI agents.

Pricing: Self-hosted is free (open source); cloud-hosted version from ~$24/month. Enterprise pricing for serious workloads.

How they compare on AI-specific tasks

A quick comparison on common AI workflow needs:

| Need | Zapier | Make | n8n | | --- | --- | --- | --- | | Basic AI step (call ChatGPT) | One click | One click | One node | | Multi-step AI workflow | Possible, gets ugly | Comfortable | Native | | Calling a local LLM (Ollama) | Workaround needed | Workaround needed | Native node | | RAG / vector store integration | Limited | Limited | Native | | AI agent loops (model decides next step) | Hard | Hard | Native agent nodes | | MCP integration | Beta as of 2026 | Beta as of 2026 | Native | | Custom prompt templates with variables | Manual | Manual | Built-in | | Streaming AI responses | Not really | Not really | Yes |

For anything beyond "single AI step in a simple pipeline," n8n has a real lead in 2026.

A decision rule

A simple decision tree:

Start with Zapier if:

• You are not technical.
• You want something working in an hour.
• Your workflows will stay simple (1-3 steps).
• You are willing to pay for convenience.
• You expect to have at most 5-10 workflows.

Start with Make if:

• You are comfortable with visual programming.
• Your workflows will be medium complexity (branching, iteration).
• Cost matters more than maximum simplicity.
• You think visually.

Start with n8n if:

• You are technical or willing to become so.
• Your workflows will be AI-heavy or complex.
• You want self-hosting (for compliance, cost, or control).
• You expect to scale to dozens of workflows.
• You want to build AI agents, not just data pipelines.

Use multiple together if:

• The lightweight workflows can live in Zapier (Slack notifications, simple integrations).
• The heavy ones can live in n8n (AI agents, complex pipelines, anything stateful).
• This is the right answer for many teams at intermediate maturity.

The cost math, honestly

A rough comparison for "100 workflows running 10,000 times/month total":

• Zapier: roughly $80-300/month on the Professional or Team tier, depending on how many tasks per workflow.
• Make: roughly $30-200/month, generally cheaper per equivalent task.
• n8n self-hosted: ~$20-50/month for a small server, plus your time managing it.
• n8n cloud: roughly $50-200/month on their paid tiers.

For high-volume workloads (100,000+ runs/month), n8n self-hosted becomes dramatically cheaper. For low-volume workloads with rare runs, Zapier's free tier may be free.

The unspoken cost is your time. Maintenance, debugging, building. Zapier has the lowest time cost per workflow; n8n has the highest. Make is in the middle.

Migration paths

Switching between tools is annoying. A few realities:

Zapier → Make. Often worth it as you grow. Make's visual workflow is more pleasant for complex pipelines, and the cost savings are meaningful. Migration is manual — there is no import tool.

Make → n8n. Often worth it for serious automation operations. n8n's flexibility unlocks things Make cannot do. Migration is fully manual.

Zapier → n8n. A bigger jump. Worth it if you are building AI agents at scale or need self-hosting. Migration is fully manual and slow.

A practical tip: when you cross 30-50 workflows in your "starter" tool, evaluate whether it is time to migrate the new builds to your "scale" tool while leaving the old ones where they are. Gradual migration is much less painful than wholesale.

The pattern that often works

For teams maturing through automation, a common evolution looks like:

1. Month 1-3: Zapier. Build 5-10 automations. Get the team comfortable.
2. Month 4-6: Add Make for the workflows that are getting too complex for Zapier.
3. Month 6-12: Spin up n8n for AI-heavy workflows (agents, RAG, MCP integrations). Keep Zapier and Make for the simple stuff.
4. Month 12+: Most new builds in n8n; legacy workflows stay where they were built.

This is fine. There is no rule that says you have to standardise on one. The cost of running two or three tools is less than the cost of forcing every workflow into the wrong shape.

A few specific patterns by tool

Zapier shines at: Slack notifications, simple data sync between SaaS tools, AI summary of an email → Slack, calendar event → prep brief.

Make shines at: workflows with conditional logic, iterating over arrays (e.g., "for each item in this list, do X"), data reshaping (taking output from one API and reformatting for another), workflows with retry and error-handling logic.

n8n shines at: AI agent loops, RAG pipelines, local LLM integration, workflows triggered by your own custom webhooks, anything requiring code customisation, anything self-hosted for compliance reasons.

A trap to avoid

The biggest mistake is picking based on what feels easiest today instead of what you'll need in 12 months. People start with Zapier because it's easy, build 20 workflows, then hit a wall they cannot work around without migrating. The migration is six weeks of pain they wish they had spent on the right platform in the first place.

The same applies in reverse: people pick n8n because it's the "powerful" choice, fail to build anything in three months because the learning curve was steep, and end up running nothing while their Zapier-using colleagues are shipping every week.

Honestly diagnose where you are and where you'll be. Pick accordingly. If unsure, Zapier for individuals, n8n for technical teams, Make for the visual middle.

A practical first step

Whichever tool you pick, build the same first workflow in it: new email matching a label → AI summary → Slack channel. This is the simplest useful workflow and gives you a clear feel for the tool's:

• Setup friction.
• Trigger configuration.
• AI step configuration.
• Output handling.
• Error reporting.

If the first workflow takes you longer than 30 minutes in any tool, your future workflows will take much longer. Pay attention to the friction; it scales.

The takeaway

Three tools, distinct sweet spots. Zapier for non-technical and simple. Make for visual and mid-complexity. n8n for technical and serious AI work. Pick by where you intend to be in 12 months, not by what is easiest today.

For most readers of an intermediate AI article, the right starting place is Zapier or n8n — Zapier if you want to ship simple workflows fast, n8n if you want to build AI-heavy automations and you have a developer (or developer-adjacent) mindset.

Build one workflow this week in your chosen tool. The second one will be faster. By the tenth, you'll have a much clearer sense of whether you chose right.