n8n vs Zapier vs Make in 2026: The Honest Migration Math

Most teams that ask "should we move off Zapier or Make to n8n" are really asking two separate questions: does n8n do what we need, and is the switch worth the hours it costs. Those are different math problems. This article keeps them separate, gives you the pricing model each platform actually uses in 2026, scores AI-agent fit, and hands you a migration-cost formula you fill in with your own numbers. No promised savings figure, because your savings depend on your task volume, your team's rate, and how many of your workflows are trivial versus gnarly. The honest answer is a method, not a number.

TL;DR

• Zapier and Make price by task or operation volume; n8n self-host prices by server.

• n8n is the strongest fit for AI-agent and multi-step LLM workflows in 2026.

• Migration cost is workflow count times rebuild hours times your blended rate.

• Self-host only pays back if monthly task spend exceeds infra plus maintenance time.

• Move high-volume and AI workflows first; leave low-volume Zaps where they are.

· Founder, Vantaige · Published 2026-05-19 · 14 min read · Last reviewed 2026-05-19

What is the real difference between n8n, Zapier, and Make?

The real difference is the pricing axis, not the feature list. Zapier and Make are hosted-only and bill by execution volume (tasks or operations). n8n is open-source and can run on your own server, so its cost is a flat infrastructure and maintenance line that does not rise with volume. Features overlap heavily in 2026; the cost curve is what diverges.

All three connect apps, run multi-step workflows, branch on conditions, and now ship AI features. Where they split:

• Zapier is the broadest connector catalog and the lowest learning curve. It is closed-source and hosted only. Billing is per task (each successful action step counts).

• Make (formerly Integromat) is a visual scenario builder with finer control over data and iteration. Closed-source, hosted only. Billing is per operation (each module call inside a scenario counts), which behaves differently from Zapier's per-task model on loop-heavy work.

• n8n is source-available and self-hostable. You can run it free on your own VPS, or pay for n8n Cloud. Self-hosted, your cost is the server plus your time, decoupled from how many executions you run. Per the official n8n self-hosting docs, a single small instance handles a large volume of workflows before you need to scale hardware.

The decision is rarely "which tool is better." It is "at our volume and with our AI needs, which cost curve wins, and does the switch cost less than it saves." That is the rest of this article.

How does n8n compare to Zapier and Make on pricing in 2026?

n8n self-hosted has a flat cost (server plus maintenance) that does not scale with execution volume. Zapier scales with tasks. Make scales with operations. So at low volume the hosted tools are cheaper because you pay near nothing; at high volume the self-hosted flat line wins because the metered bill keeps climbing while your server cost does not.

Exact prices change often, so treat the numbers below as the pricing structure to verify, not quotes. Always check current pricing on each vendor's page before you model a decision.

The structural takeaway: Zapier and Make convert "more usage" directly into "more money" every month, forever. n8n self-host converts "more usage" into "the same server bill" until you genuinely outgrow the box. The crossover point is the entire decision, and the next section is the formula for finding yours.

What is the honest migration math for moving to n8n?

The honest migration math has two halves you must compute separately: the one-time cost to move, and the recurring cost difference after you move. Migrating only makes sense when the recurring monthly saving pays back the one-time migration cost inside a payback window you are comfortable with (commonly 6 to 12 months). Here is the method, with no promised total.

Step 1. One-time migration cost.

Migration cost = (W_simple x H_simple + W_complex x H_complex) x R
              + Setup_hours x R
              + Parallel_run_months x Current_monthly_bill

Where:
  W_simple   = number of simple workflows (1 to 3 steps, no branching)
  H_simple   = hours to rebuild + test one simple workflow (estimate 0.5 to 1.5)
  W_complex  = number of complex workflows (branching, loops, many apps, AI)
  H_complex  = hours to rebuild + test one complex workflow (estimate 2 to 8)
  R          = your blended hourly rate (loaded cost of whoever does the work)
  Setup_hours= hours to stand up + secure + back up n8n once (estimate 4 to 12)
  Parallel_run_months = months you run old + new in parallel before cutover

The parallel-run term matters and teams forget it. You do not flip 200 workflows in one night. You run both stacks for a period, which means you pay the old metered bill while you build, so it is a real migration cost, not a saving yet.

Step 2. Recurring monthly difference after cutover.

Monthly_saving = Current_metered_bill
               - (Server_cost + Maintenance_hours_per_month x R)

Where:
  Current_metered_bill        = your real Zapier/Make invoice this month
  Server_cost                 = monthly VPS or n8n Cloud cost
  Maintenance_hours_per_month = patching, monitoring, fixing broken flows

Step 3. Payback period.

Payback_months = Migration_cost / Monthly_saving

Decision rule:
  If Monthly_saving <= 0          -> do NOT migrate (self-host costs more for you)
  If Payback_months > your window -> do NOT migrate yet (revisit at higher volume)
  If Payback_months <= your window-> migrating is defensible

Notice the formula can return "do not migrate." That is the point. If you run 30 low-volume Zaps and your invoice is small, Monthly_saving is likely negative once you price your own maintenance hours honestly, and the math tells you to stay. n8n's own positioning, per its hosting documentation, is that self-host wins at scale, not at every scale.

How do you know if self-hosting n8n actually saves money?

You know self-hosting saves money only when Monthly_saving from the formula above is clearly positive after you have priced your maintenance time at your real loaded rate. The single most common error is setting Maintenance_hours_per_month to zero. Self-hosting is not free; it trades a metered bill for a time bill.

Run the check in this order:

1. Pull your real invoice. Use the actual last three months of Zapier or Make billing, not the plan you think you are on. Average it.

2. Price the server honestly. A small VPS that runs n8n in Docker is inexpensive, but add backups, and add a staging box if uptime matters.

3. Price your time at loaded cost. If a developer who maintains it costs the business a meaningful hourly figure, two maintenance hours a month is not trivial. Use the loaded rate, not the salary-only rate.

4. Stress the volume both ways. Project 12 months out. The metered bill grows with usage; the server bill mostly does not. If you are growing, the gap widens in n8n's favor; if you are flat and low, it may never cross.

If you sell automation as a service rather than run it internally, the math flips: the maintenance time is billable to clients, so self-hosted n8n becomes a margin lever, not a cost. That operator economics case is covered in the build and sell AI automations operator playbook.

Which platform is best for AI agents and LLM workflows in 2026?

n8n is the strongest fit for AI-agent and multi-step LLM workflows in 2026 because it ships native agent and AI nodes, gives you a code node for arbitrary logic, supports tool calling and loops without per-step metering, and connects to Model Context Protocol servers. Zapier and Make added capable AI features, but every AI step they run is billed against your task or operation quota.

What "AI-agent fit" means in practice, broken out:

• n8n. AI agent nodes, vector store nodes, a code node for custom steps, native loops, and MCP support so a Claude or other model can call your workflow as a tool. Because self-host execution is not metered, an agent that loops 40 times to refine an answer does not change your bill. n8n is central to a large share of 2026 agentic builds for this reason. See the n8n advanced AI documentation for the current node set.

• Zapier. Strong connector reach and an agent layer added across 2025 to 2026. Good for "trigger, one AI call, route the result." Cost climbs with each AI action because it consumes tasks.

• Make. Good visual control for AI scenarios and added AI modules. Operation metering bites hardest on agent loops and iterators, where a single scenario run can consume many operations.

If your roadmap is mostly AI agents, retrieval, and multi-step LLM chains, the metered model is a structural tax on exactly the pattern you are scaling. That is why teams building agent-heavy systems gravitate to n8n. If you are wiring n8n into a coding-agent stack, the n8n MCP and Claude Code setup guide walks the exact connection.

How long does it take to migrate from Zapier or Make to n8n?

Plan for a reported technical learning curve of roughly seven days to become productive in n8n if you are coming from Zapier or Make, plus per-workflow rebuild time. The platform learning is front-loaded and one-time; the bulk of total migration time is then the linear work of recreating and testing each workflow, which scales with how many you have and how complex they are.

A realistic phasing:

1. Days 1 to 7: learn and stand up. Install n8n in Docker, secure it, set up backups, and rebuild three or four representative workflows to learn the node model. This is your Setup_hours plus the learning curve.

2. Phase 1: high-value first. Migrate the workflows with the highest metered cost and the AI-heavy ones, because those produce the biggest Monthly_saving per hour invested. Run them in parallel with the old platform.

3. Phase 2: the long tail. Migrate or retire low-volume workflows. Some are not worth moving; deleting a dead Zap is also a valid outcome.

4. Cutover. Stop the old platform only after the new flows have run clean in parallel for a full billing-relevant period.

The seven-day figure is the time to competence, not the time to finish. Total calendar time is competence plus (workflow count times per-workflow hours) plus the parallel-run window. Put all three into the Step 1 formula instead of guessing a single number.

What are the common mistakes teams make migrating to n8n?

The most common mistake is treating the move as a tool swap instead of a cost decision, so teams migrate workflows that were never worth migrating and end up with more maintenance for no saving. The math, run honestly before any rebuild, prevents most of the damage. Here are the named pitfalls and the fix for each.

• Pricing maintenance at zero. Self-host has a real time cost. Fix: always include Maintenance_hours_per_month x R in Monthly_saving.

• Migrating low-volume Zaps. A workflow that triggers twice a day saves almost nothing and still costs rebuild hours. Fix: sort workflows by metered cost, migrate top-down, stop when the saving per hour drops below your threshold.

• Forgetting the parallel-run bill. You pay the old invoice while you build the new stack. Fix: include Parallel_run_months x Current_monthly_bill in migration cost.

• No backups or staging on the self-hosted box. A single VPS with no backup is a future outage. Fix: budget backup storage and, if uptime matters, a staging instance, both in Server_cost.

• Rebuilding node-for-node. Make scenarios with many granular modules often collapse into fewer n8n nodes. Fix: redesign the logic in n8n's model rather than transliterating it, which also cuts rebuild hours.

• Ignoring connector gaps. A niche app with a first-party Zapier connector may need an HTTP request node in n8n. Fix: inventory every app before estimating H_complex; flag the ones without a native n8n node.

• Migrating during a growth spike. Rebuilding while volume and incidents are climbing magnifies risk. Fix: phase the migration; never cut over the whole estate at once.

If you run automation for clients rather than yourself, the same mistakes cost you margin and trust at once. The service-delivery framing, including how to scope and price a migration for a client, sits in the selling AI chatbots and automation retainers guide.

When should you NOT move off Zapier or Make?

You should not move off Zapier or Make when your metered bill is small, your volume is flat, your workflows are simple, and nobody on the team wants to own a server. In that scenario the formula returns a negative or near-zero Monthly_saving once maintenance time is priced honestly, and migrating loses money and adds operational load for no return.

Concrete stay signals:

• Your real invoice is low and not growing month over month.

• Most workflows are one to three steps with no AI loops.

• No team member can reliably own patching, monitoring, and incident response.

• You depend on a first-party connector that has no native n8n equivalent and the HTTP workaround is fragile.

• Uptime requirements are strict and you are not ready to fund redundancy.

The reverse is also true: the case to move strengthens fast when your invoice is large and rising, your roadmap is AI-agent heavy (metered AI steps are the worst case for the hosted model), or you sell automation and the maintenance time is billable rather than overhead. Run the Step 1 to Step 3 formula with your real invoice before you decide either way. The tool is not the question; the crossover point is.

Frequently asked questions

Is n8n really free compared to Zapier and Make?

n8n is source-available and free to self-host, so there is no per-execution fee when you run it on your own server. It is not zero-cost, though. You pay for the server, backups, and the time to maintain and secure it. The honest comparison is "metered subscription" versus "flat infrastructure plus maintenance time," and which is cheaper depends entirely on your volume and your hourly rate. Always check current n8n Cloud pricing if you do not want to self-host.

How is Make's per-operation pricing different from Zapier's per-task pricing?

Zapier counts a task for each successful action step in a Zap, so a three-action Zap that runs once is roughly three tasks. Make counts an operation for each module call inside a scenario, including iterations, so a scenario that loops over twenty items can consume many more operations than the equivalent Zapier task count. On loop-heavy or AI-iteration work, the two models can diverge sharply, which is why you must model your own workflows rather than compare headline plan prices.

Can I migrate only some workflows to n8n and keep the rest?

Yes, and for most teams a hybrid is the rational outcome. Migrate the high-volume and AI-heavy workflows where the metered saving per rebuild hour is largest, and leave low-volume workflows on the hosted platform where they cost almost nothing and need no maintenance. The formula in this article naturally produces a hybrid because it tells you to stop migrating once the saving per hour drops below your threshold. There is no requirement to move everything or nothing.

How steep is the n8n learning curve coming from Zapier?

Reported figures put the technical learning curve at roughly seven days to become productive if you already understand triggers, actions, and branching from Zapier or Make. n8n exposes more (a code node, expression syntax, hosting decisions), which is why the curve exists, but it is a one-time front-loaded cost. After competence, per-workflow rebuild time is the dominant remaining effort. Budget the seven days as Setup_hours-adjacent in your migration math, separate from per-workflow time.

Does n8n handle AI agents better than Zapier or Make in 2026?

For multi-step AI agents and LLM chains, n8n has a structural advantage in 2026 because self-hosted executions are not metered, so an agent that loops many times to refine an answer does not increase your bill, and it ships native AI agent, vector, and code nodes plus MCP support. Zapier and Make have capable AI features, but every AI step consumes tasks or operations, which taxes exactly the looping pattern agentic workflows rely on. If your roadmap is agent-heavy, that difference compounds monthly.

What does the migration math return if I only have a few simple Zaps?

Typically a negative or near-zero monthly saving, which means do not migrate. With a small invoice and simple workflows, the metered bill is already low, so subtracting your real server cost and honestly priced maintenance hours often leaves nothing positive. The formula is built to surface this so you do not spend rebuild hours chasing a saving that is not there. Revisit the calculation if your volume or AI usage grows materially.

Is self-hosted n8n harder to keep secure than hosted Zapier or Make?

Self-hosting moves security responsibility to you. With Zapier or Make the vendor patches the platform; with self-hosted n8n you own updates, network hardening, credential storage, and access control. This is not a reason to avoid it, but it is a real line in the maintenance-hours term of the formula and a reason to fund backups and, where uptime matters, redundancy. Price it in honestly rather than assuming the server runs itself.

References

1. n8n official self-hosting documentation (hosting model and scaling guidance), accessed May 2026.

2. n8n advanced AI documentation (AI agent, vector, and tool nodes), accessed May 2026.

3. Zapier pricing page (per-task pricing structure, verify current numbers), accessed May 2026.

4. Make pricing page (per-operation pricing structure, verify current numbers), accessed May 2026.

5. n8n documentation home (node model, expressions, code node), accessed May 2026.

Related from Vantaige

• n8n MCP and Claude Code setup guide (2026)

• Build and sell AI automations: the service operator playbook (2026)

• Sell AI chatbots to local businesses for a 1k to 5k retainer (2026)

• Agent 365 vs Claude Managed Agents: cost comparison (2026)