Make vs n8n (2026): Which Automation Platform?

Make is what used to be called Integromat: a hosted, visual automation product where you wire modules into scenarios on a canvas. n8n is the open, self-hostable workflow engine (with a Cloud option) that technical teams pick when they want graphs, code nodes, and control over where data lives.

If you only remember one line: Make optimises for visual, hosted scenario building; n8n optimises for control, self-hosting, and execution-shaped economics.

Neither is a moral upgrade over Zapier. Zapier still wins time-to-first-workflow and long-tail connectors for many teams. This page is the fork people hit after they decide Zapier’s task meter or hosting model is the problem — or when they are choosing between the two most-named “serious” alternatives.

Related: best Zapier alternatives, Zapier vs n8n for AI automation, AI automation for small business, AI business automation playbook.

Pricing below is described as shapes, not a frozen price list. Make’s exact list prices and operation bundles change; we will not invent precision we have not checked on a PromptHive tool page. Re-open make.com and n8n.io/pricing with your volume before you budget. Zapier figures lean on our Zapier review (checked 2026-07-27).

The short answer

Who this is for

• Teams comparing Make vs n8n after a Zapier invoice scare.
• Operators who like visual canvases but need honest limits.
• Engineers evaluating self-host vs “just pay for Cloud.”
• Anyone confused by the Integromat → Make rename in old blog posts.

If you cannot describe the process in three sentences, neither tool will save you. Write the steps first.

What both tools actually do

Both are workflow engines:

1. A trigger fires (webhook, schedule, app event, form).
2. Steps / modules / nodes transform data and call APIs.
3. Optionally an AI step classifies, extracts, or drafts.

Neither replaces ChatGPT or Claude as a thinking surface. They orchestrate models and SaaS systems so a process runs on Tuesday whether anyone is at the keyboard.

A note on the Integromat rename

If your bookmarks, Slack lore, or contractor SOWs still say Integromat, they mean Make. The product idea — modular scenarios on a canvas, operations-based commercial model, strong visual debugging — carried forward. Tutorial screenshots from 2021–2023 may show old branding and outdated plan names; always pair historical how-tos with Make’s current docs and pricing page. We say “Make (Integromat successor)” in places because searchers still use both strings, not because they are two live products.

Make’s mental model

• Scenarios on a visual canvas.
• Modules for apps and tools; routers and iterators for branches and lists.
• Operations as the primary commercial meter (each meaningful module run consumes budget — design matters).
• Hosted product; you are not patching the runtime at 2am (Make is).

n8n’s mental model

• Workflows as node graphs.
• Credentials stored as first-class objects; HTTP and code nodes for anything missing.
• Cloud: pay largely by workflow executions (start-to-finish runs), not per node the way Zapier counts tasks.
• Self-host Community Edition: software free under n8n’s license terms; you own VPS, backups, upgrades, auth, and uptime.

Side-by-side comparison

Pricing shapes (verify before you buy)

Make

Public model, in plain language:

Trap: a scenario that iterates hundreds of rows can burn operations far faster than a simple two-module “new row → Slack.” Visual complexity is not free.

Trap: free-tier limits train bad habits — people design around the cap instead of the process, then blame the tool when paid still surprises them.

n8n

Trap: a workflow that runs every five minutes is ~8,000+ executions/month before business logic. Schedule design is cost design.

Trap: AI tokens are a second bill whether you use Make or n8n.

Zapier context (why you are here)

From our Zapier review (2026-07-27): free is a small task allowance and two-step Zaps only; Professional multi-step from about $19.99/mo annual framing; Team higher. Tasks multiply with steps. That is the pain Make and n8n are asked to solve — differently.

Rule of thumb

AI workflows: how the design differs

On Make, AI shows up as modules or HTTP calls inside a scenario. The canvas makes fan-out and routers obvious. Good for “classify ticket → route → draft Slack summary.” Still put a human gate before client email or money movement — same rule as our email guide and SMB automation pieces.

On n8n, AI nodes and bring-your-own keys make model spend explicit. Engineers wire retries, branches, and warehouse writes more naturally. Good for “extract → validate schema → write DB → only then notify.”

Good AI steps

• Classify inbound text (topic, urgency, spam).
• Extract structured fields from messy free text or PDFs.
• Draft a first reply for a human to send.
• Summarise for internal channels with a link to the source.

Bad AI steps

• Inventing CRM fields you never collected.
• Irreversible refunds, deletes, or public posts without approval.
• Unbounded “agent” loops that re-prompt themselves on your card.
• Auto-publishing marketing copy without an editor (see writing guide).

Write the non-AI process first. Add one AI step. Long chains built in one sitting fail silently.

Learning curve and team fit

Make’s canvas reduces “where did the data go?” for visual thinkers. n8n reduces “I cannot express this branch without hacks” for engineers. Pick the literacy your team already has — training cost is real cost.

Reliability, errors, and silent success

Both tools can be green while wrong: wrong channel, wrong field map, wrong customer ID.

Non-negotiables on either platform:

1. Failure notifications (email, Slack, Pager-ish channel).
2. Idempotency thinking — what happens if the webhook fires twice?
3. Staging credentials for rebuilds.
4. Human-visible checks on money and reputation paths.
5. Run history review in week one of any new scenario/workflow.

Silent automation is worse than no automation. That line is intentional and repeated across PromptHive automation writing for a reason.

Observability differences (practical)

Make makes it natural to click into a scenario run and see module-by-module bundles on the canvas. Operators who think visually often find root cause faster here for medium complexity.

n8n exposes execution lists and per-node input/output; engineers often prefer the predictability and the ability to drop into a code node to log exactly what they need. Self-host adds your own logging stack if you want it — and the duty to maintain it.

Neither replaces product analytics. If you need “how many leads progressed after enrichment,” measure in the CRM, not only in the automation UI.

Migration notes

From Zapier → Make

• Rebuild scenarios module-by-module; do not expect 1:1 task-to-operation equivalence.
• Re-test filters and multi-step paths; free Make constraints differ from free Zapier constraints.
• Dual-run high-value Zaps before cutover.

From Zapier → n8n

• Export field maps and error cases from the worst task-ratio Zap first (see Zapier vs n8n).
• Rebuild triggers carefully (webhooks, polling intervals).
• Keep long-tail SaaS on Zapier if n8n would force low-value custom HTTP.

From Make → n8n (or reverse)

• Translate routers/iterators into n8n branches/split-outs (or the reverse).
• Re-measure cost: operations ≠ executions.
• Do not migrate vanity scenarios; delete them.

When to use something else

Watch-outs (honest)

• Rename fog. Searching “Integromat vs n8n” still works; buying decisions must use Make’s current docs and pricing.
• Meter mismatch. Never compare “$X on Make” to “$Y on n8n” without converting your actual scenario into both meters.
• AI second invoice. Workflow fee ≠ model fee.
• Credentials sprawl. Every integration is a secret to rotate; least privilege.
• Compliance. Self-hosting n8n changes where processing happens; it does not auto-complete GDPR or sector rules.
• Catalogue envy. Zapier will still have obscure apps first; hybrid is fine.
• No PromptHive Make logo asset yet. This article’s hero uses n8n branding only — editorial honesty, not a ranking signal against Make.

Worked examples (same process, different tools)

Example A — Inbound demo request

Process: Web form submit → create CRM deal → notify sales Slack → draft a personalised first reply for a human to send.

Either platform is fine. The quality bar is identical: no invented case studies in the draft, dual-run against manual for the first cohort, alert if CRM write fails.

Example B — High-frequency sync

Process: Every five minutes, pull new rows from a system and upsert a warehouse table.

Here n8n (especially self-host) often wins on economics and control; Make can do it but schedule × operations must be modelled carefully; Zapier task maths can get ugly. Also ask whether a native connector or reverse ETL tool is the real product — not every sync deserves a general automation canvas.

Example C — Long-tail SaaS you barely use

Process: Once a week, copy a report from an obscure tool into Sheets.

Zapier’s catalogue frequently wins pure time-to-green. Forcing n8n HTTP or a Make custom app for vanity savings is false economy. Hybrid: leave this Zap alone.

Collaboration and permissions

If your organisation already reviews infrastructure as code, n8n exports fit that culture. If your organisation reviews “the diagram Sally owns,” Make’s canvas fits better. Culture match beats feature bingo.

Security checklist (both platforms)

1. Dedicated integration users with least privilege — not founder full admin OAuth wherever avoidable.
2. Separate dev/test credentials from production.
3. Rotate keys when people leave; keep a credential inventory.
4. Minimise PII in Slack messages (link to the record, do not paste the record).
5. Review AI vendor data policies when prompts include customer content.
6. Turn off scenarios/workflows you are not running — dead automations still hold secrets.
7. For self-hosted n8n: backups, update cadence, exposed port discipline, auth in front of the UI.

None of this is glamorous. All of it is cheaper than an incident postmortem.

How to trial without lying to yourself

1. Pick one production-shaped process (not a toy “hello Slack”).
2. Implement on the candidate platform with failure alerts.
3. Dual-run against current process for enough volume to see edge cases (empty fields, duplicates, retries).
4. Record builder hours, monthly meter estimate, and who else could edit it.
5. Only then compare invoices to Zapier.

A two-hour toy scenario always makes the new tool look brilliant. Production is where operations and executions stop being abstract.

Verdict

Choose Make if you want a hosted visual scenario builder, your team thinks in canvases, and you are willing to manage operations as the cost unit without running infrastructure.

Choose n8n if you need self-hosting or deeper control, someone technical will own the system, and execution-shaped (or infra-shaped) economics fit better than Make’s model — or better than Zapier tasks.

Choose Zapier if you are still buying time and connectors, not rewriting a platform.

Many teams run hybrids. That is operations maturity, not indecision.

If you are early, ship one boring reliable flow on the tool your team can actually edit next month. Automating nothing well beats a perfect architecture diagram that never reaches production.

Further reading

• Best Zapier alternatives
• Zapier vs n8n for AI automation
• Zapier AI review
• AI automation for small business
• AI business automation playbook
• Complete AI productivity guide
• Complete AI agents guide
• Best productivity AI tools
• What free AI plans actually include