Make vs n8n (2026): Which Automation Platform Is Right for Your Stack?

Make beats n8n on price for simple automations and has a gentler on-ramp — but n8n's execution-based pricing model and self-hosting option make it dramatically cheaper at volume, and its AI capabilities are production-ready where Make's are still reportedly in beta. If you're building complex, multi-step workflows or anything AI-heavy, n8n is the stronger long-term bet.

Make vs n8n: At a Glance

Pricing: The Model Difference That Actually Matters

Make charges per credit. Every standard module run costs 1 credit; routers and error-handler directives cost 0 credits; AI modules consume variable credits at rates Make does not publish in a fixed table. n8n charges per execution — one full workflow run, regardless of how many steps are inside it, counts as 1 execution.

This distinction is not academic. A 20-step Make scenario consumes 20 credits per run. The same logic in n8n consumes 1 execution. As workflows get more complex, n8n's model pulls further ahead in cost-per-unit-of-work.

For a 3-step workflow running 1,000 times per month: Make Core ($12/mo, 10,000 credits) handles that easily with 7,000 credits to spare. n8n Starter (€20/mo, 2,500 executions) also covers it. Make is cheaper here.

For a 15-step workflow running 2,000 times per month: Make needs 30,000 credits per month. Core only includes 10,000. At the Core base rate of roughly $0.0012/credit, the 20,000 extra credits carry a 25% surcharge — that's 20,000 × $0.0015 = $30 in extra credit fees on top of the $12 base, totaling ~$42/mo. n8n Pro (€50/mo, 10,000 executions) handles 2,000 runs easily at a flat rate. Costs converge — then n8n wins at any higher volume.

Self-hosted n8n resets the math entirely. A Hetzner CAX11 VPS runs €3.29–4.50/mo. Add Docker, install n8n, and you have unlimited executions with unlimited steps for ~€4-12/mo total, depending on what you run alongside it. For any team doing meaningful automation volume, self-hosted n8n is the cheapest option on the market.

Make Pricing Tiers

n8n Pricing Tiers

Flow Logic and Complexity

Make's visual canvas is genuinely powerful. Routers (branching) and filters are available on every plan, including the free tier, at 0 credit cost. Iterators and aggregators are native — you can fan out over a list and reassemble results without writing code. Error-handler directives (Rollback, Break, Resume, Commit, Ignore) attach to any individual module, also at 0 credit cost. For agencies building client automations, this level of per-module control without a code node is a real differentiator.

n8n's logic toolkit is broader if you can code. The IF and Switch nodes handle branching; the Merge node reassembles parallel branches; Loop Over Items replaces aggregators; Execute Workflow launches sub-workflows. JavaScript expressions work anywhere in the node canvas — not just in a dedicated code block. The Code node runs full JS or Python scripts and is available on every plan including free self-hosted. For anything that needs a custom transformation, regex parsing, or an API call with complex auth, that Code node is faster to work with than wiring together several Make modules.

Both tools use a visual canvas. The execution-based vs credit-based model is invisible inside the canvas itself, but it shapes what you'd build: in Make, every module has a cost, so you think about adding them; in n8n, adding 5 extra processing steps to a workflow costs nothing extra.

Integrations: 3,000 vs 1,000 (and the Real Gap)

Make's 3,000+ native integrations is a legitimate advantage over n8n's ~1,000 native nodes. If your stack includes a niche SaaS that isn't on n8n's official list — say a specific CRM, a regional payment processor, or an industry-specific platform — Make is more likely to have it built-in.

n8n partially closes the gap with community-built nodes (count unverified; n8n lists ~2,000 but this changes), plus an HTTP Request node that can hit any REST API without a dedicated integration. For developers, the HTTP node plus the Code node makes integrating any API feasible. For non-developers, missing a native node is a real friction point.

Both support the obvious enterprise connectors: Google Workspace, Slack, HubSpot, Airtable, OpenAI, Anthropic. Neither has a meaningful gap there.

AI Features: Production vs Beta

This is the sharpest current difference. n8n's AI Agent node is its most-used building block — it supports agents, chains, memory, vector stores, RAG pipelines, and tool-calling, all built on LangChain primitives. You can connect 12+ LLM providers (OpenAI, Anthropic, Gemini, Mistral, and local models via Ollama) using your own API keys on any plan, including free self-hosted. MCP support and agent evaluations are live.

Make AI Agents launched in 2025 and are reportedly still in beta as of June 2026 — verify this before publishing. They're available on all plans via the Make AI Provider or your own LLM key. The Make AI Toolkit adds built-in AI tools inside scenarios. The catch: AI modules consume variable credits at rates Make does not publish in a fixed formula. An AI-heavy Make scenario can eat credits significantly faster than a standard one, making cost estimation difficult.

For any team building AI workflows today — agents, RAG, multi-LLM chains — n8n is production-ready. Make's AI capabilities are improving but not at the same level yet.

Error Handling and Debugging

Make's error handling is granular at the module level. Each module gets its own error-handler directive: Rollback (undo previous modules), Break (stop and store for retry), Resume (continue from next module), Commit (mark partial success), or Ignore (skip the error). Incomplete executions are stored and replayable. Pro plan adds full-text log search. This module-level granularity is useful when you need to recover partial workflow state — for example, committing a successful database write before handling a downstream failure.

n8n takes a different approach: a dedicated Error Trigger node routes any workflow failure to a separate error-handling workflow. That error workflow can do anything — send a Slack alert, log to a database, retry with different parameters. You can also configure per-node retries with a configurable number of attempts. The debug-in-editor feature lets you replay past executions with pinned data, which makes iterating on a broken workflow much faster. Log streaming is Enterprise-only.

Make's per-module approach is easier to set up for simple recovery scenarios. n8n's Error Trigger is more powerful for teams that want custom alerting and complex error routing — but it requires more initial setup.

Team Features

n8n is notably more generous on team access: unlimited users on every cloud plan, including Starter at €20/mo. Make's Teams plan ($38/mo annual) adds roles and shared templates — check Make's current terms for current seat structure specifics.

The n8n pricing cliff is a real issue for growing teams: Pro at €50/mo covers project roles and workflow history, but SSO/SAML/LDAP and Git version control are locked behind Business at €667/mo. That's a 13× price jump for what enterprise IT teams consider table-stakes features. Make requires Enterprise (custom pricing) for SSO as well, so both tools share this limitation — n8n's cliff is just steeper in absolute terms.

Shared templates and team workspaces are on Make's Teams plan. n8n's projects feature (from Pro) organizes workflows into separate namespaces with role-based access. Environments (staging vs production) require n8n Business.

Learning Curve and UI

Make is the easier of the two for non-developers. The visual canvas maps intuitively to 'connect app A to app B'; the router and filter UI is straightforward; and the module library is comprehensive enough that you rarely need custom code. New users typically get their first working scenario within an hour. The challenge arrives with advanced features: data mapping between modules requires understanding Make's data structure model, and iterators/aggregators confuse users who haven't dealt with array manipulation.

n8n's learning curve is the steepest of the mainstream automation tools — this is the top negative on G2 reviews. The JSON expression system (using JavaScript-like syntax to reference upstream node outputs) trips up non-developers who just want to pass data between steps. Self-hosting adds an ops burden that Make completely avoids. Developers, however, ramp quickly: the Code node feels familiar, the expression system is just JavaScript, and the open-source codebase means the community has documented edge cases thoroughly.

For a non-technical operations person: Make is the better starting point. For a developer or data engineer: n8n is faster to work with once you're past the initial setup.

Where Each Falls Short

Being direct about limitations is more useful than a feature list. Both tools have genuine weaknesses — here's what the data shows.

Make Limitations

• Hard stop at credit limit for non-Enterprise plans — scenarios simply stop when credits are exhausted. Users report no queue and no graceful degradation; verify whether Make now sends warning emails before the cutoff.
• Credit estimation is harder than the old operations model (changed August 2025). AI modules especially have unpredictable credit consumption because rates aren't published in a fixed table.
• 25% surcharge on all extra credits since November 6, 2025 — expensive if you mis-estimate volume. Unused extra credits expire at billing term end.
• No self-hosting option. All workflow data goes through Make's servers.
• No native code execution — complex transformations require workarounds or multiple modules.
• Make AI Agents are reportedly still beta-stage; production AI workflows on Make carry execution risk until this is confirmed stable.

n8n Limitations

• No permanent free cloud tier. The 14-day trial is all you get before paying €20/mo. Make's free tier is genuinely useful for light automations.
• Pro (€50/mo) → Business (€667/mo) price cliff is brutal. SSO/environments/Git locked at Business means most teams either overpay or go without.
• Steepest learning curve of any mainstream automation tool. JSON expressions and the node model block non-developers.
• Self-hosting requires real ops work: Docker, PostgreSQL in production, backups, memory management. Large datasets crash under-resourced instances.
• Support is forum-only below Enterprise. Email support runs CET hours only. For critical production workflows, this is a significant risk.
• Community-maintained nodes vary widely in quality; vet any community node before relying on it in production.

Which One Is Right for You

Scenario Math: What You'd Actually Pay

These calculations use current published rates. Make credit overage assumes Core plan rate ($0.0015/credit including 25% surcharge).

Scenario A — Simple 3-step Zap-replacement, 800 runs/month: Make: 3 credits × 800 = 2,400 credits. Free tier (1,000 credits) doesn't cover it; Core at $12/mo (10,000 credits) handles it with 7,600 credits to spare. Cost: $12/mo. n8n Cloud: 800 executions. Starter (€20/mo, 2,500 executions) handles it. Cost: ~€20/mo. Make wins here.

Scenario B — 12-step order processing workflow, 1,500 runs/month: Make: 12 × 1,500 = 18,000 credits. Core includes 10,000. Need 8,000 extra at $0.0015/credit = $12 extra. Total: ~$24/mo. n8n Cloud: 1,500 executions. Starter (2,500 executions) handles it. Cost: €20/mo. Costs are similar; n8n scales better if runs increase.

Scenario C — 20-step AI-assisted data pipeline, 3,000 runs/month: Make: 20 × 3,000 = 60,000 standard credits minimum — AI modules add on top at undocumented rates. Core only covers 10,000. Need 50,000+ extra: 50,000 × $0.0015 = $75+ in overage before AI module costs. Total: $87+/mo and climbing. n8n Cloud: 3,000 executions. Pro at €50/mo (10,000 executions) handles it with room to spare. n8n wins decisively.

Scenario D — 20-step AI pipeline, 10,000+ runs/month, self-hosted n8n: Make: would cost hundreds per month in credits plus AI overages. No self-host option. n8n self-hosted: ~€4-12/mo VPS. Unlimited executions, unlimited steps, BYO API keys. n8n wins by a large margin.

Migration Notes

Moving from Make to n8n requires rebuilding workflows from scratch — there's no import path between the two formats. The conceptual translation is mostly 1:1: Make modules become n8n nodes, Make routers become IF/Switch nodes, Make iterators become Loop Over Items. The Make filter UI translates to n8n's IF node conditions. Error-handler directives become Error Trigger workflows, which are more flexible but need setup.

The harder migration challenge is data mapping. Make's data panel shows the exact value of each module's output; n8n uses expression syntax like {{ $node["NodeName"].json.fieldName }} to reference upstream data. Non-developers find this the biggest adjustment. Developers find it more expressive.

Moving from n8n to Make is rarer, but the conceptual mapping works similarly. n8n's Code nodes have no direct Make equivalent — those sections need a different approach (multiple modules, JSON transformations in Make's data mapper, or a Make custom function if available).

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Rankings are never paid for. Last reviewed 2026-06-12.