n8n vs Make

Choose Make when a non-technical operator owns standard SaaS workflows; choose n8n when a technical owner needs custom code, AI loops, or self-hosting. At the live annual rates, Make Core starts at $9 per month for 10,000 credits while n8n Pro is $50 for 10,000 complete executions, but the normalized cost flips at about 5.6 ordinary billable actions per run.

The verdict: choose Make for operators, n8n for builders

Make is the better first choice for a business team that wants to connect familiar apps, see every data movement on a visual canvas, and hand maintenance to an operations person. n8n is the better long-term choice when a developer or technical operator owns the system and expects custom APIs, Python or JavaScript, AI tool loops, self-hosting, or Git-based change control.

The owner matters more than the feature list:

• A local-services operator connecting forms, a CRM, email, and SMS should pick Make. Its visual scenarios are easier to inspect, its library covers more standard apps, and the managed cloud removes server ownership.
• A funded founder building an AI support agent should pick n8n. Branching, repeated tool calls, code steps, and model integrations stay inside one workflow execution instead of multiplying ordinary action credits step by step.
• A mid-market automation lead should pick Make when cross-functional visibility is the constraint. Make Teams adds team roles and shared templates. Choose n8n instead when environments, Git, data location, or custom nodes are mandatory.
• A solo technical builder should pick n8n if the workflow is likely to become software. The early learning cost buys a cleaner path to code, custom APIs, local models, and self-hosting.

Do not self-host n8n merely because Community Edition has no software fee. Somebody still owns updates, backups, monitoring, incident response, and the license boundary. A managed Make subscription is cheaper than one avoidable outage.

n8n vs Make at a glance

Pricing and limits were verified on August 5, 2026 against the live n8n pricing page and Make pricing page. The choice turns on billing unit, ownership, and deployment. Connector counts are useful, but they are rarely the final constraint once a workflow becomes business-critical.

Pricing: the same workload and the 5.6-action crossover

Make wins the small, simple workload. n8n wins when each completed outcome requires enough steps.

The billing units explain why. An n8n execution is one complete workflow run, no matter how many steps it contains or how much data it processes. A Make credit is a unit consumed by a module action or another metered feature. Most ordinary non-AI operations cost one credit, while AI features, file processing, and code can use different rates.

Price the same lead-routing workflow on both platforms: 2,000 leads per month, with five ordinary actions for each lead.

• Make consumes 10,000 credits. Core covers that workload for $9 per month on the annual view or $12 on the monthly view.
• n8n consumes 2,000 executions. Starter covers it for $20 per month billed annually, with room under its 2,500-execution allowance.

Make saves $11 per month against the annual-billed n8n plan, or $8 when Make is paid monthly. If the workflow stays this simple, buying n8n for cost would be the wrong decision.

Complexity moves the line. Make Core's annual rate normalizes to $0.90 per 1,000 credits. n8n Pro normalizes to $5 per 1,000 complete executions at its 10,000-execution allowance. For 1,000 workflow runs, Make therefore costs $0.90 at one ordinary action per run, $2.70 at three, and $5.40 at six. n8n remains $5 across all three because step count does not change its meter.

The crossover is 5.56 ordinary billable actions per run. Below it, Make's base annual rate is lower. At six, the normalized result is $5.40 for Make versus $5 for n8n.

The code meter deserves its own check. Make's Code App supports JavaScript and Python on paid plans but consumes 2 credits per second of code execution. A five-second code step adds 10 credits to every run before the surrounding modules are counted. n8n includes JavaScript and Python code steps while continuing to meter the complete workflow as one execution.

Unused Make credits expire at the end of the subscription term. If credits run out, scenarios stop until credits are added or the plan changes, though incoming webhooks can queue within the account's allowance. n8n's execution history has retention and storage limits, but reaching those history limits does not stop workflows from running.

Pricing winner: Make for short workflows and low minimum spend. n8n for step-heavy workflows, repeated AI tool use, and code that would consume runtime credits on Make.

Make wins setup speed and app coverage

Make wins when the people maintaining the automation are operators first and builders second. Its scenario canvas, where a scenario means one automated workflow, makes filters, routes, mappings, and data bundles visible without requiring the maintainer to read code.

The current catalog advertises 3,000+ apps, compared with more than 1,000 integrations on n8n. Counts never guarantee the one connector you need, but Make has the stronger chance of covering a niche SaaS tool with a ready-made module. For a B2B operations team moving leads from a form to enrichment, a CRM, email, and Slack, that coverage reduces custom HTTP work and shortens handoff.

Make Free is useful for proving a small workflow: $0, 1,000 monthly credits, and a 15-minute minimum schedule interval. Core removes the active-scenario cap, allows one-minute scheduling, and opens the Make API. Pro adds priority execution, custom variables, and full-text execution-log search. Teams adds roles and shared templates.

The wall is not workflow branching. Make can build sophisticated routers, iterators, aggregators, subscenarios, and AI agents. The wall is economic and operational: each item flowing through a multi-module scenario can multiply credits, advanced AI can be dynamic, and every production scenario still executes in Make's cloud. The Enterprise on-prem agent reaches systems inside a private network; it does not turn Make into a self-hosted runtime.

Make also caps a paid scenario run at 40 minutes and ties 5 GB of data transfer to each 10,000 monthly credits. Long-running enrichment, large-file processing, or record-heavy loops need a model before launch, not after the first credit warning.

Setup and coverage winner: Make. It is the safer default when a non-technical team must understand and maintain standard business automation without an infrastructure owner.

n8n wins custom logic, AI agents, and deployment control

n8n wins when the automation behaves more like an internal application than a chain of SaaS actions. JavaScript and Python code steps, custom HTTP and GraphQL requests, webhooks, queues, and self-hosting remove the point where a visual builder usually forces a separate service.

For a support agent that retrieves account data, searches a vector database, calls a model, validates the response, retries a failed tool, and writes the result to a CRM, n8n's execution billing is the structural advantage. More tool calls make the workflow harder to build, but ordinary node count does not change the n8n execution meter.

n8n Pro is the practical managed plan for a technical small team: $50 per month billed annually, 10,000 executions, three shared projects, 20 concurrent executions, seven days of insights, Admin roles, Global variables, workflow history, and execution search. The full n8n pricing analysis covers the larger capacity and governance tiers without compressing them into this two-tool decision.

Self-hosting adds data-location control and custom-node freedom, but Community Edition is not OSI open source. n8n describes its Sustainable Use License as fair-code and source-available. It permits internal business, personal, and non-commercial use, but it does not permit hosting n8n and charging others to access it. Product features that collect customers' own third-party credentials can also require a separate commercial agreement. A SaaS founder should clear that boundary before treating Community Edition as a free embedded backend.

Paid self-hosting has another overlooked condition. n8n Business is listed at $800 per month billed annually for 40,000 production executions on the US page. Its license key pings n8n's license server daily and reports usage across licensed instances. If the quota is exceeded without an agreed upgrade, the published overage is EUR 4,000 for an extra 300,000-execution bucket. Self-hosted does not automatically mean unmetered once Business governance features are required.

The technical wall is ownership. Somebody must patch the instance, back it up, monitor workers and queues, test upgrades, protect credentials, and restore service. A solo founder who cannot name that owner should choose n8n Cloud or Make, not Community Edition.

Custom logic and deployment winner: n8n. It is the stronger system for technical teams, custom AI workflows, internal APIs, and workloads whose data or runtime cannot live solely in a vendor cloud.

Reliability and measured performance

n8n won one useful third-party speed sample, but the result is directional rather than universal. Mopshy reports running the same four-node workflow, webhook to enrichment to LLM to CRM upsert, 1,000 times on each platform over three days.

In Mopshy's published benchmark, self-hosted n8n on a CX22 recorded 420 ms p50 latency, 910 ms p95, and a 0.2% error rate. n8n Cloud Pro recorded 560 ms p50, 1,180 ms p95, and 0.3%. Make Pro recorded 790 ms p50, 1,640 ms p95, and 0.4%.

Those figures belong to Mopshy, which does not publish a reproducible test setup, regional placement, provider response distributions, or raw run data. Network distance and the external LLM and CRM can dominate a four-node test. Use the result as evidence that short-path latency deserves a proof run, not as an SLA.

Buyer sentiment is almost even. G2's current comparison snapshot shows Make at 4.6 out of 5 from 334 reviews and n8n at 4.7 from 297 reviews. Ratings compress very different buyers into one score, but the review themes are useful: Make draws ease-of-use praise, while n8n's learning curve appears frequently alongside its flexibility.

Measured-performance winner: n8n in Mopshy's sample. For a workflow where seconds matter, replay your own payloads against the same external services and region before committing.

Team governance: managed clarity versus engineering control

Make wins cross-functional governance; n8n wins engineering governance. These sound similar in a procurement sheet and feel very different after deployment.

Make Teams costs $29 per month on the annual view or $38 monthly for 10,000 credits. It adds teams, team roles, and shared scenario templates. Make Enterprise adds custom functions, enterprise apps, 24/7 support, overage protection, and advanced security. A growth lead, finance operator, and automation specialist can share one managed environment without owning the runtime.

n8n Pro costs $50 per month billed annually and includes unlimited users, Admin roles, three shared projects, and 20 concurrent executions. Business adds separate environments, Git version control, SSO/SAML/LDAP, and self-hosted control. That is a better governance model when workflow changes should move like software changes through review and deployment.

Neither comparison price is presented as a per-seat meter. For internal allocation across five people, Make Teams' annual view is $5.80 per seat-month, while n8n Pro is $10 per seat-month. The Make number carries only 10,000 credits; the n8n number carries 10,000 complete runs. The allocation helps budget ownership, but it does not make the workloads equivalent until actions per run are counted.

Governance winner: Make for a mixed operations team. Choose n8n when Git, separate environments, custom deployment, or data location is an explicit control rather than a preference.

Switching costs: rebuild, replay, and keep a rollback path

Switching is a rebuild, not a file conversion. Both platforms export JSON, but the schemas describe different engines.

A Make blueprint contains scenario modules, module settings, and mapped values. After import, account connections still have to be recreated. n8n exports use the platform's own JSON and can be imported from a file or URL, but that does not translate Make modules into n8n nodes. n8n also warns that exported files contain credential names and IDs, and HTTP Request nodes imported from cURL can contain authentication headers that should be removed before sharing.

The portable asset is the workflow's intent: trigger, transformations, branches, error rules, side effects, and expected outputs. Connections, credentials, execution history, data stores, webhook URLs, schedules, queue behavior, and platform-specific AI settings remain migration work.

1. 1

   Inventory the live estate

   List every active scenario or workflow, its trigger, monthly runs, ordinary actions per run, AI or code usage, connected accounts, error handlers, and downstream owner. Start with the highest-volume and highest-risk flows.

2. 2

   Export and sanitize

   Export Make blueprints or n8n workflow JSON. Store them as migration references, not import-ready target files. Remove credential names, IDs, headers, sample personal data, and keys before the files enter tickets or repositories.

3. 3

   Map behavior before modules

   Write down the expected input and output for each branch. Then map Make modules to n8n nodes, or n8n nodes to Make modules, including pagination, bundles, retries, timeouts, and error routes. Recreating boxes without their data semantics is how silent mismatches ship.

4. 4

   Replay production-shaped payloads

   Run both systems against staging credentials and compare terminal outputs, side effects, and failure behavior. Mopshy's migration practice replays the last 50 production payloads; use a larger sample when seasonality or rare branches matter.

5. 5

   Cut over with a rollback window

   Move the source webhook or schedule only after outputs match. Keep the old workflow inactive, credentials valid, and alerting visible during a rollback window. Mopshy recommends 14 days; regulated or low-frequency workflows may need longer.

Do not switch from Make to n8n merely to save $8 or $11 on a simple workload when nobody owns a server or technical workflow. The labor cost will dominate. Do not switch from n8n to Make when self-hosting, custom nodes, local models, Git review, or data location is mandatory. Do not switch either direction while a business-critical workflow lacks representative test payloads and a rollback owner.

A hybrid estate is rational when the boundary is explicit: Make for customer-facing SaaS flows that operators maintain, n8n for AI agents, internal APIs, and step-heavy back-office logic that a technical owner maintains. Connect the two with authenticated webhooks and keep observability on both sides. The wider AI automation tools comparison is useful when neither side of that boundary fits cleanly.

The decision rule that should survive the next price change

Pick Make if all four statements are true: a non-technical operator owns the workflow, standard connectors cover it, cloud execution is acceptable, and ordinary actions per run stay below the cost crossover.

Pick n8n if any hard requirement appears: self-hosting, local models, custom nodes, Git-controlled deployment, complex code, or step-heavy AI loops. Prefer n8n Cloud until a named technical owner can operate Community Edition safely.

Run both only when each platform has a distinct owner and workload boundary. A hybrid without ownership creates two failure surfaces and twice the credential sprawl.

If Zapier remains on the shortlist, the n8n vs Zapier vs Make comparison adds the easiest but usually most expensive per-action option.

Which is easier to learn, n8n or Make?

Make is easier for most non-technical operators because its visual scenario builder, app coverage, and managed cloud reduce setup and maintenance. n8n has a steeper technical curve but gives builders more control through code, custom APIs, and self-hosting.

Is n8n better than Make for AI agents?

n8n is better for complex agents with repeated tool calls, custom code, local models, or self-hosting because one complete run remains one execution. Make is better when a business operator needs a visual, managed agent workflow and its token and operation credits are predictable.

Can Make self-host workflows like n8n?

No. Make runs scenarios in its AWS cloud in EU or North America. Enterprise customers can use an on-prem agent to reach private systems, but the scenario runtime stays in Make's cloud. n8n can run in n8n Cloud or on infrastructure you control.

What is the price difference between n8n and Make?

For 2,000 five-action runs per month, Make Core covers 10,000 credits at $9 on the annual view or $12 monthly, while n8n Starter covers 2,000 executions at $20 billed annually. At six ordinary actions per run, n8n Pro's normalized $5 per 1,000 runs moves below Make Core's $5.40 annual-rate equivalent.

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