Head to head

n8n vs Make

Our take: choose the canvas and deployment model your team wants to maintain. Make and n8n both offer AI-assisted building. Make uses operation-related credits; n8n Cloud and paid self-hosted plans use execution allowances. Try the same edge cases and compare a representative bill rather than assuming friendlier or more technical means better.

Whenever vs n8n Whenever vs Make

How each one bills

Price the same month of business outcomes in dollars, not Make credits against n8n executions. Include the subscription, any usage beyond its allowance, runtime AI and external provider charges on the same currency and billing period. Make bundles, trigger checks and advanced feature rates affect the budget; self-hosted n8n runs on your own infrastructure, and its self-hosted Business plan still lists an execution allowance. No competitor dollar price or measured saving is quoted here.

n8n

Execution volume

One execution per workflow run.

n8n Cloud and paid self-hosted plans list execution allowances; deployment and plan terms differ.

Make

Operation-based credits

Scenario usage, AI-agent chat and Maia usage.

Ordinary non-AI operations normally use one credit; AI and advanced features can have variable rates, and Maia consumes credits from the same pool.

Whenever

Monthly run allowance

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Side by side

The same six questions every comparison on this site answers, with the third column for the option that is neither.

Repeatable execution logic

n8n: n8n documents configured branches, loops, waits and code, as well as AI-assisted workflow construction. Our comparison distinguishes configured logic from model-generated decisions; it is not a measured guarantee of identical n8n outputs.
Make: Scenarios run the modules you configure; a router splits the flow into routes, and filters add conditions to each route. AI apps and Make AI Agents are modules you add where you want them.
Whenever: AI writes it, then published code runs the same execution logic. With identical inputs and dependencies, non-AI logic is deterministic. Changing API data, time, randomness or an optional model step can change results; this is not an exactly-once delivery guarantee.

Runs per dollar

n8n: n8n Cloud plans meter workflow executions, and its self-hosted Business plan also lists an execution allowance. n8n also publishes a self-hosted Community Edition; if you host n8n yourself, it runs on your own infrastructure.
Make: Make bills in credits. Most non-AI operations consume one credit, while some features depend on tokens, processing time, pages or file size. The work performed inside the scenario matters.
Whenever: Pro includes 10,000 runs per month; see the pricing page for current US dollar prices. AI building and editing are free within weekly usage limits. Published runs are metered separately: the meter counts the whole run, not individual steps. External services and optional model calls can have their own charges.

What you can express

n8n: n8n offers a node canvas, expressions and code nodes. Its AI Workflow Builder can help create and refine workflows; natural-language assistance is not unique to Whenever.
Make: Make builds scenarios from modules, mapped values, routers and filters on the scenario canvas, and Maia can build and edit scenarios from a prompt. Natural-language authoring alone is not the distinction.
Whenever: Describe the job in plain language: match payments to orders, fetch every page of contacts, or alert only on unseen reviews. AI writes the procedure; you supervise its rules and results, and reading code is optional. API access still needs suitable permissions and a service reachable from the hosted runtime.

Export and portability

n8n: n8n exports and imports workflow JSON. That is a workflow definition for n8n, not a promise of drop-in execution on another runtime.
Make: Make documents JSON blueprints containing modules, settings and mapped values, sharing them with other users, and importing into scenarios or compatible tools. Connections must be recreated; check the destination’s compatibility rather than assuming a universal runtime export.
Whenever: The TypeScript is yours: read it, edit it, export it, take it with you. Moving it to another runtime can require adapting managed connections, triggers and runtime-specific integrations.

Debugging a failed run

n8n: n8n documents copying an execution into the editor and pinning its data for debugging. Its docs list this for all n8n Cloud plans and for registered Community, Business and Enterprise self-hosted plans.
Make: The visual scenario remains the unit you inspect and maintain. Trace the modules and mapped values, and consider how bundles fan out when reproducing a problem.
Whenever: Check the summary, execution log and sample outcomes, then ask for a correction in chat. Inspect TypeScript if useful; use safe inputs when testing external writes again.

Where the AI cost lands

n8n: n8n’s AI Workflow Builder can create and refine workflows, and n8n plans include agent and AI nodes for workflows. Check how the AI steps you plan to use are billed.
Make: Not every scenario needs AI. Built-in AI can have variable credit rates; when you supply custom model credentials, provider token charges are separate.
Whenever: AI writes the workflow once, with free building subject to weekly AI usage limits. A published procedure runs its steps without a model choosing them. Any model call you explicitly include still runs and has its own cost.

Our recommendation: choose by the job

Pick n8n if

Someone on the team is comfortable with expressions and code nodes.
You want to run it on your own infrastructure; n8n publishes a self-hosted edition. External tools and models can still transmit data.
Runs have many steps, so a per-execution meter matters.

Pick Make if

The team prefers to build and read automations on a scenario canvas.
Your team already maintains its automations as Make scenarios.
You want Make’s AI assistant, Maia, to build and edit scenarios from a prompt.

Or a third option: Whenever

Illustrative decision, not a customer result: Greg wants Stripe payments matched to Shopify orders by ID and mismatches listed for review. Whenever writes the procedure from his rules; he checks sample outcomes and run history without needing to read code. Fixed non-AI logic is deterministic with the same inputs and dependencies; optional AI can vary and has runtime costs.

Written as TypeScript you can review

Describe the job and Whenever’s AI writes it as TypeScript, including the conditions, repeats and data handling it needs.

One run per whole workflow

One run is one whole workflow, however many steps or items it touches. Free AI building has weekly usage limits.

TypeScript you can read

n8n exports workflow JSON for import into n8n, and Make exports scenario blueprints as JSON files. Whenever leaves you a workflow file you can read and take with you; managed connections and runtime dependencies may still need adapting.

Build free

A few useful answers

Is n8n better than Make?

There is no measured winner here. Our recommendation is to test the same job, including pagination, missing data and a repeated event, and choose the workflow your team can supervise most clearly. AI-assisted authoring is available in these builders.

Is n8n cheaper than Make?

Unit counts alone cannot answer that. Price a representative month using the applicable plan, included allowance, overage, runtime AI and external provider charges. Keep the same billing period and currency.

Can I move a scenario from Make to n8n?

Treat migration as a rebuild and validation exercise, not a promised direct import. Export what your account supports, record business rules and reconnect credentials. Whenever can write the replacement from your description; test safe examples before switching triggers.

Is there an option between the two?

Whenever offers managed execution on a subscription with a monthly run allowance and separate included runtime AI spend, with AI-written logic you supervise through summaries, samples and run history. Fixed non-AI logic is deterministic under identical inputs and dependencies; optional AI and external state can change outputs and costs.

Sources & further reading

Official source captures reviewed October 9–10, 2026. Suggested fit is our editorial judgment, not a benchmark.

Try the option that is neither

Try one workflow against representative inputs. Review its summary, results and costs before switching the trigger; inspect the code only when useful.