n8n vs Make: Which Automation Tool Fits a Service Business?

Quick answer: Pick n8n if you need self-hosting or custom workflow logic and have someone to maintain it. Pick Make if you want a vendor-hosted visual builder and your action count fits its credit allowance. n8n bills per workflow execution. Make bills per credit, and most actions use one credit.

Leads slip through disconnected forms. Staff retype the same customer details into the CRM. The booking integration breaks, and nobody owns the fix. Those gaps should drive the n8n vs Make decision, not the feature lists.

Respondents from larger companies report faster progress with AI agents. In McKinsey’s 2026 State of AI survey, 40% of respondents from organizations with more than $1 billion in annual revenue reported scaling AI agents, up from 27% the year before, while the share of respondents from smaller organizations stayed flat at 22%.

Cost and connection work also hold teams back. McKinsey also found that AI-related operating costs are beginning to constrain AI use for about one in five organizations, and Salesforce found only 27% of enterprise applications are integrated with each other. Neither figure is specific to small businesses. If your business has no IT staff watching for a broken integration, the same gaps land on the people answering customers.

Prices checked 10 Nov 2026: n8n pricing page and Make pricing page.

What Does n8n Offer Service Businesses?

n8n connects business apps through workflows built from individual steps called nodes. As a fair-code, low-code workflow automation tool, it adds JavaScript and Python code steps, webhook triggers and custom HTTP requests, so a technical team can go past the standard prebuilt connectors.

For a service business, one workflow can validate an inquiry, apply routing rules, update the CRM and alert the account manager. The payoff depends on two things: whether those steps match your systems, and whether someone on your team can maintain them.

• Best for: Teams with a technical workflow owner, custom integration needs or a firm self-hosting requirement.
• Capabilities: App integrations, workflow logic, code steps and API connections.
• Pricing unit: Monthly workflow executions. The Starter plan includes unlimited steps per execution.

Strengths

• A four-step run and a twenty-step run each count as one execution.
• Self-hosting puts the workflow engine on infrastructure you control.
• Code and API steps handle integrations that prebuilt actions miss.

Limitations

• Self-hosting adds servers, updates, backups and recovery to your workload.
• Code-heavy workflows need someone who understands the logic when business rules change.
• n8n lists some developer features, such as custom nodes, for self-hosted n8n rather than the Starter plan.

What Does Make Offer Service Businesses?

Make builds automations as visual scenarios that connect app modules. Each action, such as reading, creating or updating a record, consumes credits.

Before you choose Make, map every action in lead intake, scheduling, onboarding or invoicing. Count the repeated records too. A scenario is easy to budget once every action is on paper.

• Best for: Teams that want hosted app workflows without running their own automation server.
• Capabilities: Visual scenarios, app connections, data processing and custom app integrations.
• Pricing unit: Credits. Most actions consume one credit.

Strengths

• The Free plan, up to 1,000 credits per month, covers a small, tightly scoped pilot.
• Visual scenarios show exactly how data moves between apps.
• Make runs the engine, so there is no server for you to maintain.

Limitations

• Every added action and repeated record uses more credits.
• Some advanced AI features consume more than one credit per action.
• The Free allowance leaves zero headroom in the lead workflow modeled below.

What Problems Must Service-Business Automation Solve?

• Repeated data entry: A web inquiry, CRM record, booking and invoice often hold the same customer details. Copying them by hand delays follow-ups and lets records drift apart. Pick one system as the source of truth, then decide which system updates each field.
• Failures without an owner: One disconnected account or renamed field stops lead routing and payment reminders. Every automation needs a named owner, a failure alert and a recovery plan. Ask to see how staff find unfinished work and restart it without duplicate messages or records.
• Lead spikes that hit API limits: A seasonal rush or a batch CRM sync can send more requests than a connected app accepts at once. Test a burst of submissions in each tool and confirm how failed requests are retried, batched or queued, so no lead drops silently.
• Budgets built on subscriptions alone: The platform fee leaves out setup, maintenance labor, outside services and any infrastructure. AI adds its own line item. Compare the full operating cost against the work it removes from your team.
• Wrong financial data: Document extraction and automated matching need a check before they touch invoices or reports. Send uncertain results to a person and keep the original document. A fast workflow that posts the wrong amount creates reconciliation work and costs customer trust.
• Knowledge that leaves with the builder: Undocumented logins and unfamiliar logic make simple changes hard after handover. Keep accounts in the business’s name, document dependencies and assign a backup maintainer. Before sign-off, have a staff member make a real change without the original builder.
• Workflows locked to one platform: n8n exports workflows as JSON and Make exports scenarios as blueprint files, but neither format runs on the other platform. Moving later means rebuilding the logic, so document each workflow in plain language from day one.

n8n Executions vs Make Credits (Formerly Operations): How Billing Differs

The core pricing difference is what each tool counts. One n8n execution and one Make credit measure different amounts of work. Comparing the allowance totals side by side gives you the wrong answer.

On n8n Starter, one workflow run is one execution, no matter how many steps it has. On Make, each trigger run uses one credit and most standard actions use one credit each. A lead that passes through a trigger and three actions uses four credits.

Make switched its billing unit from operations to credits in August 2025, so older guides still say “operations.” Its pricing page notes that some advanced AI features use more than one credit. Check the consumption of every action you plan to use.

Two Make patterns raise the count quickly:

• Polling triggers: Make’s credit documentation counts the first module of every scenario run as one credit, even when it returns no data. A trigger that polls every 15 minutes runs 96 times a day, or 2,880 times in a 30-day month, almost three times the Free plan’s allowance of up to 1,000 credits per month. Use an instant (webhook) trigger where the app offers one.
• Line items and loops: An iterator uses one credit to split an array, then each later module uses one credit per item. A 10-line invoice passing through two modules after the iterator uses 21 credits. On n8n, the same items stay inside one execution.

Keep outside charges separate. Your CRM, messaging provider and AI service each bill on their own terms. Neither an execution allowance nor a credit allowance covers the full cost of the process.

n8n vs Make Pricing: What Does One Lead Workflow Cost?

This is an illustrative model, not a client case study. It assumes 250 new leads a month, an instant (webhook) trigger and one completed workflow per lead, with no retries, extra runs or other automations.

Each lead follows the same four steps: receive the inquiry, create the CRM record, send an acknowledgment and alert the assigned salesperson. In Make, that is one incoming webhook trigger plus three standard action modules. Make’s credit documentation counts one credit per trigger run, including runs that return no data, and one credit per standard operation.

What Does the Make Calculation Show?

250 leads × (1 trigger run + 3 action modules) × 1 credit = 1,000 credits per month. Make’s Free plan offers up to 1,000 credits per month, so the model uses the entire allowance.

The workflow fits inside the Free plan’s credit allowance, as long as it also stays within the plan’s other limits. There are zero spare credits for anything extra, including polling checks or retries. Confirm credit consumption and plan limits on Make’s pricing page before you launch.

What Does the n8n Calculation Show?

250 leads × 1 execution = 250 executions per month. Starter includes 2,500 executions a month, so the workflow uses 10% and leaves 2,250 for other automations.

This model uses the Starter price n8n lists: $20 per month, billed annually. That is $20 × 12 = $240 a year, or $20 ÷ 250 = $0.08 per modeled lead each month.

The n8n pricing page lists Starter at $20/month billed annually, with 2,500 workflow executions per month and unlimited steps. Confirm the price shown at checkout before you set a budget.

What Does the Model Leave Out?

The model excludes setup, ongoing support, outside app fees, AI usage and extra processing. It also assumes your apps support every required action. Run a real test before you commit.

Make costs less in this narrow example. n8n leaves far more of its allowance unused, but spare capacity is not the same as better value. The answer changes once your real workflow adds actions, polling, line-item loops or hosting requirements.

Who Controls Hosting and Customer Data?

n8n’s hosting documentation supports running it on your own infrastructure, including Docker installs, on premises or in a private cloud. n8n also sells hosted plans. You choose where the engine runs.

Self-hosting does not keep every transaction inside your network. A record sent to a cloud CRM or an outside AI provider still leaves your systems. Document each destination separately from the automation engine.

Make runs its infrastructure on AWS. Its on-premise agent, available to Enterprise customers, lets scenarios reach apps and databases on your local network. That is a connection feature, not a self-hosted Make engine.

For either tool, list the data entering each step, the credentials that grant access and the records kept for troubleshooting. Match those needs to documented settings and contract terms. Hosting location answers only part of the data question.

How Should You Test Reliability Before Launch?

Test both tools on the same workflow with the same pass criteria. Include missing fields, duplicate submissions, expired credentials and an app that rejects an update. A good demo shows recovery as clearly as success.

• Connector coverage: Confirm the exact action you need, including custom fields and attachments. An app name on the integrations list is not enough.
• Duplicate prevention: Set a stable ID before the workflow creates CRM records, appointments or invoices.
• Failure recovery: Walk through the alert, inspection, fix and safe replay of unfinished work.
• Handover: Require business-owned accounts, written documentation and a named maintainer.

Measure business outcomes, not just technical runs. Track inquiries reaching the right owner, acknowledgments delivered, the exception backlog and time spent fixing records. Pick the measures that match your team’s actual work.

Different service-business operating models need different tests. An agency onboarding flow needs approval checks. A multi-location service team needs accurate routing and clear location ownership.

n8n vs Make for AI Agents: What Changes When You Add LLMs?

AI brings variable outputs and extra usage into an otherwise predictable workflow. Keep fixed rules, such as required fields and routing permissions, as explicit logic. Use AI where judgment adds value, like classifying an inquiry or pulling data from a document.

The two tools package agents differently. n8n’s AI nodes are built on LangChain, and an agent run inside a workflow counts as one execution, while your model provider bills its own usage. Make’s new AI Agents app is in open beta and available on all plans through Make’s AI Provider. Connecting a custom AI provider requires a paid plan, and Make’s AI Agents credit documentation notes that credits can include token-based use.

Before you hand work to AI agents, decide what needs human approval and what happens when output is incomplete. Set spending limits. Review results before any automated financial update goes live.

Give an agent more authority only after it proves three things: accurate processing, manageable exceptions and a clear owner. That is how service teams get measurable results from AI.

Which Tool Should You Choose, and What If Neither Fits?

Answer three questions. Who maintains the workflow? What does one completed transaction consume? Where must the engine run?

Choose Make when hosted scenarios fit and your measured credit use, including trigger checks and loops, stays inside the plan. Choose n8n when self-hosting or custom logic justifies the upkeep. When your process outgrows both, the answer is a custom build: a CRM, client portal or command center you own.

Brixx Digital, which publishes this guide, builds those systems for service businesses. Explore our automation solutions, or see Brixx Digital pricing before you plan a build.

Frequently Asked Questions (FAQs)

Is Make cheaper than n8n for a small service business?

In our model, yes. Make’s Free plan offers up to 1,000 credits per month, which covers 250 leads at one webhook trigger plus three standard actions each. n8n Starter is $20/month billed annually. Polling checks, extra actions, setup and outside services change the total.

Does one n8n execution equal one Make credit?

No. An n8n execution is one full workflow run. Make counts one credit per trigger run and one per standard action. The modeled lead workflow uses one n8n execution, or four Make credits (one webhook trigger plus three actions), per lead.

Should we self-host n8n for more data control?

Self-host when running the engine on your own infrastructure is a real requirement and someone owns its maintenance. Connected cloud apps still receive whatever data you send them. Map the full data flow before you pick a deployment.

Is n8n harder to learn than Make for a non-technical team?

Both tools use visual builders. n8n’s strengths, such as code steps, custom API requests and self-hosting, assume someone is comfortable with technical upkeep. Make runs the engine for you, but your team still has to learn how each scenario uses credits. Have a staff member build the same pilot workflow in both before you decide.

Which is better for building AI agents: n8n or Make?

n8n suits teams that want LangChain-based agent nodes, self-hosting and one execution per agent run, with the model provider billed separately. Make suits teams that want agents inside hosted scenarios. Its AI Agents app is in open beta on all plans through Make’s AI Provider, custom provider connections need a paid plan, and credits can include token-based use. In both, keep human approval on financial updates.