n8n vs Make (2026): Features, Pricing & Which Is Better?

Choosing between n8n and Make is no longer simply about finding a tool that can connect one app to another. Both platforms now support complex workflows, APIs, data processing, custom logic and AI-powered automation, but they approach those tasks differently.

Make focuses heavily on making automation visual and accessible. Its Scenario Builder is easier for marketers, operations teams, founders and other business users to understand without extensive technical knowledge. n8n also provides a visual workflow builder, but gives developers considerably more room to work with APIs, code, custom logic and their own infrastructure.

The growth of agent-based automation makes this distinction increasingly important. McKinsey's 2026 State of AI research found that 40% of respondents at organizations with more than $1 billion in annual revenue reported scaling AI agents, compared with 27% a year earlier. As businesses move from simple app connections toward longer automated processes, choosing an automation platform increasingly affects cost, maintenance and how much technical control a team has.

For most non-technical business users, Make is the easier starting point. For developers and technical teams that value self-hosting, custom APIs and deeper workflow control, n8n is usually the stronger choice. The better platform ultimately depends on the workflows you plan to build rather than which one has the longest feature list.

n8n vs Make: Quick Comparison

The important point is that the same feature can feel very different on each platform. Make emphasizes visual-first workflow building and its large integration ecosystem, while n8n is positioned more heavily toward technical teams that want to combine visual automation with code and infrastructure control.

What Is n8n?

n8n is a workflow automation platform built around a visual node-based editor. Users can connect applications, APIs, databases, webhooks and processing steps to create workflows without building every integration from scratch.

Where n8n becomes particularly interesting is when a workflow goes beyond standard app connections. Developers can incorporate JavaScript or Python, make custom HTTP and GraphQL requests, manipulate data and connect services that do not have a ready-made integration. n8n currently advertises 1,000+ integrations on parts of its integration site, while its main product pages emphasize hundreds of pre-built nodes alongside the ability to connect virtually anything through APIs.

Deployment is another major differentiator. n8n provides managed cloud plans as well as a self-hosted Community Edition. Technical teams can therefore run the platform on their own infrastructure when they need greater control over deployment or data.

That flexibility brings additional responsibility. A self-hosted installation still needs servers, backups, monitoring, security updates and ongoing maintenance, so “self-hosted” should not automatically be interpreted as “free.”

What Is Make?

Make, formerly known as Integromat, is a visual automation platform where workflows are called Scenarios. Users connect modules on a canvas and add filters, routers, transformations and other logic to control how information moves between applications.

Its main strength is visibility. A marketing or operations professional can look at a scenario and understand the general path data follows without needing to read code. That becomes useful not only when building an automation but also when troubleshooting it months later.

Make has also built a substantial integration ecosystem, advertising 3,000+ pre-built applications across business software, productivity, marketing and AI services. Its product has expanded beyond traditional no-code automation as well, with AI Agents and coding capabilities making more advanced workflows possible.

For users comparing a wider range of products, AI Tool Hunt's workflow automation tools category provides other platforms built for similar use cases.

n8n vs Make: Ease of Use

Make has the advantage for most beginners. Its Scenario Builder makes triggers, actions, filters and branching paths visually clear, allowing users to understand how information travels through a workflow. Someone familiar with a business process can often start building without first learning programming or server management.

This visual approach also helps with debugging. Individual modules expose the data moving through the scenario, making it easier to locate a step where an unexpected value or failed connection caused a problem.

n8n is visual as well, so describing it as developer-only would be inaccurate. Many useful workflows can be built entirely through nodes. The difference becomes clearer as complexity increases because n8n exposes users more directly to expressions, APIs, JSON structures, data transformations and code.

For technically experienced users, that can actually make difficult workflows easier to build because there are fewer restrictions when a standard node cannot do exactly what is required. For beginners, however, the same flexibility creates a steeper learning curve.

Make is therefore easier to start with, while n8n becomes increasingly attractive as the builder's technical requirements grow.

Integrations, APIs and Customization

Make has a clear advantage if your priority is having as many ready-made integrations as possible. Its 3,000+ pre-built apps cover many widely used services, reducing the need to configure APIs manually.

That matters for ordinary business automation. If your workflow needs to connect popular CRM, ecommerce, communication and productivity platforms, a maintained Make module can save considerable implementation time.

n8n's approach becomes more valuable when the integration you need does not already exist or does not support the exact action required. HTTP requests, GraphQL, webhooks and custom nodes allow technical users to connect directly to APIs, while JavaScript and Python can handle transformations or business logic that would otherwise be difficult to represent visually.

The distinction is therefore straightforward: Make is stronger when you want the integration to already exist; n8n is stronger when you are comfortable creating or extending the integration yourself.

This difference matters more as workflows become specialized. A standard CRM-to-email automation may be quicker in Make, while an internal system involving proprietary APIs, unusual data structures and custom processing is more likely to benefit from n8n's flexibility.

n8n vs Make Pricing in 2026

Pricing is one of the most important differences because n8n and Make do not meter automation in the same way.

n8n Pricing

n8n's cloud plans primarily charge according to workflow executions. An execution represents one complete run of a workflow rather than billing separately for every standard step inside that workflow.

As of September 2026, n8n lists Starter at €20 per month when billed annually for 2,500 workflow executions, while Pro starts at €50 per month for 10,000 executions. Its pricing documentation also states that plans include unlimited workflows and users and that executions can contain unlimited steps.

This model can be useful for complex workflows. If one automation contains dozens of processing steps, those individual steps do not automatically become dozens of separately billed executions.

n8n also offers a self-hosted Community Edition, but businesses should include server and maintenance expenses when comparing its cost with managed platforms.

Make Pricing

Make uses a credit-based model. Its Free plan provides 1,000 credits per month, while paid plans increase available credits and add more functionality.

For many conventional modules, an operation consumes a credit, although some AI and advanced features can use credits differently depending on the service and processing involved. This means workflow design can have a direct impact on consumption.

Consider a simplified scenario that runs 1,000 times per month and performs 15 standard billable operations each time. If each operation uses one credit, the workflow would consume around 15,000 credits per month.

The equivalent conceptual workflow could behave very differently under n8n's execution model because the complete workflow run, rather than every individual standard step, is the primary billing unit.

That does not make n8n universally cheaper. Simple Make workflows can be economical, while self-hosted n8n introduces technical overhead that does not appear in the subscription price. The useful comparison is therefore the cost of completing your real workflow at expected monthly volume, not simply the cheapest advertised plan.

n8n vs Make for AI Automation and Agents

Both platforms have moved aggressively into AI-powered automation, making this one of the most important parts of the comparison in 2026.

The workflows themselves are also becoming more complicated. Zapier's Q2 2026 AI Workflow Index found that among the high-adoption customers it studied, a typical AI workflow performed 220 automated actions per month compared with 102 for a conventional workflow. Interestingly, AI accounted for only a minority of workflow steps, showing that effective AI automation still depends heavily on traditional integrations, logic and data movement.

n8n is well suited to technical AI workflows because models can be combined with APIs, databases, custom code, retrieval systems and deterministic workflow logic. Its current AI platform also emphasizes human approval, traceable agent decisions and the ability to use different model providers. n8n reports more than 200,000 GitHub stars, illustrating the scale of the technical community around the platform.

Make approaches AI automation from a more visual direction. Its AI Agents can be incorporated into scenarios alongside the same applications and business processes users already automate. This can make agent-based workflows more approachable for operations and business teams that do not want to build custom orchestration infrastructure.

For users specifically comparing agent platforms, AI Tool Hunt's AI agent tools category provides additional options.

Neither platform has a universal advantage here. Make is particularly attractive for visually managed business AI workflows, while n8n is stronger when agents need extensive API access, custom code, infrastructure control or highly customized logic.

Self-Hosting, Security and Data Control

Self-hosting is one of n8n's clearest advantages. Its Community Edition can be deployed on infrastructure controlled by the user, while commercial options provide additional capabilities for organizations with more demanding governance requirements.

This can matter when workflows interact with private databases, internal systems or information that an organization wants to keep within its own infrastructure. n8n also supports deployment models that give technical teams considerably more control over where workflows execute.

However, self-hosting does not automatically mean better security. It transfers responsibility. Your organization becomes responsible for updates, network configuration, credentials, database security, backups and monitoring.

Make follows a managed-cloud model, which removes much of that operational work. Organizations have less control over the underlying deployment, but they also do not need to maintain the automation infrastructure themselves.

The choice therefore comes down to control versus convenience. If self-hosting is a genuine requirement and your team can maintain the environment properly, n8n is the obvious choice. If you want the provider to manage the infrastructure, Make is simpler.

Which Platform Is Better for Your Use Case?

Instead of treating Make and n8n as competitors with one universal winner, it is more useful to match each platform to the people who will actually use and maintain it.

Agencies are one area where the answer depends heavily on the client. An agency building standard CRM, marketing and lead-management workflows may find Make faster to deploy and easier to hand over. An automation agency building internal tools, custom integrations or sophisticated AI systems may find n8n more flexible over the long term.

n8n vs Make: Which Should You Choose?

Choose Make if your priority is getting business workflows running quickly through a visual interface, especially when the people maintaining those workflows are marketers, operations professionals, founders or other non-developers. Its large pre-built integration library and managed infrastructure reduce the technical work required for common automation.

Choose n8n if your workflows regularly require custom APIs, JavaScript or Python, unusual data transformations, self-hosting or deeper infrastructure control. Developers and automation engineers are more likely to benefit from the additional flexibility than to be slowed down by it.

Before committing to either platform, build the same realistic workflow in both. Avoid testing something trivial such as sending a form response to a spreadsheet. Instead, use a representative process involving an external API, branching, data transformation, an AI step, a CRM update, a notification and error handling.

Then compare how long the workflow takes to build, how easy it is to understand, what happens when you intentionally break a step and how much the expected monthly usage will cost. That practical test is far more useful than comparing feature counts alone.

Final Verdict

The n8n vs Make decision ultimately comes down to how much technical control your workflows require and who will be responsible for maintaining them.

Make is the better starting point for most non-technical users, marketing teams and operations teams. Its visual approach, large integration ecosystem and managed infrastructure make sophisticated automation accessible without requiring every workflow to become a development project.

n8n is the stronger choice for developers, technical automation teams and organizations that need self-hosting or extensive customization. Its combination of visual workflows, APIs, code and infrastructure flexibility gives technically capable teams more freedom when processes become complicated.

For AI automation, the gap is narrower because both platforms now support agent-based workflows. The deciding factor is less about whether AI features exist and more about how much control you need around the models, data, APIs and actions surrounding them.

If you are still undecided, build one representative production workflow in both platforms and compare build time, maintenance, debugging and actual monthly cost. The platform that performs better on those four factors is more likely to be the right choice than whichever one looks stronger on a generic feature checklist.

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