Make vs n8n: Pricing, AI Agents, and Verdict
Make is the faster no-code automation platform for non-technical users, billed per operation and cloud-only, while n8n is a low-code platform that you can self-host, billed per workflow execution, and stronger for AI agents. Make uses a colorful visual builder where every module run counts as one operation. n8n uses a node canvas with a built-in code step, and its Community Edition runs free on your own server with unlimited executions.
Both tools connect apps, move data, and run scheduled or triggered workflows. The decision usually comes down to pricing model, technical comfort, data control, and how much AI logic the workflows carry.
This comparison covers pricing, ease of use, integrations, self-hosting, AI agent support, and user feedback from Reddit and LinkedIn. It uses the official Make and n8n documentation plus independent 2026 reviews. For a related comparison, see AI Comparison.
Make vs n8n at a Glance
Make wins on ease of use, integration count, and low-volume cost, while n8n wins on self-hosting, data control, per-execution pricing at scale, and AI agent workflows. The table below frames the detailed sections. For the wider field, see n8n vs Zapier.
| Attribute | Make | n8n |
|---|---|---|
| Hosting | Cloud only | Cloud or self-hosted |
| License | Proprietary SaaS | Fair-code, source-available |
| Billing unit | Per operation (each module run) | Per workflow execution, or server cost |
| Free plan | 1,000 operations per month | Self-host free, cloud trial |
| Entry paid price | Around $9 to $10 per month | Around $22 to $26 per month cloud |
| Integrations | 2,000 or more prebuilt apps | 500 or more, plus custom HTTP and code |
| Custom code | Limited functions | Full JavaScript and Python code nodes |
| AI agents | AI Agents in beta since 2025 | Native AI Agent nodes and LangChain |
What Is Make?
Make, formerly Integromat, is a cloud no-code automation platform that connects apps through a visual scenario builder and charges for each individual module that runs. You drag apps onto a canvas, link them with routers and filters, and Make executes the scenario on a trigger or schedule. Every step that processes data counts as one operation against your monthly quota.
Make focuses on approachability. Its interface is highly visual, it has more than 2,000 prebuilt app integrations, and its debugger shows exactly which module failed and why.
Make runs only in the cloud. A dedicated instance is available as Private Cloud on Enterprise plans, but there is no free self-hosted version. Make added AI Agents in beta in 2025 for tasks like text generation, routing, and classification.
What Is n8n?
n8n is a source-available, low-code workflow automation tool that you can run on n8n Cloud or self-host for free, billed per full workflow execution rather than per step. A workflow that runs 40 nodes still counts as 1 execution, which changes the economics of large or AI-heavy automations. It uses a node-based canvas with a Code node for custom JavaScript or Python.
n8n targets technical users and teams that want control. Self-hosting with the Community Edition is free beyond server costs, and it keeps all workflow data inside your own infrastructure.
n8n leads on AI workflows in 2026. It ships native AI Agent nodes, LangChain integration, vector store nodes, and tool-calling, so a workflow can use a model as a decision-maker rather than a single step. Its cloud free tier limits active workflows, and it has fewer one-click integrations than Make.
Feature Comparison: Make vs n8n
Make leads on prebuilt integrations and beginner UX, while n8n leads on custom logic, self-hosting, and AI agent depth. These 5 feature areas separate them.
Ease of use
Make is designed for people with no coding background, with guided module configuration and a clean visual flow. n8n is usable without code for simple flows but expects more comfort with data structures, expressions, and occasional scripting.
Integrations
Make has more than 2,000 native app connectors with prebuilt actions and triggers. n8n has fewer native integrations but covers gaps with a flexible HTTP Request node and community nodes.
Custom logic and code
n8n includes full JavaScript and Python code nodes, branching, loops, and sub-workflows. Make offers built-in functions, iterators, and aggregators but no general-purpose code environment.
Self-hosting and data control
n8n can run entirely on your own server, which suits strict data privacy and compliance needs. Make is cloud only unless you are on an Enterprise Private Cloud plan.
AI agent workflows
n8n provides AI Agent nodes, memory, and tool use for multi-step reasoning. Make’s AI Agents are newer and best suited to single-shot tasks today.
Performance Comparison: Cost at Scale and Reliability
Make is cheaper and simpler at low volume, while n8n becomes far cheaper as workflows grow in volume or steps per run. The table shows how each behaves as usage increases.
| Scenario | Make | n8n |
|---|---|---|
| Under 3,000 simple runs per month | Cheaper on the Core plan | Cloud plan costs more |
| High-volume multi-step workflows | Operations add up quickly | 1 execution per run, much cheaper |
| AI agent workflow with 20 steps | 20 operations per run | 1 execution per run |
| Self-hosted at scale | Not available | Server cost only, unlimited runs |
| Debugging a failure | Visual module-level debugger | Execution log and branching |
The crossover point is the key number. Below roughly 3,000 runs per month, Make’s entry plan is the cheaper option, and above that, especially for AI workflows, n8n’s per-execution model or self-hosting wins by a wide margin.
Pricing: Make vs n8n
Make starts at around $9 per month for 10,000 operations, while n8n Cloud starts near $26 per month for 2,500 executions and n8n self-hosted is free beyond server costs. The table lists the common tiers.
| Plan | Price | Included volume |
|---|---|---|
| Make Free | $0 | 1,000 operations per month |
| Make Core | Around $9 to $10 per month | 10,000 operations per month |
| Make Pro | Around $16 to $19 per month | 10,000 operations, more features |
| n8n self-hosted | Server cost only, often $5 to $10 per month | Unlimited executions |
| n8n Cloud Starter | Around $22 to $26 per month | 2,500 executions per month |
| n8n Cloud Pro | Around $55 to $66 per month | 10,000 executions per month |
An operation on Make and an execution on n8n are not the same unit. A single workflow run can be 1 execution on n8n but 15 or more operations on Make, which is why complex automations shift the value toward n8n.
Pros and Cons
Make’s strengths are speed to a working automation and integration breadth; n8n’s strengths are control, code, and cost efficiency at scale. The lists below cover 4 points each.
Make advantages:
- Very approachable visual builder for non-technical users
- More than 2,000 prebuilt app integrations
- Clear module-level debugging
- Cheapest option for low-volume automations
Make drawbacks:
- Per-operation billing gets expensive on multi-step workflows
- Cloud only, with no free self-hosting
- Limited custom code
- AI Agents still early compared with n8n
n8n advantages:
- Free, unlimited self-hosting with full data control
- Per-execution billing that scales well
- Full JavaScript and Python code nodes
- Deep native AI agent and LangChain support
n8n drawbacks:
- Steeper learning curve than Make
- Fewer one-click integrations
- Cloud free tier limits active workflows
- Self-hosting adds maintenance work
User Reviews and Community Feedback
Automation builders start on Make for its simplicity and move to n8n as workflows get complex, costly, or AI-driven. These themes come from Reddit and LinkedIn. A related breakdown is Best AI Marketing Tools.
On r/automation, a widely read “Make.com vs n8n” thread concluded that “both are great, but Make.com is more user-friendly, while n8n is better for flexibility and self-hosting.” Multiple r/n8n threads ask whether it is worth switching from Make, usually citing pricing and flexibility as the reason. See also Make vs Zapier.
Make feedback praises the on-ramp. Beginners describe building a first working automation in an afternoon, and small teams like the visual debugger for spotting a broken step. More on this in AI Comparison Tools.
n8n feedback centers on scale and AI. LinkedIn builders describe Make as best for “simple A to B to C integrations” and n8n as the tool that “brings visual programming with actual logic” for loops, data workflows, and agents.
Use Cases: When to Choose Make or n8n
Choose Make for quick business automations and non-technical teams, and choose n8n for high-volume, code-heavy, or AI agent workflows and strict data control. These 6 scenarios map each tool to a job.
Choose Make for:
- Marketing, sales, and operations automations built by non-developers
- Connecting many SaaS apps with prebuilt modules
- Low-volume workflows where the Core plan is enough
- Teams that want the fastest path to a working automation
Choose n8n for:
- High-volume or multi-step workflows where operation costs balloon
- AI agent workflows with memory and tool use
- Deployments that must keep data on your own servers
- Developers who want custom code inside automations
Final Recommendation
Pick Make if speed and simplicity matter most and your volume is modest, and pick n8n if you need control, scale economics, or serious AI automation. Many teams prototype on Make and rebuild the workflows that matter on n8n. These 3 questions decide it.
Choose Make if:
- The people building automations do not write code
- Your monthly run volume stays under a few thousand
- You want the widest set of prebuilt integrations
Choose n8n if:
- Your workflows are long, frequent, or AI-driven
- Data must stay inside your own infrastructure
- You want to self-host and remove per-run limits
Alternatives to Make and n8n
The strongest alternatives in 2026 are Zapier for the easiest setup, Microsoft Power Automate for Microsoft-heavy teams, and Activepieces for an open-source option closer to n8n. Each shifts the balance of ease, ecosystem, and openness.
Zapier has the largest app catalog and the simplest builder, at a higher per-task price. Power Automate fits organizations already in Microsoft 365, and Activepieces is a fair-code, self-hostable tool for teams that want an n8n-style model with a simpler interface.
Frequently Asked Questions
Is Make or n8n better?
Make is better for non-technical users and fast, simple automations. n8n is better for developers, high-volume workflows, AI agents, and teams that need self-hosting.
Is n8n cheaper than Make?
n8n is cheaper at scale, especially self-hosted or for multi-step and AI workflows, because it bills per execution rather than per module. Make is cheaper for low-volume automations under about 3,000 runs per month.
Can you self-host Make like n8n?
No, Make does not offer standard self-hosting. A dedicated instance is available only as Private Cloud on Enterprise plans, while n8n self-hosting is free with the Community Edition.
Which is better for AI agents, Make or n8n?
n8n is the stronger platform for AI agents in 2026, with native AI Agent nodes, memory, and LangChain integration. Make added AI Agents in beta and works best for single-shot AI tasks.
Does Make have more integrations than n8n?
Yes, Make has more prebuilt integrations, with over 2,000 native app connectors against roughly 500 for n8n. n8n closes the gap with a flexible HTTP Request node and community nodes.
Should a beginner start with Make or n8n?
A beginner should usually start with Make because of its visual builder and gentler learning curve. Moving to n8n later makes sense once workflows grow complex or costly.
Final Verdict
Make and n8n serve different builders: Make is the no-code tool that gets non-technical teams to a working automation fastest, and n8n is the low-code tool that scales with volume, code, and AI. If your automations are straightforward and your volume is modest, Make is the simpler and cheaper choice. If your workflows are large, frequent, AI-driven, or subject to data rules, n8n’s per-execution pricing and self-hosting make it the better long-term platform.
For most non-technical teams, Make is the right starting point. For developers and anyone running AI agent workflows at scale, n8n is worth the steeper learning curve.
