n8n vs Make (Integromat): Which Automation Platform Is Right for You in 2026?

n8n and Make (formerly Integromat) are two of the most capable workflow automation platforms available in 2026. Both have grown significantly over the past two years — n8n now powers over 60,000 self-hosted instances alongside its cloud offering, while Make serves millions of users with one of the most polished visual automation builders on the market. They solve the same core problem (connecting apps and automating workflows), but they approach it from fundamentally different directions.

This is not a post that declares a winner. The right choice depends on your technical comfort level, your data requirements, your budget trajectory, and whether you care about owning your infrastructure. We will walk through the differences honestly, with concrete numbers where they matter.

Quick Comparison Table

Before diving into the details, here is a high-level overview of where each platform stands in early 2026.

Pricing Deep-Dive

This is where the two platforms diverge most sharply, and where the decision often starts.

Make Pricing (2026)

Make uses an operations-based pricing model. Every action a module performs counts as one operation — reading a row from Google Sheets, sending a Slack message, making an HTTP request. Critically, polling triggers also consume operations even when they find nothing new. A scenario that checks an inbox every 5 minutes uses 8,640 operations per month just from the trigger, before it processes a single email.

Additional operations can be purchased. The Pro and Teams plans add features like custom variables, priority execution, and team management rather than more operations at their base price. For high-volume use cases, Make costs scale roughly linearly with throughput.

n8n Cloud Pricing (2026)

n8n counts executions, not operations. A single workflow execution can contain dozens of node operations and it still counts as one execution. This is a meaningful distinction: a Make scenario with 10 modules uses 10 operations per run; the equivalent n8n workflow uses 1 execution. For complex workflows with many steps, n8n Cloud is often 5–10x more cost-efficient per run than Make.

Self-Hosted n8n: The Third Option

Here is where the pricing comparison breaks down entirely in n8n's favor. Self-hosted n8n has no execution limits, no workflow limits, and no per-user fees (for the community edition). Your only cost is the server.

A team running 50 active workflows with moderate volume would spend €60/mo on n8n Cloud or $18.82+/mo on Make (likely more with additional operations). The same workload on a self-hosted VPS costs $9.58/mo with no execution ceiling. For a detailed cost breakdown, see our n8n pricing comparison.

Technical Capabilities

Both platforms are technically capable, but they serve different audiences and make different trade-offs.

Where n8n Wins

• Custom code everywhere. n8n lets you write JavaScript or Python in any workflow via Code nodes. You can transform data, call APIs directly, run complex logic — anything you can write in code, you can do in n8n. Make supports JavaScript in a more limited "custom function" context, but it is not the same level of flexibility.
• Self-hosting and source access. n8n is distributed under a fair-code license (Sustainable Use License). You can inspect the source, contribute fixes, and self-host without restrictions on the community edition. Make is a closed-source SaaS platform.
• Queue mode for scaling. Self-hosted n8n supports a queue-based execution mode with Redis and separate worker processes. This allows horizontal scaling across multiple containers or servers. Make handles scaling internally, but you have no control over execution priority or resource allocation.
• Webhook reliability on self-hosted. When you self-host n8n, webhooks hit your server directly. You control the infrastructure, the uptime, and the retry logic. On Make, webhooks go through Make's infrastructure — you are dependent on their uptime and their queue processing.

Where Make Wins

• Visual builder polish. Make's scenario editor is genuinely beautiful and intuitive. The circular module design, the drag-and-drop routing, the visual data flow — it is one of the best-designed no-code interfaces in the automation space. n8n's canvas editor is functional and improving, but Make's is more refined.
• Pre-built templates and modules. Make offers 2,000+ pre-built app integrations out of the box. n8n has roughly 400 first-party nodes plus hundreds of community-contributed nodes, but Make's library is larger and often more polished for common SaaS-to-SaaS integrations.
• Error handling UX. Make's built-in error handling — with visual error routes, retry modules, break modules, and ignore directives — is more intuitive for non-developers. n8n's error handling is powerful but requires more configuration.
• Zero infrastructure management. Make is fully managed. There are no servers to maintain, no Docker containers to update, no SSL certificates to manage. For teams without DevOps capacity, this is a legitimate advantage.

Self-Hosting: The Dealbreaker

This is the single largest differentiator between these two platforms, and it deserves its own section.

Make cannot be self-hosted. It is a closed-source, cloud-only platform. Your workflow definitions, execution data, credentials, and all processed data live on Make's infrastructure. You can choose between US and EU data regions, but that is the extent of your control.

n8n can be fully self-hosted. You run it on your own server, in your own data center (or your chosen provider), under your own domain. Workflow data never leaves your infrastructure. Credentials are encrypted with a key only you possess. There are no execution limits, no workflow limits, and no feature gates on the community edition.

For many teams, this is not a philosophical preference — it is a business requirement. If you process customer PII, financial data, healthcare records, or any information subject to data residency regulations, self-hosting gives you complete control over where that data lives and who can access it.

The practical cost of self-hosting n8n is a VPS and about 30 minutes of initial setup. On a MassiveGRID Cloud VPS with high-availability infrastructure, the starter configuration (2 vCPU / 4 GB RAM / 64 GB SSD) costs $9.58/mo and handles the majority of production workloads. Our complete Docker setup guide walks through the entire process from server provisioning to first workflow.

AI & LLM Capabilities

AI workflow support has become a major differentiator in 2026, and this is an area where n8n has moved decisively ahead.

n8n offers 70+ AI-specific nodes with deep LangChain integration. You can build multi-step AI agent workflows that chain LLM calls with tool use, retrieval-augmented generation (RAG), vector database queries, and custom function calling. The AI nodes support OpenAI, Anthropic Claude, Google Gemini, Mistral, Ollama (for local models), and other providers. Importantly, self-hosted n8n can run AI workflows that call locally hosted models — your prompts and responses never leave your server.

Make offers AI modules for OpenAI and a few other providers, but the integration is shallower. You can send prompts and receive completions, but building complex agent architectures with memory, tool use, and multi-step reasoning requires significantly more workaround. Make's strength has always been connecting SaaS applications, and its AI capabilities reflect that focus — adequate for simple "send this text to GPT and use the response" scenarios, but limited for anything more sophisticated.

If AI agent workflows are a core part of your automation strategy, n8n is the stronger choice by a significant margin. If you only need occasional LLM calls within otherwise traditional automation flows, both platforms handle that adequately.

Ease of Use & Learning Curve

This is where Make genuinely excels, and it would be dishonest to downplay it.

Make's visual builder was designed from the ground up for non-technical users. Scenarios are built by dragging circular modules onto a canvas and connecting them with visual data flow lines. The interface makes it immediately obvious how data moves through a scenario. Module configuration uses clear form fields with inline help. The template library lets you start with pre-built scenarios and modify them. For someone who has never built an automation before, Make is significantly more approachable than n8n.

n8n's editor is a canvas-based node graph that will feel familiar to anyone who has used tools like Node-RED, Blender's compositor, or Unreal Engine's Blueprints. It is powerful and flexible, but it assumes a certain level of technical comfort. Configuring nodes often involves understanding JSON structures, writing expressions, and occasionally debugging data transformations. n8n is investing heavily in UX improvements, and the editor has become noticeably more polished through 2025 and into 2026, but the learning curve remains steeper than Make's.

The trade-off is clear: Make is easier to start with, n8n is more powerful once you are up to speed. Teams with developers or technically inclined operators will find n8n's flexibility worth the initial learning investment. Teams composed primarily of marketing, sales, or operations staff may find Make's interface more productive.

Integration Ecosystem

Make offers 2,000+ pre-built app integrations. If you need to connect Shopify to Google Sheets to Slack to Mailchimp, Make almost certainly has dedicated modules for each with pre-configured fields. The breadth of Make's integration library is one of its strongest selling points, and for common SaaS-to-SaaS automation, it is hard to beat.

n8n has roughly 400 first-party nodes plus hundreds of community-contributed nodes, totaling over 1,100 integrations. The gap with Make is real for niche SaaS applications. However, n8n compensates with its HTTP Request node and Code node, which let you connect to any service with an API — and most modern services have APIs. Building a custom integration in n8n takes minutes, not days.

Both platforms support webhooks for receiving data from external services, and both can make arbitrary HTTP requests to services without dedicated integrations. The practical difference is whether you want a pre-built module with a form-based UI (Make) or are comfortable configuring API calls directly (n8n).

For enterprise and internal tool integrations (databases, custom APIs, internal services), n8n's flexibility with code nodes, SSH tunnels, and self-hosted network access often matters more than pre-built module count.

Data Privacy & Compliance

Automation platforms process sensitive data by nature — API keys, customer records, financial transactions, authentication tokens. Where that data lives and who can access it matters.

Make: All data is processed on Make's cloud infrastructure. You can choose US or EU data residency. Make is SOC 2 Type II certified and GDPR compliant. However, your workflow definitions, execution logs, and stored credentials are on Make's servers. You trust Make's security posture and their employees' access controls.

n8n Cloud: Similar model to Make — data is processed on n8n's managed infrastructure with EU hosting available. n8n is also SOC 2 certified.

n8n self-hosted: Complete data sovereignty. Everything — workflow definitions, credentials, execution data, processed customer records — stays on your server. You choose the jurisdiction, you control the encryption keys, you define the access policies. For organizations subject to GDPR, HIPAA, PCI DSS, or industry-specific data regulations, self-hosted n8n is the only option among these two platforms that offers full control.

If your workflows process data that cannot leave your infrastructure for legal or policy reasons, Make is not an option. n8n self-hosted is. For our guide on GDPR-compliant deployment, see GDPR-Compliant n8n Hosting in the EU.

Who Should Choose What

Choose Make If:

• Your team is primarily non-technical (marketing, sales, operations) and needs to build automations independently.
• You need a large library of pre-built integrations for common SaaS tools and want minimal configuration.
• Your automation volume is moderate (under 10,000 operations/month) and cost predictability matters more than cost minimization.
• You have no requirement for self-hosting, data residency control, or running AI agent workflows.
• You value a polished, intuitive visual builder over raw technical flexibility.

Choose n8n Cloud If:

• You want n8n's technical capabilities (code nodes, AI integration, complex data transformations) without managing infrastructure.
• Your team is technically comfortable but does not have DevOps capacity for server management.
• You are starting with a moderate number of workflows and may scale to self-hosting later.

Choose Self-Hosted n8n If:

• Cost control at scale is important — you want to pay for infrastructure, not per-execution.
• Data sovereignty is a requirement. Your workflow data, credentials, and processed records must stay on infrastructure you control.
• You are building AI agent workflows that call local LLMs or process sensitive data through AI pipelines.
• You need unlimited workflows, unlimited executions, and no artificial feature gates.
• Your team has basic Docker and Linux skills, or is willing to follow a setup guide.

The Verdict

There is no universal winner here. Make and n8n are both excellent platforms that have earned their positions in the market.

Make is the better choice for non-technical teams that need a beautiful, intuitive automation builder with extensive pre-built integrations. It requires zero infrastructure knowledge, and its visual editor is genuinely best-in-class for no-code users. The trade-offs are clear: you pay per operation, you cannot self-host, and your technical ceiling is lower when you need custom logic or advanced AI workflows.

n8n is the better choice for technical teams, developers, and anyone who values infrastructure ownership. The self-hosting option is a genuine game-changer for cost control, data sovereignty, and unlimited scaling. The AI capabilities are substantially ahead of Make. The trade-off is a steeper learning curve and the responsibility of managing your own deployment (if self-hosting).

For readers of this blog, the decision often comes down to this: if you are comfortable with Docker and a Linux terminal — or willing to learn — self-hosted n8n on a $9.58/mo VPS gives you more capability, more control, and lower long-term costs than either Make or n8n Cloud. That is not marketing spin; it is arithmetic.