n8n vs Make 2026: Pricing, AI, and Self-Hosting Compared

n8n vs Make is the sharper question once you’ve ruled Zapier out on price. These two tools are closer to each other than either is to Zapier. Both have visual workflow builders. Both support complex logic. Both are popular with technical teams in Europe.

But they charge differently, they handle AI differently, and one of them you can self-host. Those differences matter more than most comparison articles let on, especially once your workflows process lists or call AI models. For the broader landscape before you commit to either, the workflow automation tools overview covers the full category.

I build automations with both. Here’s what actually separates them.

This is what most people miss

Make and n8n both have visual builders, both support branching and error handling, and both connect to hundreds of apps. The real difference is how they count usage, and it changes the economics of everything.

Make charges per credit. Until August 2025, Make counted “operations.” Each step in a scenario used one operation. They’ve since renamed it to “credits,” but the logic is the same: every module that runs costs at least 1 credit. A 10-step scenario running once = 10 credits minimum.

n8n charges per execution. One execution = one complete workflow run, no matter how many nodes it touches. That same 10-step workflow? One execution on n8n.

This gets worse with iterators. If a Make scenario processes a list of 200 orders, the iterator alone costs 200 credits (one per item), plus whatever each downstream module consumes per item. On n8n, processing 200 items in a single run is still one execution.

There’s also a new wrinkle since 2025: Make’s AI modules consume variable credits. A simple AI text generation might cost 1.1 credits. A complex document analysis could be 2.5 or more. The exact amount depends on token usage, file size, and processing time. This makes AI-heavy scenarios harder to predict on Make.

What each tool actually costs

Make (2026 pricing)

Make bills monthly or annually (annual saves ~15%). All plans start at 10,000 credits/month and scale up.

You can scale credits within each plan. Core maxes out at 300,000 credits/month. Pro and Teams go up to 8,000,000. Unused credits now roll over for one month (new in 2026).

One useful feature: all paid plans now let you connect your own AI API keys (OpenAI, Anthropic, etc.) for just 1 credit per operation, so you pay your AI provider directly instead of Make’s variable credit rate. This was previously locked to Pro and above.

n8n (2026 pricing)

n8n Cloud plans are in EUR. Self-hosted is free with no execution limits. The full pricing breakdown including hidden costs lives in our n8n pricing guide.

All cloud plans include unlimited workflows, unlimited users, and all integrations. No features are gated. You only pay for more executions. n8n also has a startup program: 50% off the Business plan for companies with fewer than 20 employees.

Three real scenarios

The per-step vs per-run billing model means the cost gap between Make and n8n grows with workflow complexity. Here’s what that looks like in practice.

Small business: ~1,000 workflow runs/month

Five simple automations, averaging 5 steps each, running a few times a day.

At this scale, Make is actually cheaper than n8n Cloud. Self-hosted n8n still wins on raw cost, but Make’s $9/month Core plan is hard to argue with for simple use cases.

Medium business: ~10,000 runs/month

Ten automations averaging 8 steps each, running throughout the day.

Here the gap opens. n8n Cloud is 30-40% cheaper because you’re paying for 10,000 runs, not 80,000 step-level credits. Self-hosted is a fraction of either.

High volume: ~50,000 runs/month

Complex operations (order processing, inventory sync, reporting) with 10+ step workflows.

At this volume, cloud pricing is roughly comparable. The real story is self-hosted n8n at ~€40/month versus either cloud option at €700+. That’s where self-hosting pays for itself many times over.

Where each tool is stronger

Make has the better out-of-box experience. n8n has the better engine under the hood. That’s the short version.

AI is where they’ve diverged the most

Both Make and n8n have added AI features in 2025 and 2026, but they’ve taken very different approaches.

Make’s approach: AI as a feature. Make has built-in AI modules (text generation, summarization, image analysis) that run through Make’s own AI infrastructure. They’ve also added Maia, a natural-language builder that generates scenarios from plain English descriptions. In late 2025, they launched visual AI Agents on the canvas with a reasoning panel. It’s useful, but it’s AI bolted onto an automation platform.

n8n’s approach: AI as infrastructure. n8n ships 70+ dedicated AI nodes built on LangChain. You can build full retrieval-augmented generation (RAG) pipelines: ingest documents, split text into chunks, create vector embeddings, store them in Pinecone or Qdrant, and query them with any LLM. The Agent node supports ReAct-style reasoning with tool use, conversation memory, and multi-model chaining.

n8n also supports MCP (Model Context Protocol), which lets your workflows act as tool servers for external AI agents, or consume tools from other MCP servers. Make doesn’t have this.

If you’re adding “summarize this email” to a workflow, either tool handles it. If you’re building an AI agent that researches leads, scores them against your ICP, drafts personalized outreach, and logs everything to your CRM, that’s n8n.

The option Make doesn’t have

This is a binary difference: you can self-host n8n, you cannot self-host Make.

For some businesses this doesn’t matter. Make offers EU data centers, and if you’re comfortable with cloud-hosted data processing, Make’s infrastructure is fine.

But for businesses with strict compliance requirements (GDPR data processing agreements, industry-specific regulations, or clients who contractually require data to stay on specific infrastructure), self-hosting is a hard requirement. n8n is the only option here.

Self-hosted n8n isn’t free in the real sense. I covered this in detail in my n8n vs Zapier comparison, but the short version: a Hetzner VPS runs about €5/month, but you’re also investing time in setup, maintenance, updates, and monitoring. For my clients, I handle all of that. They get the cost and compliance benefits without the operational burden.

Make is the right choice when…

• Your team isn’t technical. Make’s visual builder is more polished than n8n’s. The drag-and-drop interface is genuinely easier for non-developers. If no one on your team writes code, Make has the lower learning curve of the two.
• You need a specific integration. Make has 3,000+ pre-built connectors. n8n has 400 official nodes. The gap is partly closed by n8n’s 5,800+ community nodes, but if you need a reliable, maintained connector for a niche app, Make is more likely to have it.
• Your workflows are simple and low-volume. At 1,000 runs/month with 5-step workflows, Make’s $9/month Core plan is genuinely cheaper than n8n Cloud. If you don’t need code flexibility, self-hosting, or advanced AI, Make gives you more for less at this scale.
• You want AI scenario building. Maia, Make’s natural language builder, is available on every plan including free. Describe what you want in plain English, and it generates the scenario. n8n doesn’t have an equivalent.
• You value predictable operations over flexibility. Make’s managed infrastructure means zero server management, guaranteed uptime, and professional support. For businesses that want automation to “just work” without any infrastructure thinking, that’s worth paying for.

n8n is the right choice when…

• Your workflows process lists or batches. Any workflow that loops through items (orders, invoices, contacts, products) is dramatically cheaper on n8n because of the per-execution billing. This is the single biggest cost driver.
• You need code inside your workflows. Full JavaScript and Python nodes, not sandboxed snippets. If your automation requires real data transformation, API manipulation, or custom logic, n8n treats code as a first-class citizen.
• You’re building AI-powered workflows. The gap is significant. n8n’s LangChain integration, agent nodes, RAG pipelines, and MCP support put it in a different category than Make for AI automation.
• Data sovereignty is a requirement. Self-hosted n8n on your own infrastructure means complete control over where data lives and how it’s processed. For GDPR compliance or client contracts that mandate specific data handling, this is often non-negotiable.
• Cost matters at scale. The per-execution billing model means n8n gets relatively cheaper as workflows get more complex. And self-hosted n8n at €5-40/month replaces cloud subscriptions that run into hundreds.
• You have technical capacity. Either on your team or through someone like me. n8n rewards technical investment with dramatically lower costs and more capability. But it does require that investment.

n8n vs Make FAQ

Is n8n cheaper than Make?

For low-volume simple workflows (under ~5,000 monthly steps), Make Core at $9/month is actually cheaper than n8n Cloud Starter at €24/month. The break-even shifts to n8n once workflows process lists, call AI APIs, or have more than ~6 to 8 steps per run, because n8n charges one execution per workflow run regardless of complexity. Self-hosted n8n is the cheapest option at any volume above trivial.

Can I migrate from Make to n8n?

Yes. Workflows in both tools export to JSON. There is no automated converter, but the building blocks map cleanly: Make modules become n8n nodes, Make iterators become n8n’s loop-over-items behavior (which is free, since the whole loop runs in one execution). Plan on rebuilding rather than translating, but the architecture transfers in hours, not days, for typical scenarios.

Does Make have better integrations than n8n?

Make claims 3,000+ pre-built apps. n8n has 400+ official nodes plus 5,800+ community nodes. For mainstream apps (Google Workspace, Slack, HubSpot, Stripe, Shopify), both cover everything. Make has the edge on long-tail niche integrations. n8n has the edge on AI, vector databases, and self-hosted-first tools (Postgres, Redis, MinIO).

Is n8n harder to learn than Make?

Yes, modestly. Make has a more polished drag-and-drop builder and a natural-language scenario generator (Maia) on every plan. n8n’s canvas is closer to a code-aware tool: more flexible but less hand-holding. If your team prefers visual tools and doesn’t write code, Make has the lower learning curve. If your team is comfortable with even light scripting, n8n unlocks more capability per hour invested.

Can I self-host Make like n8n?

No. Make is cloud-only. They offer EU and US data centers, but the platform itself runs on Make’s infrastructure. If self-hosting is a hard requirement (GDPR data processing constraints, client contracts, or air-gapped environments), n8n is the only option of the two. Other self-hostable alternatives are covered in our n8n alternatives guide.

Which one is better for AI workflows?

n8n. Both have AI features, but the gap is significant. n8n has 70+ dedicated AI nodes built on LangChain, supports retrieval-augmented generation (RAG) pipelines end-to-end, has an Agent node with ReAct-style reasoning, and supports Model Context Protocol (MCP) for using your workflow as a tool server for external AI agents. Make’s AI is more “summarize this email” than “build an autonomous agent”. For occasional AI calls, both work; for AI-first workflows, n8n is in a different category.

What about Zapier?

Zapier is the third option in this space, but it’s typically 4 to 5x more expensive than n8n at the same workload because every step counts as a billable task. The full breakdown is in our n8n vs Zapier comparison.

What I tell clients

Make and n8n are closer competitors than either is to Zapier. But they serve different profiles:

Choose Make if your workflows are straightforward, your team prefers visual tools, and you’re running at low-to-medium volume. Make’s UX advantage is real, and at small scale the cost difference isn’t significant enough to justify n8n’s learning curve.

Choose n8n if you’re doing anything complex (batch processing, AI integration, custom code, multiple interconnected workflows) or if you need data to stay on your own infrastructure. The cost advantage compounds quickly once workflows have more than a few steps or process lists of items.

For most of my clients (European SMEs with real operational workflows) I build on n8n. The combination of self-hosting (for cost and compliance) plus full code and AI flexibility makes it the better long-term foundation. I set up the infrastructure, build the workflows, and handle the maintenance. They get Make-level ease of use on the operations side because they never touch n8n directly.

But I’ve also set up Make for clients where it was genuinely the better fit, usually small teams with simple needs and no one technical to maintain a self-hosted instance.

The right answer depends on your situation. If neither tool feels right, our n8n alternatives guide covers when to look elsewhere entirely.

If you’re weighing the two, send me an email and I’ll give you an honest take. First conversation is always free.