# Make vs n8n (2026): Cloud Ease vs Self-Hosted Power — Which Wins > Make vs n8n, compared honestly: per-operation vs per-execution billing, self-hosting, ease vs power, AI, a head-to-head table, when to pick each, and how to migrate. _Source: https://professionalstoolkit.com/articles/make-vs-n8n — The Professional's Toolkit · updated 2026-07-29_ --- > **TL;DR —** Make vs n8n is a fight between two genuinely powerful tools, decided by three questions: **do you want to self-host, how many steps do your workflows have, and how deep does your AI need to go?** n8n is open-source, self-hostable, bills per *workflow run* (dramatically cheaper for complex, many-step automations), and has pulled ahead on AI-native depth. Make is cloud-first, easier to learn, connects to far more apps out of the box, and is the faster way to ship an AI workflow a non-engineer can maintain. Developer + control + complex/AI-heavy: n8n. No-code polish + breadth + gentle cloud start: Make. **1 run = 1 execution** — n8n counts a whole workflow as one execution regardless of steps — which is why it can be 10–20× cheaper than Make for complex, many-step automations. - **$9 vs $24** — Make / n8n cloud entry (per mo) - **Self-host** — n8n only — free, unlimited executions - **10–20×** — n8n cheaper on complex, multi-step flows ## The one-line answer Choose **n8n** if you have technical hands on the team and either want to self-host (data sovereignty, flat cost), run complex many-step workflows where its billing model saves you a fortune, or need deep AI (RAG, local models, multi-agent orchestration). Choose **Make** if you want the smoother no-code experience, the widest out-of-the-box app coverage, a cheaper cloud entry point, and don't want to own any infrastructure. Unlike the Make-vs-Zapier decision — which is mostly ease vs cost — this one is genuinely close on power; it turns on *ownership*, *workflow shape*, and *how far your AI ambitions go.* ## The real difference: how they count usage This is the crux, and it's the opposite of a rounding error. **Make bills per operation** — every module in a run counts. **n8n bills per execution** — one whole workflow run counts as **one execution, no matter how many steps it contains.**[1] Put a five-step workflow next to itself: on Make that's ~5 operations per run; on n8n it's **1 execution**. Scale that across thousands of runs and the gap is enormous — n8n can be **10–20× cheaper than Make for complex, multi-step automations.**[1] The catch is what you pay in the other direction. n8n Cloud starts at **$24/mo for 2,500 executions** (Pro is $60/mo for 10,000),[2] versus Make's **$9/mo for 10,000 operations** — so for *simple, low-step* workflows at modest volume, Make is actually the cheaper cloud option. The crossover is complexity: the more steps per workflow, the more n8n's per-execution model pulls ahead. Model your own shape before assuming either is "cheaper." > 💡 **Count your steps before you choose:** Few steps at modest volume → Make's $9 cloud wins. Many steps at scale → n8n's per-execution model wins big. The crossover is workflow complexity, not brand loyalty. ### A worked cost comparison Put real numbers on it. Take a **10-step workflow** running **1,000 times a month.** On **Make**, that's ~10 operations × 1,000 = **10,000 operations** — exactly Core's monthly allowance, so you're at $9 but with no headroom.[2] On **n8n**, it's 1 execution × 1,000 = **1,000 executions** — comfortably inside the $24 Starter tier, or effectively free if self-hosted.[2] At this level they're close, with Make cheaper on the cloud sticker. Now scale the *same* workflow to **5,000 runs/month.** Make jumps to **50,000 operations** — five credit packs on top of Core. n8n needs **5,000 executions** — the $60 Pro cloud tier covers it, or **unlimited on a ~$7/mo self-hosted server.**[2] The more steps per run and the higher the volume, the wider n8n's lead. Flip it around, though — a **2-step workflow at low volume** — and Make's $9 entry beats n8n's $24 comfortably. That's the whole decision in one line: **step-count × volume** is the dial, and it moves the answer. ## Self-hosting: n8n's trump card (and its hidden bill) Here's the option Make simply doesn't offer. n8n's **Community Edition is free, open-source software with unlimited executions** — you run it on your own server for roughly **$5–10/mo** in hosting, and self-hosting saves most teams **$200–700/year** versus cloud.[2] More importantly for some, self-hosting means your automation data never leaves infrastructure you control — a real requirement for regulated industries, privacy-sensitive workloads, or anyone with a strict data-residency policy. Make is cloud-only; if "it must run on our servers" is on your list, the comparison ends here in n8n's favor. Be honest about the trade, though, because "free software" isn't "free." A production self-hosted n8n typically wants **Postgres, Redis and queue workers** behind it, plus updates, backups and uptime monitoring — real operational overhead that lands on whoever owns the box.[3] So the true comparison isn't "$7 server vs Make's $9"; it's "$7 server *plus* engineering time vs Make's $9 and zero maintenance." For a team with a capable engineer, that overhead is trivial and the savings are real. For a team without one, Make's managed cloud is the cheaper option once you price in the hours. Freedom with a maintenance bill attached. ## Integrations: Make's quiet advantage This one rarely makes the headline but decides more real projects than it should. **Make connects to far more apps out of the box** — on the order of **1,800+ native integrations** — while **n8n ships around 400+**, extended by **500+ community-published nodes.**[3] For most teams that difference is invisible; the tools you use are covered on either. But when you need a mid-market SaaS with a native connector, Make is likelier to have it ready, whereas on n8n you may be reaching for the community node or the HTTP request. And the community-node route carries a caveat n8n itself flags: **installing community nodes means running unverified third-party code on your instance**, which requires self-hosting and a security judgment call.[3] Both platforms cover "anything with an API" through a generic HTTP module, so neither hits a hard wall — but Make's out-of-the-box breadth is a genuine time-saver, especially for non-technical builders who'd rather not vet npm packages. ## AI: n8n has gone deeper, Make ships faster Here's where our read has shifted, and we'd rather be straight about it than flatter the product we score well. Through 2026, **n8n has pulled ahead on AI-native depth.** It offers native **LangChain integration, an AI Agent node for autonomous tool use, vector-database support, local models via Ollama, and proper RAG workflow building** — roughly **70 dedicated AI/LLM nodes** in total.[4] If you're building sophisticated multi-agent systems, doing retrieval over your own documents, or keeping models on your own hardware for privacy, n8n is the more capable tool today. Make's answer is different in kind. Its **next-generation AI Agents** live right inside the scenario builder, interpret input and choose tools within your workflow, take context from **uploaded files with no RAG pipeline to build**, and are shareable across a team.[3] The honest caveats: they're still in **beta**, they **can't operate outside a Make scenario**, and they **don't do native RAG.** So the trade is real and clean: **n8n is deeper; Make is faster to ship** an AI-enhanced workflow that a non-engineer can actually maintain. Which matters more is a question about your team, not the tools — and it's the whole subject of our [Make AI Playbook](/articles/make-ai-playbook). ## Ease vs power Both are powerful; the difference is the on-ramp. Make leans into **no-code polish** — a visual canvas where each module *describes what it does*, so you can infer your way forward. n8n takes a more **technical, node-based** approach ("a flowchart on steroids") and its nodes often lack in-app help text, leaving you to infer meaning from names alone.[1] The upside of that density is depth: n8n supports **JavaScript custom nodes, dynamic API calls and source-level extension**, which is why teams building automation *into their own product* often prefer it.[1] Rule of thumb by job: if a non-developer will maintain the automations, Make; if an engineer owns them, n8n's ceiling is higher. ## Head-to-head, at a glance | Dimension | Make | n8n | |---|---|---| | **Hosting** | Cloud only | Cloud **or** self-hosted (open source) | | **Billing** | Per operation (module) | Per execution (whole run) | | **Cloud entry price** | $9/mo · 10,000 ops | $24/mo · 2,500 executions | | **Self-host cost** | — | Free software · ~$5–10/mo server + upkeep | | **Cost for complex flows** | Climbs with steps | 10–20× cheaper (steps are free) | | **Native integrations** | ~1,800+ out of the box | ~400+ native · 500+ community nodes | | **AI depth** | Agents in-canvas (beta, no native RAG) | Deeper — LangChain, RAG, local models, ~70 AI nodes | | **Ease of use** | Easier — guided, no-code | Steeper — node-based, technical | | **Extensibility** | HTTP module + apps | JS custom nodes, source-level | | **Best fit** | No-code teams, breadth, cloud, simple-to-mid flows | Developers, self-hosting, complex or deep-AI workflows | ## When to choose n8n Choose n8n when **control, complexity or AI depth dominates.** If you must self-host for data sovereignty, if you have a developer who'll live in it, if your workflows are genuinely many-step and high-volume (where per-execution billing saves 10–20×), or if your AI needs real RAG and multi-agent orchestration, n8n is the better and often far cheaper tool. It's also the natural pick if you're embedding automation into a product you ship. ## When to choose Make Choose Make when **ease, breadth and cloud-first simplicity matter more than ownership.** If you don't want to run servers, if non-technical teammates will build and maintain automations, if you value the widest out-of-the-box app coverage, or if your workflows are simple-to-moderate at modest volume (where $9 Core beats n8n's $24 entry), Make is the smoother, cheaper start — with a no-code experience and a ship-fast AI layer n8n doesn't match. The orientation still rewards a technical hand, but the guided canvas and deep template library mean a determined non-technical builder can get real work done. > ⚠ **Self-hosting isn't free-free:** n8n's software is free, but a production instance wants Postgres, Redis and queue workers behind it — plus updates, security and uptime you own. Budget the maintenance time (or a managed host) before you bank the savings. ## Ecosystem, templates and who actually picks which The tools' communities reflect their audiences, and that matters more than it looks. Make leans on a large **template library and no-code community** — you'll usually find a pre-built scenario close to what you want and adapt it, which shortens the learning curve for non-developers. n8n's strength is a **developer ecosystem**: it's one of the fastest-growing open-source automation projects, with **45,000+ GitHub stars and a 40,000+ member forum**,[4] contributing nodes, sharing self-hosting recipes, and letting you read or extend the source when a connector doesn't do quite what you need. One optimizes for "someone already built this"; the other for "I can build or fix anything." (Worth knowing on support: n8n's dedicated technical help is reserved for higher tiers, with forum-first support otherwise and email mainly on European business hours.)[4] In practice the split falls along predictable lines. **Marketing, ops and agency teams** — people who want outcomes, not infrastructure — gravitate to Make: cloud-hosted, guided, template-rich, broad connectors. **Engineering-led teams, product companies embedding automation, and privacy-sensitive or regulated orgs** gravitate to n8n: self-hostable, source-extensible, cheap at complexity, deep on AI. If you sit in the middle, the tie-breaker is usually who will *own and maintain* the automations day to day — a non-technical owner tips it to Make, an engineer tips it to n8n. ## Switching and migration As with any two automation platforms, moving between them is a **rebuild, not an import** — but the node/module mental model carries over cleanly, so it's mechanical. If you're leaving Make for n8n, the usual driver is cost on complex workflows, deeper AI, or a self-hosting mandate, so rebuild your heaviest, most step-dense scenario first — that's where n8n's execution model pays off immediately. Going the other way (n8n → Make), you're usually trading some cost for no-code ease and broader connectors, so start with the workflow your non-technical team most needs to own. Either way, keep both running in parallel until each rebuilt workflow is validated with a real-data test. ## The verdict Neither wins outright — they're built for different owners. n8n is the better tool for developers, self-hosters, and anyone running complex or deeply-AI workflows at volume, and both its per-execution pricing and its AI-native depth are genuine structural advantages there. Make is the better tool for no-code teams who want a polished cloud experience, the widest app coverage, and a cheap, fast start — and it's the quicker route to an AI workflow a non-engineer can keep running. Our own scoring rates Make highly for exactly that audience (the full [Make review](/articles/make-review) has the details), while acknowledging plainly that a technical team optimizing complex-automation cost or deep AI will often be right to pick n8n. Decide on the three questions up top — self-host or not, simple or complex, shallow or deep AI — and the answer is usually obvious. ## FAQ **Make vs n8n — what's the core difference?** Make is a polished, fully hosted visual automation platform billed by operations; n8n is open-source and can be self-hosted, giving you data control and flat infrastructure cost, with a more developer-oriented, code-friendly feel. Make optimizes for convenience and ease; n8n for control and ownership. **Is n8n cheaper than Make?** It can be, especially at high volume — self-hosted n8n has no per-operation fee, so you pay for your server rather than per module run, which scales cheaply for heavy workloads. But you take on hosting and maintenance. n8n also offers a paid cloud tier; against that, the cost comparison narrows and depends on volume. **Can I self-host Make like I can with n8n?** No — Make is cloud-only, with no self-hosted option. If self-hosting for data control, compliance, or cost is a requirement, that alone points you to n8n (or another self-hostable tool), because it's something Make structurally doesn't offer. **Which is easier for non-technical users, Make or n8n?** Make is generally friendlier for non-developers — its hosted, visual experience is more polished and requires no setup. n8n is capable visually too but leans more technical, rewards comfort with code and APIs, and (if self-hosted) requires you to run the infrastructure. Non-technical teams usually find Make the smoother path. **Should a developer choose Make or n8n?** Developers who value control, self-hosting, code nodes, and flat cost often prefer n8n. Those who'd rather not manage infrastructure and want a polished hosted tool with strong app coverage may still pick Make. If data ownership or heavy-volume economics matter, lean n8n; if convenience wins, Make. ## Sources 1. n8n vs Make comparison — Softailed — https://softailed.com/blog/n8n-vs-make 2. n8n pricing 2026 — cloud vs self-hosted — https://connectsafely.ai/articles/n8n-cloud-pricing-guide 3. n8n vs Make (integrations, AI agents) — Betterclaw — https://www.betterclaw.io/blog/n8n-vs-make-ai-agents 4. n8n 2026 updates & AI nodes — Nodesify — https://nodesify.com/blog/n8n-workflow-automation-guide-2026 5. Make — official pricing — https://www.make.com/en/pricing ## References [1] n8n vs Make comparison analysis (Softailed) — https://softailed.com/blog/n8n-vs-make (2026-07) [2] n8n pricing 2026 (cloud vs self-hosted) — https://connectsafely.ai/articles/n8n-cloud-pricing-guide (2026-07) [3] n8n vs Make — integrations, self-hosting overhead & Make AI Agents (beta) — Betterclaw — https://www.betterclaw.io/blog/n8n-vs-make-ai-agents (2026-07) [4] n8n AI-native depth (LangChain, RAG, ~70 AI nodes) & community stats — Nodesify — https://nodesify.com/blog/n8n-workflow-automation-guide-2026 (2026-07)