Picking the wrong automation platform costs you every month in dollars, something that stings twice as hard in LATAM. The short answer for 2026: Zapier is the simplest but the most expensive at scale; Make offers the best price-to-power balance in the cloud; and n8n is the only self-hostable one, the cheapest at volume and the most powerful for automating with AI — in exchange for a more technical learning curve. At 10,000 monthly runs, n8n self-hosted costs USD 10-30 (just the server), Make about USD 30-60, and Zapier USD 130-250. At Deepyze we run all three for clients; this comparison comes from real invoices, not from pricing pages.
n8n vs Make vs Zapier: the comparison table
| Criterion | n8n | Make | Zapier |
|---|---|---|---|
| Billing model | Per run (cloud) or free self-hosted | Per operation (each module counts) | Per task (the most expensive) |
| Cost at 10,000 runs/mo | USD 10-30 (self-hosted) / ~USD 60 (cloud) | USD 30-60 | USD 130-250 |
| Self-hosting | Yes (open source) | No | No |
| Learning curve | Medium-high | Medium | Low |
| Complex logic | Excellent (embedded JS, loops, sub-workflows) | Good | Limited |
| Native AI nodes | The best: agents, memory, vectors | Good modules | Basic |
| Data privacy | Total when self-hosted | Passes through their servers (EU) | Passes through their servers (US) |
| Ready-made integrations | ~500 + any API over HTTP | ~2,000 | ~7,000 |
Indicative pricing as of April 2026; all three adjust their rates often, but the proportions have held for years.
The detail that changes the math: what counts as a "run"
This is where pricing pages confuse you and budgets blow up:
- Zapier charges per task: every step of the flow that executes is a task. A 5-step Zap that runs 2,000 times a month is 10,000 tasks. That's why the "2,000-task" plan runs out in the first week.
- Make charges per operation: similar idea, every module that fires consumes an operation. An 8-module scenario multiplies your real consumption by 8.
- n8n cloud charges per full workflow run: a 40-node workflow counts as 1 run. That alone makes it cheaper for the same plan. And in self-hosted there is no counter: run a million executions, the cost is the same VPS.
The practical rule we use: estimate your real monthly runs, multiply by the number of steps in the flow, and only then look at the plans. Most companies underestimate their volume by 3-5x.
Learning curve: who's going to maintain it
- Zapier: anyone on the marketing or operations team can run it. That's its great strength and the reason it still exists at that price.
- Make: requires understanding data structures (arrays, mappings). A "power user" profile masters it in weeks.
- n8n: for simple flows it's accessible; for serious flows (transformations, error handling, custom APIs) you need technical judgment. If you don't have it on the team, that's exactly the gap an AI automation partner fills — it designs, implements and hands you the flow documented.
Automating with AI: where the real difference is in 2026
If your goal is to connect LLMs to your processes —classifying emails, answering WhatsApp, extracting data from documents—, the comparison tilts hard:
- n8n has the most complete AI ecosystem: agent nodes with tools, conversational memory, vector databases for RAG, and full control over the prompt and the model (including picking the cheap model for each step, key to controlling cost — we cover this in ChatGPT vs Claude for business).
- Make handles the intermediate cases well: decent OpenAI and Claude modules, enough for classification and simple generation.
- Zapier integrates AI but with little fine control: for a serious case you hit the ceiling fast, and every LLM call eats tasks from your plan on top of the tokens.
That's why the backbone of our AI chatbots and automation projects tends to be n8n self-hosted: fixed cost, data in the client's infrastructure and total freedom of models.
Want to know what you'd pay on each platform with your real volume? Book 30 minutes and we'll work out the numbers with your figures, free.
Which to choose based on your situation
- Non-technical team, fewer than 500 runs/month, popular apps → Zapier. The hour you save on setup is worth more than the price difference at that volume.
- Medium volume (1,000-10,000/month), someone with a power-user profile → Make. Good balance, reasonable price, no server to maintain.
- High volume, sensitive data, serious AI, or expected growth → n8n self-hosted. The upfront install investment (Docker, HTTPS, backups, monitoring: USD 500-1,500 if you outsource it) is recovered in 2-4 months against the competition's cloud plans.
- Just starting and unclear on volume → n8n cloud (from USD 24/month): you validate and migrate to self-hosted when the numbers call for it, without rewriting anything.
When none of the three is enough: the jump to custom code
Software-factory honesty from people who live off this: no-code platforms have a ceiling, and it's worth knowing before you hit it.
- Extreme volume: above ~100,000 runs/month or with heavy spikes, even n8n self-hosted demands serious tuning (queues, workers, dedicated database). At some point, a custom service in code is more stable and cheaper to operate.
- The flow IS your product: if the automated process is the core of the business (not a support function), depending on a visual platform limits you in testing, versioning and speed of change. That's where custom software belongs.
- Critical latency: if the user waits for the response on screen (it's not a background process), the platforms add seconds that a custom endpoint won't.
- Logic that no longer fits on a canvas: when the workflow has 200 nodes and no one dares touch it, it stopped being no-code: it's badly versioned code.
The mature pattern we recommend: n8n for integrations and orchestration, custom code for the heavy business logic, connected by API. You keep the speed of no-code where it adds value and the solidity of code where it's needed.
Direct answer to the two key questions
- Which is cheapest at 10,000 runs/month? n8n self-hosted: USD 10-30 of server, end of the math. Make follows (USD 30-60) and Zapier comes last (USD 130-250).
- Which allows self-hosting? Only n8n. If data sovereignty or cost at scale matter to you, the decision is made.
If you want to go deeper on n8n before deciding, we wrote a complete n8n guide. And if you'd rather we solve it with you: at Deepyze we install, design and maintain automations on n8n (and migrate flows from Zapier and Make every month). Tell us about your case and within 24 hours you'll have a fixed-price proposal, from a team in your own time zone.
Frequently asked questions
Which is cheapest at 10,000 runs per month: n8n, Make or Zapier?+
n8n self-hosted, by a wide margin: your only cost is the server, USD 10-30/month regardless of volume. Make runs around USD 30-60/month depending on how many operations each scenario consumes, and Zapier is the most expensive: USD 130-250/month because it charges for every task executed.
Which of the three allows self-hosting?+
Only n8n. Its open source version installs on your own server, with unlimited runs and your data in your own infrastructure. Make and Zapier are cloud-only: your data always passes through their servers and you pay by volume.
Which is easiest to use for someone non-technical?+
Zapier, clearly: you connect two apps in 10 minutes without understanding anything about data. Make requires thinking in structures and mappings. n8n is the most technical of the three, although its visual editor and AI nodes have made the entry curve much gentler.
Which has the best AI integrations in 2026?+
n8n leads: native nodes for OpenAI, Claude and open source models, conversational memory, vectors and agents with tools. Make has solid AI modules. Zapier integrates the main providers but with less fine-grained control over prompts, context and costs.
When does it make sense to drop these tools and move to custom code?+
When cost per operation exceeds what maintaining code would cost, when the flow is the core of the business, or when you need latencies and volumes the platforms can't handle. Typically above 100,000 runs/month or with very complex logic, custom development pays for itself.
Want this working in your company?
At Deepyze we turn manual processes into systems that work on their own: AI automation, web and mobile apps, and custom software. Tell us your case and you will have a concrete proposal within 24 hours.
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