n8n vs Make.com vs Zapier: The 2026 Comparison
Pick n8n if you need self-hosting, code nodes, or cheap data-heavy runs (it bills per workflow execution). Pick Make.com for visual multi-path branching and the most step-by-step Apify integration. Pick Zapier for the largest app catalog (7,000+) and the simplest setup. All three connect to Apify natively.
The three tools bill differently, and that's the decision. Zapier charges per task (one module action = one task). Make.com charges per operation (one module processing one bundle). n8n charges per workflow execution (one run = one execution, regardless of how many steps or items flow through it). For data-heavy Apify work, where one scrape produces hundreds of items you then fan out, n8n's execution model is dramatically cheaper, Make is a clear second, and Zapier becomes untenable at volume.
The billing-model decision
The platforms look similar on a canvas. They don't bill similarly. Understand this before comparing anything else.
| Model | What counts | Impact on scraping |
|---|---|---|
| Zapier: tasks | Every successful action performed by any step, per item | A 500-row Apify dataset run through "filter → Slack → Sheets" ≈ 1,500 tasks |
| Make.com: operations | Every module run against every bundle | Same workflow ≈ 1,500 operations: same arithmetic, lower unit price |
| n8n: executions | One full workflow run, regardless of items or steps | Same workflow = 1 execution, whether it processes 5 or 50,000 rows |
n8n's model was restructured in 2024–2025 to bill on workflow executions rather than per-step. n8n's own pricing page calls this out explicitly: "An execution is a single run of your entire workflow. It doesn't matter how many steps are in the workflow or how much data it processes."
For Apify pipelines that scrape hundreds or thousands of rows per run, this is the biggest single cost lever among the three tools.
Pricing reality check (verified April 2026)
| n8n Cloud Starter | Make.com Core | Zapier Professional | |
|---|---|---|---|
| Entry paid price | €20/mo (annual) | ~$9/mo (annual) | $19.99/mo (annual, 750 tasks) |
| Included usage | 2,500 workflow executions | 10,000 operations | 750 tasks (base tier) |
| Concurrent runs | 5 | Varies by plan | Plan-dependent |
| Self-hosted free | Yes (Community Edition, no execution cap) | No | No |
| Free tier | 14-day trial on Cloud | ~1,000 ops/mo perpetual | 100 tasks/mo perpetual |
The "$249/mo at 10k tasks" ceiling for Zapier that floats around older comparisons is no longer the right number. Zapier's current Professional plan scales by task tier, and 10k tasks lands in the $133–$169/mo range depending on billing cadence and add-ons. Check zapier.com/pricing before budgeting; the slider tiers change.
n8n self-hosted (Community Edition) is still free under the Sustainable Use License for internal business automation. You pay for infra only: a $6–12/mo VPS handles most small-team workloads. Business features (SSO, version control, multi-environment) require a paid license starting at €667/mo.
Full comparison table
| n8n | Make.com | Zapier | |
|---|---|---|---|
| Billing unit | Workflow execution | Operation (module × bundle) | Task (module × item) |
| Self-hosting | Yes (Community Edition, Enterprise license) | No | No |
| AI nodes | Native AI agent node, 400+ AI-capable nodes, code nodes | Native Claude, OpenAI, Mistral, Gemini modules | Growing AI actions; less granular |
| Apify compatibility | Native Apify node | Separate modules: Run Actor, Wait, Get Dataset Items | Native app; fewer discrete steps |
| Code steps | JavaScript and Python code nodes | Inline functions + HTTP module | Code by Zapier (JS/Python, limited) |
| Learning curve | Steepest (JSON data flow, node wiring) | Middle (visual but with routers/iterators/aggregators) | Shallowest (linear Zaps) |
| Integrations | 500+ | 2,000+ apps | 7,000+ apps |
| Best for | Data-heavy flows, self-hosting, AI orchestration | Multi-path branching with routers and filters | Maximum app coverage, simple linear flows |
Which tool is best for Apify workflows?
Apify's execution model is async: you start a run, the Actor works, you poll or await completion, then you read structured rows from a dataset. Tools that expose those three phases as distinct steps integrate cleanly; tools that wrap them into one black-box action hit friction fast.
Make.com is the strongest default for complex Apify pipelines. Its Apify app exposes Run an Actor, Wait for an Actor Run to Finish, and Get Dataset Items as separate modules, letting you insert filters, error handlers, iterators, and routers between phases. This maps one-to-one to how Apify's API actually works.
n8n wins when you need self-hosting, code nodes, or many items per run. The native Apify node covers core operations; execution-based billing makes n8n significantly cheaper than Make when your workflows process hundreds or thousands of rows per run. The tradeoff is a JSON-flow mental model that non-developers find harder than Make's canvas.
Zapier fits simple, event-driven Apify use: form submission → run Actor → notify Slack with the first result. For multi-step Apify lifecycles with wait states, dataset pagination, and per-bundle retries, Make or n8n will feel less like fighting the tool.
n8n: the engineering-team choice
n8n's value is control plus economics. Self-hosted deployments keep data on your network, which matters for regulated industries, EU data residency, and anything involving PII you can't ship to a US cloud. Code nodes (JavaScript and Python) let you transform payloads without leaving the workflow, and the AI Agent node plus 400+ AI-capable integrations make it a strong hub for LLM chains alongside Apify-sourced web data.
The cost model is the headline feature for Apify-heavy workloads. A nightly Apify scrape that produces 5,000 product rows, runs three transformations, and writes to Postgres is one execution on n8n. On Make, the same flow is roughly 15,000+ operations. On Zapier, 15,000+ tasks. At scale, the math is not subtle.
The tradeoff is ramp-up time: expect a week of wiring JSON between nodes before it feels natural. For teams that already think in APIs and repositories, that investment pays back quickly. See the Apify n8n integration guide to wire the native Apify node into a workflow.
Make.com: the visual complexity choice
Make.com (formerly Integromat) is built around a canvas with first-class routers, iterators, aggregators, and filters. Branching logic stays readable as workflows grow, which matters when you have "if product dropped >10%, notify Slack; if new SKU, enrich and push to HubSpot; else, just log" kinds of paths.
The Apify integration is the most step-oriented of the three major no-code platforms, a real, quantifiable advantage when you're orchestrating async Actor runs. If your workflow needs explicit wait states, dataset paging, and error recovery between steps, Make expresses those naturally without workarounds.
Per-operation billing rewards efficient scenario design (use aggregators to batch writes) and punishes naive ones (iterating the same dataset twice doubles your ops). Scenarios that move thousands of rows per run should be cost-modeled before deployment. The Apify Make.com integration guide walks through the Run Actor, Wait, and Get Dataset Items modules.
Zapier: the coverage choice
Zapier's moat is app coverage and time-to-first-automation. 7,000+ apps, minimal jargon, and a Zap runs within minutes of signing up. For one-to-one, event-driven Apify use ("row added to Airtable → run Actor → post to Slack"), it works and it's fast.
Be honest about the limits:
- Apify lifecycle granularity is narrower than Make's. Expect to wrap multi-step async Actor work in an intermediate webhook.
- Task-based billing punishes fan-out. A scrape that produces 500 rows and runs each through three steps is 1,500 tasks, so Zapier's Professional 2K-task tier is eaten in a single daily run.
- Retries and conditional routing exist but are less expressive than Make's routers or n8n's branch logic.
If you're evaluating Zapier purely for Apify pipelines at any meaningful volume, Make or n8n will almost always be a better fit. Zapier earns its slot when the Apify job is a small step inside a larger Zap that spans apps Make and n8n don't yet support as well. The Apify Zapier integration guide shows how to trigger an Actor from a Zap.
Which should you pick?
Pick n8n if you want to self-host (data residency, PII, EU compliance), need JavaScript or Python code nodes, run AI agent chains, or push hundreds to thousands of items per run where per-execution billing slashes cost. Best for engineering teams comfortable with API-shaped data.
Pick Make.com if your workflows branch (routers, iterators, aggregators, filters) and you want the most step-by-step Apify integration with explicit Run Actor, Wait, and Get Dataset Items modules. Best for operators who want visual control without writing code.
Pick Zapier if you need the broadest app catalog (7,000+), the fastest time-to-first-automation, and your Apify job is one simple event-driven step inside a larger linear Zap. Best for non-technical users and one-to-one triggers.
No. Make.com is cloud-only. If you need a self-hosted automation stack, n8n (Community Edition, free under the Sustainable Use License) is the standard alternative.
Self-hosted Community Edition is free with no execution cap (you pay for your VPS). n8n Cloud currently offers a 14-day trial on paid tiers, and there is no permanent free Cloud tier. Starter Cloud is €20/mo with 2,500 workflow executions; Pro is €50/mo.
For simple trigger-and-deliver patterns, yes. For multi-step workflows where you trigger an Actor, wait for completion, and process dataset items with retries and routing between steps, Make.com or n8n fit Apify's async model more naturally.
Make.com has a gentler curve for non-developers thanks to its canvas builder and guided module configuration. n8n expects comfort with JSON-shaped data moving between nodes; it pays back faster if you already work with APIs.
Billing unit. Zapier and Make charge per step-per-item (task or operation). n8n charges per workflow execution: one run is one execution regardless of how many items or steps flow through it. For an Apify scrape that produces 5,000 rows and runs them through five transformations, n8n bills one execution; Make bills roughly 25,000 operations. The difference compounds at scale.
Yes. Apify offers a native n8n node, a Make.com app with separate Run Actor, Wait, and Get Dataset Items modules, and a Zapier app for triggering Actors. Make exposes Apify's async run lifecycle as the most discrete steps, n8n is cheapest for high-volume runs thanks to execution billing, and Zapier is simplest for one-step triggers.
