Apify vs n8n
An independent, review-free comparison compiled by the SaaSTracker editorial team. Both products are profiled in full, and neither can pay for placement here.
The short answer
Editorial assessmentApify compared with n8n
These sit at different layers and are frequently used together. n8n is the workflow orchestrator, the thing that decides what runs when and where the output goes; Apify is the extraction engine it calls. n8n's own HTTP nodes can scrape simple pages, but it has no proxy pool, no headless browser fleet, and no library of maintained scrapers, so anything defended will fail there. The durable setup is an n8n workflow that triggers an Apify Actor, waits on the webhook, and routes the dataset into a CRM or warehouse.
Choose Apify if
Growth, RevOps, and data teams that need custom web data at a scale a manual list build cannot reach, and that want ready-made scrapers for the common sources with an escape hatch into custom code when a target is unusual. Also strong for anyone wiring web data into an AI agent through MCP.
Choose n8n if
Technical or semi-technical teams building long, multi-step automations, enrichment waterfalls, AI agents, and internal integrations, who want per-execution economics in the cloud or full data control self-hosted, and who are comfortable reading JSON when something breaks.
Side by side
13 attributes| Attribute | Apify | n8n |
|---|---|---|
| Category | GTM Engineering | GTM Engineering |
| Starting price | Free with $5 of monthly platform credit; paid plans from $29 per month ($26 billed annually) (free plan available) | About $24/mo (Cloud Starter, 2,500 executions; roughly $20 on annual billing) (free plan available) |
| Pricing model | Prepaid platform credit with usage metered against it. Each plan includes a monthly credit allowance equal to its price, consumed by compute units (memory multiplied by run time), proxy bandwidth, storage operations, and data transfer. Rented or paid Store Actors carry their own fees on top, set by the Actor's developer as a monthly rental, a per-event charge, or a per-result charge. Seats are not the pricing axis; usage is. | Two paths. Cloud: subscription tiers metered by workflow executions per month (a run is one execution regardless of step count), with unlimited workflows and steps on every tier. Self-hosted: the Community edition is free under the fair-code Sustainable Use License, with paid Business and Enterprise self-hosted tiers adding collaboration, SSO, environments, and support. Prices are quoted in euros; dollar figures shift slightly with exchange rates. |
| Free plan | $5 of platform credit per month, community support, limited trial access to rented Actors, and access blocked rather than billed once credit is spent | Self-hosted Community edition is free and unmetered on your own infrastructure |
| Free trial | Free plan with no credit card required, plus limited trials on most paid Store Actors | Cloud trial, no card required |
| Best for | Growth, RevOps, and data teams that need custom web data at a scale a manual list build cannot reach, and that want ready-made scrapers for the common sources with an escape hatch into custom code when a target is unusual. Also strong for anyone wiring web data into an AI agent through MCP. | Technical or semi-technical teams building long, multi-step automations, enrichment waterfalls, AI agents, and internal integrations, who want per-execution economics in the cloud or full data control self-hosted, and who are comfortable reading JSON when something breaks. |
| Setup time | Minutes for a first Store Actor run: sign up, pick an Actor, fill the form, export a CSV. A scheduled pipeline with proxy configuration, webhooks, and a downstream destination is a half day. A custom Actor is a normal software project, measured in days, and its length depends on how hard the target defends itself rather than on Apify. | Cloud: minutes to sign up, and a first webhook-to-Slack workflow the same hour. Self-hosted: a Docker one-liner for a test instance; a hardened production deployment with backups and HTTPS is a half-day for someone who has run containers before. |
| Learning curve | Low to run existing Actors, moderate to operate the platform well. The two things that take time are understanding the cost model well enough to predict a bill, and learning which proxy and browser settings a given target requires. Writing Actors requires working JavaScript or Python. | Steeper than task-based tools: expect a few days to internalize items, expressions, and node data mapping, typically via imported templates. Engineers acclimate almost immediately; operators without API experience need the templates and patience. |
| Platforms | Web console, REST API, JavaScript and Python API clients, Apify CLI, Docker-based Actor runtime, Hosted MCP server | Web app (cloud), Self-hosted (Docker, npm), Embedded (commercial license), REST API |
| Compliance | GDPR, SOC 2 Type II, CCPA | SOC 2, GDPR |
| Founded | 2015 | 2019 |
| Headquarters | Prague, Czech Republic | Berlin, Germany |
| Ownership | Independent, venture-backed | Venture-backed, founder-led |
Strengths and limitations
Apify
Strengths
- The Store is the moat: for most common data sources, someone has already built, debugged, and now maintains the scraper, which collapses a two-week project into an afternoon of configuration.
- Real infrastructure underneath, with proxies, headless browsers, storage, scheduling, and retries handled rather than left as an exercise.
- Genuinely usable free tier with $5 of monthly credit, no credit card, and full API and MCP access, so evaluation costs nothing.
- Crawlee is open source and runs anywhere, which meaningfully limits lock-in for teams writing their own crawlers.
Limitations
- Compute-unit billing is hard to predict before you run a job, because cost is memory multiplied by time and both depend on how the target site behaves that day.
- Included platform credit expires monthly with no rollover, so buying a tier for occasional heavy months means paying for unused capacity in the quiet ones.
- Store Actor quality is uneven; some are abandoned, some break silently when a target site changes markup, and the run-success statistics on the Actor page are the only pre-purchase signal.
- Paid Actors add a second billing layer on top of platform credit, and the total cost of a workflow is not visible from any single number on the pricing page.
n8n
Strengths
- Per-execution pricing inverts the category's cost model; long, complex workflows, exactly the kind GTM engineering produces, are where it is cheapest relative to rivals.
- Free self-hosted Community edition provides a permanent exit ramp and full data sovereignty, a structural negotiating position no closed rival offers.
- Code nodes and the HTTP Request node remove the capability ceiling; if an API exists, n8n can drive it without leaving the canvas.
- LangChain-based agent nodes made it one of the fastest paths from 'AI idea' to 'AI in production', with approvals, retries, and logging around the model.
Limitations
- The learning curve is real: items, expressions, and JSON structure confront you in week one, and non-technical users often stall where Zapier would have carried them.
- The integration catalog (400+) is an order of magnitude smaller than Zapier's; long-tail SaaS tools frequently mean building against the raw API via the HTTP node.
- Fair-code is not open source by OSI definition, which complicates some corporate policies and community expectations, and the license debate resurfaces periodically.
- Cloud Starter's 2,500 executions can pinch for high-frequency polling triggers; trigger design (webhooks over polling) becomes a cost skill.
Pricing compared
Apify
Prepaid platform credit with usage metered against it. Each plan includes a monthly credit allowance equal to its price, consumed by compute units (memory multiplied by run time), proxy bandwidth, storage operations, and data transfer. Rented or paid Store Actors carry their own fees on top, set by the Actor's developer as a monthly rental, a per-event charge, or a per-result charge. Seats are not the pricing axis; usage is.
- Free$0
- Starter$29
- Scale$199
- Business$999
Judged against building the same thing yourself, Apify is inexpensive: a residential proxy pool, headless browser fleet, retry logic, and storage would cost more than $29 a month in engineering attention alone, before any infrastructure. Judged against a fixed-price scraping tool, it is harder to compare, because you are buying capacity rather than a defined output, and the honest answer for month one is that you cannot forecast the bill precisely. The mitigation is straightforward: run the job once at a small limit, read the usage breakdown, multiply. Teams that do this usually find the real cost of a recurring list build lands in the tens of dollars a month, and teams that skip it are the ones who write reviews about unpredictable pricing.
n8n
Two paths. Cloud: subscription tiers metered by workflow executions per month (a run is one execution regardless of step count), with unlimited workflows and steps on every tier. Self-hosted: the Community edition is free under the fair-code Sustainable Use License, with paid Business and Enterprise self-hosted tiers adding collaboration, SSO, environments, and support. Prices are quoted in euros; dollar figures shift slightly with exchange rates.
- Community (self-hosted)Free
- Starter (Cloud)~€20 to €24
- Pro (Cloud)~€50 to €60
- Business / EnterpriseCustom
For long workflows, nothing in the category touches n8n's economics: the per-execution meter makes a 40-step enrichment waterfall cost the same as a 2-step alert, and the same pipeline on task or operation pricing can cost ten to fifty times more at identical volume. Against that, the true cost is skill: n8n assumes you can read JSON, debug an HTTP call, and occasionally write a line of code. Teams with that skill get Zapier-class capability at a fraction of the spend, plus a free self-hosted exit ramp that caps vendor risk permanently. Teams without it will burn the savings in time.
Editorial verdict on each
Apify
Apify is the most complete web scraping platform a small team can buy without a sales call, and its real asset is the Store: for the sources most go-to-market teams need, someone has already written and is still maintaining the scraper, which is the difference between a two-week engineering project and an afternoon. Underneath it, the proxy pool, browser fleet, storage, and scheduling are the parts people underestimate until they have tried to build them, and Crawlee being open source keeps the lock-in honest. The MCP server has made it one of the more useful data sources to hand an AI agent. The cost of all this is a billing model you have to learn: compute metered by memory times time, credit that expires monthly, and a second layer of fees on paid Actors that no headline price reflects. Budget an hour to measure one representative job before committing, set a spending limit on day one, and check an Actor's success rate before you depend on it. Do those three things and it is very good value; skip them and you will write one of the reviews about unpredictable pricing.
Read the full Apify profilen8n
Momentumn8n is what the automation category looks like when it is priced and licensed in the buyer's favor: pay per run instead of per step, read the source, and leave for your own server whenever you like. For the GTM engineer building long enrichment waterfalls and AI-agent workflows, those three facts compound into the category's best deal, and the LangChain-native agent tooling has made it the default engine of the DIY stack. The honest cost is skill: it asks more of you in week one than Zapier ever will, and its integration catalog still leans on the HTTP node for the long tail. Teams with a technical operator should default here; teams without one should pay the Zapier tax knowingly.
Read the full n8n profileApify profile last reviewed 2026-08-23; n8n last reviewed 2026-08-22. Pricing is compiled from public sources and can change without notice. See our methodology.