Most reviews of automation platforms read like feature spreadsheets. Zapier supports 7,000 apps. Make supports 1,500 apps. n8n supports 400 native plus anything with HTTP. The features matter less than this number: at 1,000 monthly automation runs across 20 workflows, Zapier costs roughly 5x what Make costs and roughly 10x what self-hosted n8n costs. The right answer to the comparison question depends almost entirely on whether you ever expect to operate at that volume.
The three platforms are not direct substitutes despite covering similar territory. Each makes a specific tradeoff between time-to-first-automation, ceiling of complexity, and cost at scale. Picking the wrong one is the most common reason automation projects feel like they failed.
Zapier - the time-to-value champion
Zapier wins on every metric that matters in the first ten minutes of using a new tool. The interface is the simplest. The integration library is the largest. The documentation has been refined over more than a decade of customer support feedback. A non-technical marketer can build a working three-step automation in fifteen minutes without ever reading the docs.
The cost story changes once your automation count grows. Zapier's pricing is per-task and the free tier ends at 100 tasks per month. Real businesses hit that ceiling within the first week of adoption. The professional tier starts around $49 per month for 750 tasks. A real automation library running 50 active zaps with each firing 100 times a month consumes 5,000 tasks. That is the team plan or higher, $399 per month and up. At 50,000 monthly tasks, Zapier becomes the line item finance starts asking about.
Make (formerly Integromat) - the middle ground
Make sits between Zapier and n8n on every axis. The pricing is operation-based with a generous tier (10,000 operations for $9 per month at the time of this writing). Complex multi-step workflows that would consume many Zapier tasks consume one Make scenario, dramatically lowering effective cost.
The downside is the UI. Make uses a visual flow diagram that feels powerful in a demo and confusing in production. Building a real workflow involves connecting modules, configuring routers, and reasoning about iterators. The result is more powerful than Zapier's linear step list but takes meaningfully longer to learn. A Make user who already understands the platform can build automations that would be impossible in Zapier without a developer. A Make user on day one will be slower than they would be on Zapier.
n8n - the cost-at-scale answer
n8n is open-source, can be self-hosted, and is the only platform on this list where execution cost can approach zero at scale. The cloud tier exists and is competitively priced ($24 per month for the starter), but the real differentiator is that you can run n8n on a $5 VPS and process unlimited workflows for the cost of the hosting.
The catch is operational ownership. Self-hosted n8n means you maintain the server, manage upgrades, handle backups, and debug execution failures yourself. The platform is reliable, but "reliable" with a self-hosted service still requires someone on staff who knows how to read logs and restart containers. For a startup with a competent ops engineer this is no obstacle. For a marketing team without one, the cost of operational ownership outweighs the savings on the platform fee.
The decision matrix that actually matches reality
The correct platform falls out of three honest questions about your situation.
- Who is building the automation? A non-technical operator with no developer support: Zapier. A technical operator comfortable with debugging: Make or n8n. A developer treating workflow automation as production infrastructure: n8n self-hosted.
- How many automations do you expect to run? Under 10 active zaps with low volume: Zapier. 10-50 active automations with mixed volume: Make. 50+ automations or any single workflow with thousands of monthly executions: n8n.
- What is your data sensitivity? Customer PII or regulated data flowing through workflows: n8n self-hosted lets you keep data inside your own perimeter. Zapier and Make handle compliance well at higher tiers but mean your data passes through their infrastructure.
Where AI integration changes the math
All three platforms shipped LLM nodes in 2024 and they have matured. Zapier has tight ChatGPT and Claude integrations with structured input fields. Make has the broadest set of model providers but the integration UX is more raw. n8n added AI Agent nodes that combine LLMs with tool calls in a way that feels like the future of these platforms.
The interesting shift is that AI calls are usually the most expensive node in a workflow regardless of platform. A workflow with three Zapier steps and one OpenAI call is not paying Zapier for the AI work. It is paying Zapier per task and OpenAI per token. The platform pricing matters less when AI is the dominant cost. This narrows the gap between the three platforms when AI is heavily used.
Pick the cheapest platform you can afford to operate. The savings on the bill mean less than the time you save on debugging when something breaks at 2am.
The migration trap
Teams that pick Zapier first and migrate to Make or n8n later usually underestimate the cost of rebuilding workflows. Zapier's flat step model does not translate cleanly to Make's flow diagram or n8n's node graph. A 50-workflow automation library can take a quarter to migrate, with a window of double-running both systems while you verify reliability.
The implication: pick the platform that fits where you will be in 18 months, not where you are today. If you are a small marketing team with no plans to grow your automation footprint, Zapier is fine and you should not optimize early. If you are scaling and automation is a core operations capability, start on n8n or Make even though the first weeks will be slower.
The default we recommend
For agency clients running 5-20 automations as part of their operations, Make is usually the right starting point. Cheaper than Zapier at any meaningful volume, more capable in complex workflows, and runs on someone else's infrastructure so the client team is not maintaining a server. We help white-label clients design and ship automation libraries on whichever platform fits their operating model, including n8n self-hosted setups when data sensitivity or volume justifies the operational investment. The platform is the easy part. Talk to us if you want help deciding which one matches your actual workflow profile.
