Zapier, Make (formerly Integromat), and n8n are all described as "automation tools" — but they sit in meaningfully different places on the spectrum from simple to powerful, and from managed to self-hosted.
Choosing the wrong one doesn't just mean picking up the bill for features you don't use. It means building automations that hit a wall at exactly the wrong moment, or paying enterprise prices for something you could run cheaper with more control.
The 30-Second Summary
- Zapier: Best for simple, fast integrations between SaaS tools. Polished UX, large app library, expensive at scale.
- Make: Better for complex multi-step flows with visual branching. More powerful than Zapier, better value, steeper learning curve.
- n8n: Best for developers, agencies, or businesses that want full control, custom code, and no per-task pricing. Self-hosted or cloud.
Zapier
Zapier is the most widely used automation platform in the world. Its strength is breadth: 6,000+ integrations, a UI anyone can use on day one, and a large community with pre-built workflows for almost every common scenario.
Where it struggles:
- Cost scales sharply with task volume — enterprise plans run £500–2,000/month
- Logic is linear — branching workflows get messy fast
- Custom code integration is limited compared to competitors
- Error handling is basic — failed runs are hard to debug at scale
Best for: Small teams connecting standard SaaS tools (CRM, email, calendar, Slack) with simple triggers and actions. Works well for fewer than 10,000 tasks per month.
Make (formerly Integromat)
Make uses a visual canvas where you can see the entire flow of a scenario at once, including branches, filters, error routes, and data transformations. It's significantly more powerful than Zapier for complex multi-step automations.
Its pricing is operations-based rather than task-based, which often works out 3–5× cheaper than Zapier for the same workload. The UI requires more initial investment to learn.
Best for: Marketing teams, agencies, and businesses running complex multi-branch flows — data enrichment pipelines, multi-channel customer journeys, document processing with conditional routing.
n8n
n8n is the only one of the three that can be self-hosted — which changes the economics and the capability ceiling entirely. When you self-host, there are no per-task charges. Run millions of operations per month for the cost of a server.
n8n also has a built-in code node (JavaScript or Python), sub-workflows, and native AI agent capabilities — you can build a workflow where an LLM decides which branch to follow based on the content of an email.
Trade-off: n8n has the highest setup cost. Self-hosting requires basic DevOps comfort, and the app library (500+ integrations) is smaller than Zapier's — though it covers every major platform.
Best for: Developers, technical founders, agencies that need custom logic, or any business where automation volume is high enough that per-task pricing becomes a significant cost.
Head-to-Head Comparison
- Ease of use: Zapier > Make > n8n
- Power and flexibility: n8n > Make > Zapier
- Pricing at scale: n8n (self-hosted) > Make > Zapier
- AI and LLM integration: n8n > Make ≈ Zapier
- Integration library size: Zapier > Make > n8n
- Debugging and observability: Make > n8n > Zapier
When to Use a Custom Pipeline Instead
All three tools are excellent for connecting existing software. They struggle when:
- The logic is complex enough that the visual flow becomes unmaintainable
- You need tight control over timing, retries, and error handling
- You're processing large volumes of data with transformations that don't fit a GUI
- The automation needs to interact with a proprietary or internal system with no public API
In these cases, a custom-built pipeline is more reliable and often cheaper to maintain long-term. We build these for clients when off-the-shelf tools hit their limits.
Our take
Start with Make if you're new to automation and need real power without writing code. Move to n8n when task volumes justify self-hosting. Use custom pipelines for anything that needs to handle failure gracefully at scale.
