n8n vs Zapier vs Make: which orchestrator for which job.
n8n, Zapier, and Make are workflow orchestrators: tools that connect other tools and run steps in order when something happens. All three can move a row from a form into a CRM. They diverge on what happens when the workflow is a production system with state, retries, code, and an AI model in the middle. The dev agent and bug fixer Paul Rahme built at the marina-software client run on n8n; Aldenebai still uses Zapier where it fits. Here is the honest split.
The comparison
| DIMENSION | N8N | ZAPIER | MAKE |
|---|---|---|---|
| Hosting | Self-hosted or cloud. Self-hosting keeps data and credentials on your infrastructure. | Cloud only. | Cloud only. |
| Code steps | First-class: JavaScript or Python nodes, custom HTTP, branching, loops. | Limited code steps; built for no-code. | Visual, with functions and iterators; code is possible but not the centre. |
| AI steps | Native model nodes plus raw HTTP to any provider, including your own cloud account. | Built-in AI actions; provider choice is narrower. | AI modules available; similar constraints. |
| State and queues | Good fit for orchestration: waits, callbacks, one-at-a-time dispatch, watchdogs. | Best for stateless, trigger-and-act automations. | Handles multi-step scenarios well; long-running orchestration is harder. |
| Cost model | Flat when self-hosted; usage-based on cloud. | Per task; grows with volume. | Per operation; grows with volume. |
| Who runs it | Needs an engineer for setup and upkeep. | Ops teams run it without engineering. | Ops teams, with a steeper learning curve than Zapier. |
| Best for | Production pipelines with AI, code, and state; anything touching sensitive data. | Fast, simple connections between SaaS tools, run by the people who own the process. | Complex visual scenarios with many branches and moderate volume. |
Why Aldenebai orchestrates on n8n
The dev agent that turns Linear tickets into pull requests is a queue with rules: pick one ticket at a time, dispatch it to a GitHub Actions worker, wait for the callback, gate on the build, retry a red build, flag anything stuck for more than an hour. That is orchestration with state, and it lives naturally in n8n, self-hosted on the client's infrastructure so credentials and ticket content never leave it. The pipeline, stage by stage →
The heavy lifting does not happen inside n8n. Coding, testing, and compiling run in GitHub Actions; n8n decides, records, and notifies. Keeping the orchestrator thin is what makes it debuggable.
Where Zapier still wins
When the automation is simple, stateless, and owned by a non-engineer: a form into a CRM, a new deal into a channel, a weekly export into a sheet. Zapier is faster to build, easier to hand over, and nobody has to maintain a server. Paying per task is fine at low volume.
It stops fitting when the workflow needs a loop, a wait for an external callback, or a decision that depends on the last run. At that point the Zap becomes a chain of Zaps, and the chain becomes the thing nobody wants to touch. Make sits between the two: more expressive than Zapier, less at home than n8n when the workflow is really a small program.
What happens when step four fails: retries, state, and idempotency
The difference between a demo and a production workflow is what happens on the bad day. A callback never arrives; an API returns a 500 halfway through; the same webhook fires twice. Zapier and Make retry a failed step and surface the error in a task history, which is enough for stateless hand-offs. For a pipeline that holds state, such as a ticket that is "in progress" until a worker reports back, you need to store that state somewhere, time it out, and make every step safe to run twice. In n8n that is built explicitly: a wait node with a timeout, a state check before each action, and a watchdog workflow that scans for anything stuck. The dev agent at the marina-software client runs exactly that: one ticket in flight, a 30-minute watchdog, and a flag on anything older than an hour.
Which one for AI agent pipelines: model steps, queues, and approval gates
An AI step is a step that can be slow, can fail, and can be wrong. It needs a queue in front of it, a timeout behind it, a confidence check on its output, and an approval gate before anything irreversible. All three tools can call a model. Only n8n makes it comfortable to run the model through your own cloud account with a raw HTTP node, keep the prompt and the output in your own logs, and route low-confidence results to a person with the context attached. That is why the agent pipelines here run on n8n, and why the simple notifications around them often still run on Zapier.
Which tool for which job
The same question the Automation Audit answers for your company, in table form.
| JOB | SHAPE | PICK | WHY |
|---|---|---|---|
| Form or deal into the CRM | Stateless, one direction, owned by ops | Zapier or Make | Fast to build, easy to hand over, nothing to host |
| Ticket to pull request | Queue, callback, build gate, retry | n8n, self-hosted | State, one-at-a-time dispatch, watchdog; code and tickets stay on your infrastructure |
| Enrichment across sources | Many lookups, confidence gate, CRM write | n8n or code | Loops, rate limits, and a precision gate before any write |
| Month-end reconciliation | Scheduled, several systems, human sign-off | n8n or Make | Branching, exceptions routed to a person, an audit trail of what matched |
| Notifications and reminders | Trigger and message | Zapier | Not worth a server |
Self-hosting and data residency: with n8n on your own infrastructure, credentials, ticket content, and model prompts never leave your perimeter. On the cloud versions of any tool, check where task data is stored and for how long before routing anything sensitive through it.
Questions people ask when choosing
Can I start on Zapier and move to n8n later?
Yes, and it is a common path. The workflow logic ports; what changes is where it runs and who maintains it. Move when a Zap needs a loop, a callback, code, or has to handle data you would rather keep on your own infrastructure.
Is self-hosting n8n worth the upkeep?
Yes, for automations that touch tickets, code, customer records, or credentials: data and secrets stay inside your perimeter. For a handful of low-risk SaaS connections, the cloud versions of any of the three are fine.
Which one should carry the AI model calls?
Whichever lets you run the model through your own cloud account, so the provider does not train on your data and you hold the keys. n8n's HTTP node makes that straightforward; check the same for the others before routing sensitive content through a built-in AI action.
Does Aldenebai only build on n8n?
No. The orchestrator follows the job. Production AI pipelines with state run on n8n; simple hand-offs owned by ops teams often stay on Zapier or Make. The Automation Audit picks the tool after mapping the work, not before.
Related pages
Pick the job first. The tool follows.
The free Automation Audit ranks your repetitive workflows by hours saved and risk, then names the right orchestrator for each.