One is a self-hostable canvas with a large community catalogue. The other is the biggest hosted integration library there is, priced per task. Here is which fits which team.
Both are good products. The decision usually comes down to who maintains the automations and how much volume runs through them.
The first two columns answer the question in the title. The third is the same job with the automation written as code.
| Feature | n8n | Zapier | Aisle |
|---|---|---|---|
| What you build | A node graph | A trigger and ordered steps | A Python program |
| Hosting | Self-host or n8n Cloud | Hosted only | Managed only |
| Billable unit | The workflow execution | The task, and every step counts | The run, plus your model calls |
| Adding a step | Costs nothing | Costs more per run | Costs nothing |
| Branching | Wired on the canvas | Paths, each billed | Language control flow |
| Change history | Workflow JSON you export | Zap version history | A revision on every save, with a diff |
| Rollback | Re-import an older export | Restore a version | One click |
| Prompts | In the node | In the step | Versioned, called by slug |
| Model output | Not constrained | Not constrained | Constrained by an output schema |
| Retrying a partial run | The workflow runs again | Replay the run | Checkpoints skip finished items |
| Tests | Not included | Not included | A tests.py in the same revision |
| Non-developer run surface | Forms | Interfaces and Forms | Typed inputs render a form |
| Integrations | Several hundred nodes, plus community | Thousands of apps | 45+ integrations, plus raw HTTP |
| Self-hosting | Yes | No | No. The runtime is proprietary. |
n8n gives you control and hands you the server. Zapier removes the server and prices the automation per step. If your team writes Python, neither trade is necessary.
A Task is a deterministic AI automation you build in Python, hosted and run on secure cloud infrastructure. Your code owns the orchestration: what runs, in what order, and when. The model is called at the steps that need judgment.
since = aisle.run.last_run_at.date().isoformat() tickets = aisle.integrations.jira.get_tickets_by_time_span( query_type="date_range", start_date=since, ) for ticket in tickets: triage = aisle.ai.run_prompt( slug="triage-ticket", variables={"body": ticket["description"]}, ) aisle.integrations.jira.update_ticket( ticket["key"], field_id="labels", tag_name=triage["team"], )
Usually at volume, and especially self-hosted, because n8n bills per workflow execution while Zapier bills per task and counts every step. A ten-step Zap costs ten tasks per run. Self-hosting trades the bill for server and maintenance time, so the saving depends on what that time is worth.
For a non-technical user, yes. Zapier is built so anyone can connect two apps in a few minutes. n8n assumes more comfort with data shapes and expressions, and with running a server if you self-host. In exchange it expresses complex logic more directly.
For most technical teams, functionally yes, with two caveats. Zapier has a much larger catalogue, so check your specific apps. And someone has to own the n8n instance if you self-host, which is an ongoing cost rather than a one-off setup.
n8n has gone further, with an AI Agent node and a chat trigger. Both share the same two gaps: neither versions prompts, and neither constrains the shape of what a model returns, so malformed output travels downstream until something breaks.
Write the automation as code on a managed runtime. Aisle is one option, where a task is Python and the infrastructure is not yours. Temporal, Inngest and Trigger.dev are alternatives if you want durable execution as a library inside an application you already run.
Competitor details reviewed . Vendors change plans and features without notice, so check theirs before deciding.
Open the editor, write a script against your connected accounts, and put it on a trigger.