The Make alternative, written in Python.
Make draws the automation as a scenario. A Task is a program, so an iterator over a thousand records is a loop rather than a thousand operations.
base = aisle.inputs.get("base_id") with aisle.files.open(aisle.inputs.get("file")) as f: for rows in aisle.parsers.csv_chunks(f, chunk_size=500): for row in rows: enriched = aisle.ai.run_prompt( slug="enrich-company", variables={"row": row}, ) aisle.integrations.airtable.create_record( base, "Companies", fields=enriched, )
Model and provider agnostic.
Anthropic
OpenAI
Gemini
xAI
OpenRouter
Amazon
Perplexity
MoonshotAI
Meta
Qwen
Aisle vs Make
Make is the most expressive of the visual builders and its canvas handles branching better than most. These rows are about the point where the diagram stops helping.
| Feature | Aisle | Make |
|---|---|---|
| What you build | A Python program | A visual scenario |
| Billable unit | The run, plus the model calls your code makes | The operation, per module execution |
| Iterating 1,000 records | One run | Operations that scale with the rows |
| Branching and iteration | Language features | Routers and iterators |
| Change history | A revision on every save, with a line-level diff | Scenario versions |
| Tests | A tests.py rides with the task, in the same revision | Not included |
| Model output | An output schema constrains the model to your JSON | Not constrained |
| Prompts | Versioned in a prompt platform, called by slug | Prompt text sits in the module |
| Retrying a partial run | Checkpoints skip the items that finished | Re-run, with incomplete-execution handling |
| Control flow | Loops, branches and functions in code | Wired on the canvas |
| Shared team credentials | Yes, brokered server-side, shared with chat | Yes |
| Run surface for non-developers | Typed inputs render a form | Scenario inputs |
| Seeing the flow as a picture | Read the code | Yes, and it is good |
| Integrations | 45+ integrations, 390+ operations, plus raw HTTP | Very large app catalogue |
Written in code, not drawn on a canvas.
A scenario is easiest to read at the size it was first drawn. Code grows in lines rather than in area, stays searchable, and reviews as a diff.
An iterator is a loop
Fan out over a thousand rows with a concurrency limit and a rate limit as arguments. It is one run, and the checkpoint means a retry resumes at the failure rather than starting the batch again.
result = aisle.parallel( enrich, rows, concurrency=10, max_per_minute=300, checkpoint="q1-enrich", )
Versioned prompts, called by slug
A prompt inside a module has no history and cannot be reused elsewhere. Aisle prompts are objects called by slug and shared with chat and projects, so editing one updates every task that calls it with no code change and no redeploy.
enriched = aisle.ai.run_prompt( slug="enrich-company", variables={"row": row}, )
Revisions and rollback
Every save writes a revision with a line-level diff and a changelog note, and any revision restores in one click. Version tracking is fast and simple, which is the part a canvas makes hardest.
Moving a scenario across.
Move the scenarios that got expensive or hard to change, and leave the small ones where they are.
- Start with the scenario burning the most operations per month.
- Describe it to the builder and it drafts the task as code your developer edits.
- Connect the accounts once at the org level.
- Run both in parallel, then move the trigger over.
Where Make fits better.
Make does some things Aisle deliberately does not attempt.
- The team maintaining the automations does not write code.
- Seeing the whole flow as a diagram is how your team reasons about it.
- You need an app from the long tail of the catalogue with no usable API.
- Volume is low enough that operation pricing never becomes noticeable.
Questions
Is Aisle cheaper than Make?
It depends on shape. Make bills per operation, so iterating a thousand records costs a thousand operations before any real work happens. A task is a single run regardless of how many records it processes, which is where the difference shows up.
Can I use Make alongside Aisle?
Yes, and most teams should. A task exposes a webhook with a synchronous response, so a Make scenario can call a task for the AI or heavy-processing part and carry on with the rest of the flow.
Does Aisle have a visual builder?
There is a workflow canvas, but a Task is not built on it. The builder drafts a working task from a plain-English description, and what it produces is Python you read and edit rather than a graph.
What is the best Make alternative for developers?
For a canvas you can self-host, n8n or Activepieces. For durable execution inside your own codebase, Temporal, Inngest or Trigger.dev. Aisle fits when you want the automation to be Python but do not want to operate the infrastructure under it.
Competitor details reviewed . Vendors change plans and features without notice, so check theirs before deciding.
More comparisons
Ship your first task.
Open the editor, write a script against your connected accounts, and put it on a trigger. Or describe it, and the builder drafts the task as code you edit.