Custom AI Automation vs Zapier & Make
When does the no-code SaaS stop being the right answer?
Zapier and Make are excellent. We recommend them often. But for some workflows the monthly task fees, the cognitive overhead of stitching together 14 nodes, or the lack of real AI/data handling become the bigger cost. Here's an honest take from someone with no incentive to push either side.
Side-by-side
| Dimension | Zapier / Make | Custom Python / AI |
|---|---|---|
| Time to first run | Hours | Days–weeks |
| Cost at low volume (<5k runs/mo) | £20–£100/mo | Build cost £1.5k–£8k, then ~£20–£200/mo |
| Cost at high volume (>50k runs/mo) | £300–£2,000+/mo | ~£50–£200/mo |
| AI work (LLM, OCR, embeddings) | Limited, expensive | Native, cost-controlled |
| Data transformation logic | Light (filters, formulas) | Anything you can code |
| Versioning & testing | None / very limited | Full Git + CI |
| Audit trail & compliance | Built-in (basic) | Built to spec |
| Vendor lock-in | Workflow lives in SaaS | You own the code |
| Best when | ≤5 steps, standard apps, low volume | AI involved, high volume, custom logic, audit-critical |
Quick verdict
Use Zapier/Make for fast wins where your workflow fits one of their templates. Move to custom Python the moment any of the following are true: monthly run cost beats £200, you need real AI, you need version control or testing, or you need data transformations beyond filters and lookups.
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