Comparison
Custom AI vs Make (Integromat)
Make is the strongest visual automation platform. Custom AI is the right call when "visual" stops scaling.
The Honest Verdict
Make handles dramatically more complex workflows than Zapier and gives you more bang per dollar at volume. It's the right answer for visual automation up to a meaningful complexity ceiling. Custom AI takes over when reasoning has to happen across steps, when workflows need real memory, or when you've hit the point where maintaining a 60-node Make scenario is harder than maintaining code.
Pick Make when
You have moderately complex workflows (5–50 steps), a small team comfortable building in a visual editor, and your reasoning needs are bounded to per-step prompts.
Pick Custom AI when
Your workflows need persistent state across runs, reasoning that's harder than a single LLM call can solve, or scenarios so complex that maintaining them in a visual canvas has become a tax.
Side by Side
The dimensions that matter.
| Dimension | Make | Custom AI |
|---|---|---|
| Complexity ceiling | High — but visual canvases get unwieldy past 30–50 nodes | Practically unbounded |
| Cost at scale | Operations-based pricing; predictable up to a point | Engineering upfront; flat inference cost at scale |
| AI workflow reasoning | Per-step prompts with limited cross-step context | Architected reasoning with memory, planning, and tool use |
| Debugging complex flows | Run history per scenario; hard for 50+ node flows | Code-level observability, tracing, and replay |
| Version control | Limited; scenario history but no real git workflow | Full git-based version control, CI/CD, environments |
| Custom integrations | HTTP module for anything not pre-built | Native integration with any system, including on-prem |
| Team handoff | Whoever knows the canvas owns the system | Standard engineering practices — easier to hand off long-term |
Real Scenarios
When each is the right call.
Winner: Make
A SaaS company wants to enrich lead data, route by segment, and update the CRM with a 12-step workflow.
Make handles this beautifully. Visual workflow, moderate complexity, no persistent reasoning needed. Build it in Make and move on.
Winner: Custom AI
An insurance brokerage wants an agent that reads every renewal email, checks policy terms across PDFs, drafts a response, and remembers each conversation across weeks of back-and-forth.
Cross-conversation memory + document reasoning + multi-week state. This isn't a scenario you can express well in a visual canvas.
Winner: Custom AI
A property manager has a Make scenario with 70 nodes that's become impossible to debug or modify without breaking something.
At this complexity, code is easier to maintain than canvas. The team's already paying engineering-level cost in canvas maintenance.
Winner: Make
A small content team wants to take Notion posts, generate social variants, and schedule them across channels.
Make solves this with one scenario and an OpenAI module. Don't overengineer creative workflows that fit in 10 nodes.
Common Questions
Questions we hear on this comparison.
Make has AI modules — isn't that enough?
For per-step AI calls, yes. For workflows where the AI needs to reason about what to do across steps (read this, decide, then read this, decide again), Make's architecture forces awkward workarounds. Custom AI handles that natively.
Is custom AI maintainable by non-engineers like Make is?
No, and that's a real tradeoff. Custom AI requires engineering ownership. If your team has no engineering capacity and doesn't want any, Make is the better long-term home.
What's the cost crossover point?
Roughly when monthly Make operations cost approaches the amortized cost of a custom deployment — typically 50k–500k operations/month depending on complexity. Above that, custom AI is usually cheaper.
Want a recommendation for your specific situation?
A free process audit examines your actual workflows and tells you honestly whether Make, custom AI, or both is the right call.