Make vs. an engineered AI automation system.
Make.com is the workflow automation platform we recommend most often when the work is "connect tools and move data with logic." It is more flexible than Zapier, cheaper at volume, and the visual builder is excellent. For AI-driven systems - where a language model is making decisions, retrieving context, drafting outputs - Make is the wrong shape. Not because it is bad, because workflow automation and AI engineering are different jobs.
TL;DR - In one paragraph
Make.com (formerly Integromat) is a strong visual workflow automation platform - better than Zapier for complex chains, cheaper at volume, with more flexible logic. It is still a workflow tool, not an AI engineering platform. Super In Tech is the right choice when AI is doing real work; Make is the right choice for visual workflow chains where AI is incidental.
Side-by-side breakdown
| Feature | Make (DIY) | Super In Tech |
|---|---|---|
| Pricing model | ✓Per operation; $9-$29/mo for typical SMB usage | ✓Fixed-price build + monthly retainer |
| Visual editor | ✓Best-in-class - explicit data flow visualization | ✓We use Make where it fits; custom code where it does not |
| Complex logic | ✓Excellent - branches, iterators, error handlers | ✓Built into architecture; explicit tests for every branch |
| AI / LLM integration | ✗Modules for OpenAI, Claude - no eval, no retrieval, no monitoring | ✓Multi-model routing, RAG, eval suites, production AI engineering |
| Cost at 100k operations/mo | ✓$29-$99/month (excellent) | ✓Build + retainer (high) - only worth it for AI-heavy workloads |
| Time to production (workflow only) | ✓Hours to days | ✓Weeks (overkill for pure workflows) |
| Time to production (AI agent) | ✗Months to never (you build evals + monitoring yourself) | ✓4-8 weeks fixed |
| Best for | ✓Workflow automation, data movement, integration glue | ✓AI agents, AI-augmented systems, mission-critical AI workflows |
The full picture
+Make (DIY), Where it works
- Best-in-class visual workflow editor - explicit data flow, easy to debug
- Better pricing than Zapier at any meaningful volume
- Excellent for complex multi-step chains with branching and error handling
−Make (DIY), Where it falls short
- AI integration is module-level - no evaluation, no retrieval-augmented generation, no monitoring
- Workflow tool, not an AI engineering platform - stretches thin when AI is the workhorse
- Operations-based pricing can sting on AI workflows that retry or chain heavy LLM calls
★Why businesses choose Super In Tech
- 1Production AI engineering - multi-model routing, RAG, eval suites, monitoring - included
- 2For AI-heavy workflows we may use Make for parts of the system, but the core lives in code
- 3Predictable retainer cost regardless of volume; no operations-based price spike
- 4Mission-critical AI workflows get the engineering rigor that workflow tools were not designed for
- 5Hand-over of everything we build - workflows, code, documentation - no lock-in
Our honest take
Use Make for workflow automation, data movement, and integration glue - it is excellent at those. Use Super In Tech when AI is the actual work. Many of our clients run Make alongside our systems: Make handles the deterministic plumbing, our code handles the AI.
What you actually pay
Make: $9-$29/mo Core/Pro tiers, scaling to higher tiers based on operations. Super In Tech: USD 6,000-USD 35,000 fixed-price build + USD 1,500-USD 5,000/month operate retainer. For pure workflow needs, Make wins on cost. For AI engineering, the comparison is not cost - it is "do you have AI engineers in-house?"
Frequently asked
For most use cases past beginner-level, yes - better pricing at volume and more flexible logic. For first-time-no-code users, Zapier has the gentler learning curve. We use Make more often than Zapier internally.
Use Make when the workflow is "move data between tools with deterministic logic." Hire us when the workflow involves a language model making decisions, retrieving context, or drafting outputs that need eval and monitoring. The two are complementary, not substitutes.
Yes - for the right shapes. We have shipped Make-based workflow automation for clients where the deterministic plumbing was the whole job. We hand over the scenarios + a runbook on completion.
Make has AI modules (OpenAI, Claude, etc.) that let you call models inside scenarios. That is different from "AI agent." An agent plans, decides which tool to call, retrieves data, evaluates its own output, and operates with optional human review. Building that pattern in Make alone is hard. Building it in code is the right tool for the job.
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