Both tools chosen. Compare is enabled.
Every pairing here opens a written comparison. Don't see your pair? Pin both tools in the catalogue to compare specs side by side.
Compare
Gumloop vs Make
A plain-English comparison to help you choose between them.
Two visual canvases with different centres: Gumloop puts AI operations at the heart of the flow, Make puts app-to-app integration there. Pick Gumloop when the workflow's core steps are AI, scraping, document processing and model calls chained over whole datasets in batches, work too AI-centric for classic trigger-action platforms. Pick Make when the work is connecting hundreds of apps with real logic, branching, iteration and error paths drawn as scenarios, priced by credits so sophisticated volume stays affordable, with AI agents available as reusable components where a flow needs judgement.
Side by side
- Summary
Gumloop is a visual canvas for AI-heavy automation: drag nodes together and batch-process documents, scrape and transform data, and chain AI steps into workflows without code.
- Best for
- Batch document and data processing with AI steps
- Visual workflow building without code
- Scraping and transforming data in one canvas
- Cost
- Freemium (Free tier + paid plans)
- Ease
- Openness
- Hosted service
- Data
- Workflows touch whatever systems you connect; credential scoping deserves the same care as any automation platform.
- Summary
Make is visual automation with engineering sensibilities: scenarios built on a canvas where branching, loops, error handling and data transformation are first-class, connecting thousands of apps at credit-based pricing that undercuts the per-task platforms at volume.
- Best for
- Branching, loops and error handling built visually
- High-volume automation at credit-based pricing
- Reusable AI agents inside scenarios, reasoning visible
- Cost
- Freemium (Free tier + paid plans)
- Ease
- Openness
- Hosted service
- Data
- Per Make's Help Center, "effective august 27th, 2025, we're replacing operations with credits as our billing unit," with existing operations converting 1:1. Standard modules stay at 1 credit, but native AI modules consume credits variably: per one 2026 review, "A workflow with AI Agents can consume 43-50 credits per execution (Small model), versus the few credits of a classic workflow." Extra credits cost 25% more than in-plan (Help Center, updated 6 Nov 2025), for both manual and auto-purchase. The sticker price is not the bill; consider calling AI APIs directly via HTTP for cost control.
By area
Where each one pulls ahead, area by area.
| Area | Pick Gumloop when | Pick Make when |
|---|---|---|
| Automation & agents | Gumloop puts AI operations at the centre of the canvas, chaining scraping, document processing and model calls into batch workflows where AI steps are first-class nodes | the work is classic app-to-app integration, with branching, iteration and error handling drawn across hundreds of connected apps |
Common questions
How much do the canvases actually overlap?
Visually a lot, functionally less than it seems. Both draw workflows, but Gumloop's batch runs cover a whole dataset in one execution, with AI steps as first-class nodes, while Make's scenarios excel at event-driven integration, one record moving through many apps with the connector breadth of an incumbent. The shape of your input, a folder or an event stream, usually decides.
Which wins on connector breadth?
Make, and Gumloop concedes it directly: classic application-to-application integration breadth favours the incumbents, and a job that is mostly plumbing belongs elsewhere. Gumloop's counterweight is the AI-processing depth, extraction, transformation and model calls over documents at volume, the gap between no-code simplicity and writing the pipeline in Python.
How should credits be budgeted on each?
Both meter in credits, differently stressed. Gumloop's price the AI work itself, so heavy recurring batches need their economics modelled before scaling, and some pipelines eventually graduate to code. Make's credits price the volume flowing through scenarios, cheaper than task-priced rivals at scale but still compounding at the extreme, so data-heavy iteration deserves the arithmetic first.
Related comparisons
Read the full guides
Where to start
Not sure what to adopt first?
Five quick questions about your job, task and constraints. We'll suggest your top three tools, plus the one to try first.
Tool facts last checked July 2026