Description
Gumloop is an AI-native workflow automation platform, backed by a $50 million Series B from Benchmark, built around a visual node canvas where users connect triggers, apps, and AI nodes into automations called flows. It leans more heavily on AI than general-purpose automation tools, with built-in nodes for LLM calls (GPT-4, Claude, Gemini), web scraping, browser automation, and unstructured document processing, alongside sub-agents that can run steps in parallel. Gumloop bills usage in credits, where every AI-powered node consumes credits based on which model it calls, standard AI calls costing far fewer credits than advanced models like GPT-4 or Claude Opus. Gumloop’s integration library is smaller than Zapier’s or Make’s, reflecting its focus on AI-heavy workflows rather than the broadest possible app coverage. Company customers include Shopify and Instacart, per Gumloop’s own case studies. It suits technical users and content, business operations, or research teams who want deep AI capability built directly into their automation nodes rather than bolted on as an add-on.
Key Features
- Visual node canvas — drag-and-drop builder connecting triggers, apps, and AI nodes into flows.
- Built-in LLM nodes — call GPT-4, Claude, Gemini, or other models directly inside a workflow step.
- Web scraping and browser automation — native nodes for extracting data from live web pages.
- Sub-agents — run multiple workflow steps in parallel rather than strictly sequentially.
- Document processing — extract structured information from unstructured documents.
- Bring-your-own-model — connect a personal OpenAI, Anthropic, or open-source API key, switchable per pipeline step.
- Analytics dashboard — tracks execution history, credit usage, and cost per run in real time.
How It Works
Building a flow means placing nodes on a visual canvas: a trigger to start the automation, followed by app-connection nodes, AI nodes for tasks like summarization or classification, and logic nodes for branching. Each AI-powered node consumes credits when it runs, with the exact cost depending on which model handles the call; a standard AI call might cost a couple of credits, while an advanced model like GPT-4 or Claude Opus can cost 10x or more for the same step, so a flow with several advanced-model calls burns through a credit allowance quickly. Sub-agents let parts of a flow execute in parallel rather than waiting on each step in sequence, useful for flows that fan out into several independent research or scraping tasks. Teams work in shared workspaces where a flow one person builds can be shared and reused by others, and an analytics dashboard tracks exactly how many credits and what cost each run consumed.
Technical Architecture & Overview
- Core Engine: Supports GPT-4, Claude, Gemini, and open-source models, selectable per pipeline step; bring-your-own-API-key option available.
- Deployment: Cloud-based web platform; no self-hosted option documented.
- API Surface: Hosted MCP features on the Pro plan and above; integrates with roughly 200+ apps, a smaller library than Zapier’s or Make’s.
- Known Limits: Free plan is capped at 5,000 credits/month, 1 seat, and limited concurrency; credit consumption varies significantly by which AI model a node calls, making budgeting harder to predict until usage patterns are established.
Pros & Cons
| Pros | Cons |
|---|---|
| Built-in LLM, scraping, and document-processing nodes give deeper AI capability than most general automation tools | Smaller integration library (~200 apps) than Zapier’s 8,000+ or Make’s 3,000+ |
| Sub-agents run steps in parallel, speeding up flows with independent research or scraping tasks | Credit costs vary widely by AI model used, making budgeting harder to predict until you’ve run flows enough to learn consumption patterns |
| Pro plan includes unlimited seats, rather than charging per user | Advanced AI model calls (GPT-4, Claude Opus) can burn credits 10x faster than standard calls |
| Bring-your-own-API-key option gives direct control over model choice and cost per pipeline step | Steeper learning curve than simpler no-code tools, favoring technical users |
| Backed by a $50M Series B from Benchmark, with enterprise customers including Shopify and Instacart | Enterprise pricing is fully custom, with no published starting figure |
Pricing
| Plan | Price |
|---|---|
| Free | $0/month (5,000 credits/month, 1 seat, limited concurrency) |
| Pro | $37/month (20,000+ credits/month, unlimited seats, team analytics, hosted MCP) |
Platform Availability
Web
Best For
Business operations teams | Developers | Content creators | AI-heavy workflow builders
Frequently Asked Questions
Is Gumloop free to use?
Yes. The Free plan includes 5,000 credits per month, 1 seat, and limited concurrency, enough to test the visual builder and small AI-powered flows before upgrading.
How do credits work in Gumloop?
Every AI-powered node consumes credits when it runs, with the amount depending on the model called; standard AI calls cost only a couple of credits, while advanced models like GPT-4 or Claude Opus can cost significantly more per call.
Does Gumloop charge per seat?
No. The Pro plan includes unlimited seats for one flat monthly price, unlike tools that charge per team member added.
How many apps does Gumloop integrate with?
Roughly 200+ apps, a smaller library than Zapier’s 8,000+ or Make’s 3,000+, reflecting Gumloop’s focus on AI-native workflow capability over sheer integration breadth.
How does Gumloop differ from Zapier or Make?
Gumloop builds AI directly into its nodes, with native LLM calls, web scraping, and document processing, while Zapier and Make focus on broader app connectivity with AI as an add-on layer rather than the core building block.
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