Gumloop vs Relevance AI
 Gumloop |  Relevance AI | |
|---|---|---|
| Sentiment | ||
| Score | 32/100 | 42/100 |
| Rank in category | #6 | #4 |
| Rated comments | 19 | 15 |
| Best for | AI-heavy workflows like web scraping, research and structured data extraction; Quick prototypes and internal workflows built by non-developers; Teams that run the deterministic plumbing in n8n or Make and want a dedicated AI step | Non-developers prototyping AI agents that call tools; Teams willing to budget carefully for credit-based usage |
| Skip it if | Large, business-critical workflows that must run unattended; Builders who want direct manual control over every setting; Teams that need Zapier-level breadth of app integrations | High-volume chatbots answering many queries; Beginners who want to understand how their automations work underneath; Tight budgets |
| Starting price | $37/mo | Custom pricing |
| Pricing (vendor-stated) | Pro starts at $37/mo with 20,000 credits a month (7,400 plus 12,600 bonus), unlimited seats and agents; credits are indexed at $0.005 each, and an 8% orchestration fee applies to task cost (16% when you bring your own model API key). Enterprise is custom. 14-day free trial; no free plan listed. | Sales-led pricing; the pricing page lists only a custom Enterprise plan (custom actions and vendor credits, unlimited agents, users and workforces) with no public prices or self-serve plans. |
Independent users treat Gumloop as a fast way to build AI-heavy workflows such as scraping, research and data extraction, and several like its clean interface and built-in AI helper. The trade-off is depth and stability: users report UI bugs on large workflows, runs breaking mid-way, a thinner integration layer than Zapier, and most still reach for n8n when they need control or production reliability. More than a third of the Reddit mentions are promotional, including comments plugging Runable, Doe and other tools, near-identical tool-list replies, and a comparison thread other users called out as astroturfing; this review rests on about 19 independent opinions, many of them brief.
Users like
- Quick to build with for smaller and AI-first jobs: users describe it as fast for prototypes, internal workflows and research automations[1][2][3][4]
- Clean, simple interface and a genuinely helpful AI chatbot for troubleshooting, which heavy Zapier and Make users found easy to pick up[1][2][3]
- Handles LLM steps, scraping and unstructured data more natively than a generic node-based flow, so some users pair it with n8n rather than replace it[1][2][3]
- Lets people who understand the business process build workflows without IT oversight or self-hosting n8n[1][2]
Users complain about
- Buggy and unstable as workflows grow: users report UI blocking and dragging bugs on big workflows, runs breaking mid-way and quirks that make it tricky to use[1][2][3]
- Not trusted for production scale: users keep it for prototypes, say its generated workflows are rough to leave running unattended, and find its integration layer thin next to Zapier[1][2][3]
- Less capable than n8n for complex orchestration: users who tried both say n8n's agent, memory and vector tooling handled what Gumloop choked on[1][2]
- Leans too hard on its own AI: one user could not add an environment variable to a custom node except by asking the assistant, which timed out, and another found its agents added little over a direct ChatGPT prompt[1][2]
Independent discussion is thin: about 15 opinions, several of them one-line recommendations, and most first-hand reports date from 2024 and 2025. Users who built with it say it is easy to pick up for no-code agents, but credits make it expensive, free-plan limits run out mid-build, and it has limits on heavier tasks. A third of the mentions are promotional, mostly replies that name Relevance AI while plugging another product such as SimplAI, Marblism or a competing agent builder.
Users like
- Easy to pick up for building agents without code; users call it one of the easiest builders and used it to prototype quickly[1][2][3][4]
- Capable enough for real data work: one user built a 40,000-record name-matching and vectorization system in four weeks with tutorials, after paid developers failed[1]
Users complain about
- Credits get expensive: every action costs credits, users ran out quickly or hit free-plan limits before finishing a build, and one team found it too expensive for what it needed[1][2][3]
- Limited for heavier work: response times slow on more intense tasks, it is a poor fit as a chatbot, and users note limits on what it can do[1][2]
- Abstracts too much for learning: an experienced builder advises beginners to avoid it because you end up not knowing why things work or break[1]