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Gumloop vs Vellum

A side-by-side comparison of two ai agents tools — pricing, integrations, and the trade-offs that matter — so you can pick the right fit fast.

Gumloop compared with Vellum
FeatureGumloopVellum
CategoryAI AgentsAI Agents
PricingFreemium · from Free / $97/moPaid · from Custom pricing
Best forFounders, Marketers, Product ManagersDevelopers, Product Managers
Use casesautomating business workflows, building AI agents, scraping and enrichmentTesting and evaluating prompts systematically, Building production-grade LLM applications, Iterating on AI agent workflows before deployment
IntegrationsGoogle Sheets, Slack, OpenAIOpenAI API, Anthropic API, API
Rating
WebsiteVisit GumloopVisit Vellum

Gumloop

No-code platform for building AI automations.

Pros

  • +No-code visual builder
  • +Good for AI-heavy workflows
  • +Reusable templates
  • +Team collaboration

Cons

  • Pricing climbs with usage
  • Newer ecosystem
  • Fewer integrations than incumbents

Vellum

Development platform for building, testing, and deploying production LLM agents.

Pros

  • +Provides systematic prompt testing and evaluation tools
  • +Helps teams iterate on agent workflows before production deployment
  • +Built specifically for production LLM application development

Cons

  • Most valuable for teams building and iterating on LLM apps regularly
  • Requires some technical setup to integrate into existing pipelines

Gumloop vs Vellum FAQ

Is Gumloop better than Vellum?
Neither is universally better — both are ai agents tools. Gumloop (Freemium, from Free / $97/mo) is a strong fit for automating business workflows, while Vellum (Paid, from Custom pricing) suits Testing and evaluating prompts systematically. Pick by your primary use-case and budget.
What is the main difference between Gumloop and Vellum?
Gumloop focuses on "No-code platform for building AI automations." whereas Vellum focuses on "Development platform for building, testing, and deploying production LLM agents.". Their pricing starts at Free / $97/mo and Custom pricing respectively.

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