Gumloop vs Make: Which AI Tool is Better?

The Fundamental Trade-off: AI-Native Simplicity vs. Integration Depth

Gumloop and Make are both no-code automation platforms, but they were built with different assumptions about what the hardest part of automation is. Make was designed around the problem of connecting apps - its 3,000+ integrations cover virtually every SaaS product a business might use. Gumloop is now an AI agent platform: you build agents that connect to tools such as Slack, HubSpot, Gmail and Salesforce, with 35+ AI models built in, and its original drag-and-drop workflow builder is labeled legacy.

In practice this means Gumloop reaches a useful result faster when the job involves LLM processing - summarizing documents, categorizing data, generating drafts. AI agents are the center of Gumloop's interface, not one module tucked into a library of 3,000+ equally-weighted options. Make has added AI modules and they work well, but AI is one module among thousands rather than the central thesis of the product.

The integration library gap is not trivial. Make's 3,000+ connections versus Gumloop's smaller catalogue means that as soon as a workflow touches a specific tool - a niche CRM, an industry-specific platform, an internal system - Make is more likely to have a native connector. Gumloop relies more heavily on webhook connectors or a custom MCP server for anything outside its core set. That is workable for technical users but adds friction for non-technical ones.

Who Gets the Most Out of Each Tool

Make suits teams and individuals who:

• Need to connect many different apps - the 3,000+ integration library is the strongest in this category
• Have complex conditional logic in workflows - Make's branching and filtering capabilities are mature
• Are migrating from Zapier and want more power at lower cost - Make handles multi-step, high-complexity workflows Zapier cannot
• Need a free plan or a low entry price - Make has a free plan and paid plans from $9/month billed yearly ($10.59 billed monthly)

Gumloop suits people who:

• Are building AI agents where the model's work is the actual value - research, classification and summarization across company tools
• Want to get a working workflow running in under an hour with minimal setup
• Do not have infrastructure to manage and want a fully hosted solution
• Are experimenting with AI automation and want the simplest possible learning curve

Pricing Reality

Only Make has a free tier, and it is functional for small projects and testing; Gumloop offers a 14-day free trial of its $37/month Pro plan. Make's free plan allows 1,000 credits per month. Gumloop Pro includes 20,000 credits a month ($0.005 each) that cover model tokens, compute and paid API calls, plus an 8% orchestration fee. At scale, Make's credit-based pricing can become expensive for high-volume automations. Gumloop's credit consumption for AI-heavy agents adds up similarly. Neither is cheap at high volume - the difference is that Make's pricing scales with credits used by scenario runs while Gumloop's scales with AI usage specifically.