Side-by-side

Retool vs Make

RetoolMake

Bottom line: Retool for engineering and ops teams building internal tools; Make for operations and marketing teams automating multi-step, cross-app processes.

	Retool Build internal tools and AI apps fast with drag-and-drop plus code Visit	Make Make is a visual workflow automation platform that connects over 3,000 apps and services, enabling users to build automated workflows without extensive coding Visit
Votes	0	0
Pricing	Freemium	Freemium
Category	Automation	Automation
Tags	low-codeinternal-toolsai-agentsdashboardsworkflows	automate-workflows
Best for	Engineering and ops teams building internal tools Startups replacing hand-built admin panels Teams adding AI to existing business data	Operations and marketing teams automating multi-step, cross-app processes No-code and low-code builders who want visual control over complex logic Businesses running high-volume workflows sensitive to per-operation cost
Pros	Very fast to build data-backed internal tools Escape hatch into SQL and JavaScript for real control Retool AI adds practical LLM features and agents on your data Self-hosting available for compliance-sensitive teams Large library of integrations and components	The visual scenario builder makes complex, multi-branch automations far easier to design and audit than linear step-based tools, with data flow and logic represented clearly on a single canvas. A library of 3 000+ pre-built app connectors, backed by generic HTTP, webhook, and custom app modules, means nearly any API can be reached even when a native integration is missing. Make is genuinely AI-native, offering agentic automation, ready-made AI agents, and an MCP server that lets AI assistants trigger real actions across connected apps rather than just generating text. The pricing is competitive for high-volume workflows, with an operations-heavy allowance at entry-level tiers and a no-time-limit free plan for experimentation.
Cons	Steeper learning curve than pure no-code builders Pricing mixes per-seat and usage-based AI/Workflow costs Can get expensive as teams and usage scale Not aimed at public-facing consumer apps AI credits and Workflow runs require monitoring	The visual canvas is powerful but has a real learning curve; newcomers to automation often find its data mapping, iterators, and error handling harder to grasp than simpler competitors. The recently introduced credit-based billing model can make costs harder to predict, since advanced and AI-powered actions may consume more credits than a standard operation. Complex scenarios can become difficult to maintain and debug at scale, and heavy reliance on the platform creates a degree of workflow lock-in. There is no self-hosted or offline option, so teams with strict on-premise or air-gapped requirements will need an alternative.

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