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Activepieces vs Make

ActivepiecesMake

Bottom line: Activepieces for developers; Make for operations and marketing teams automating multi-step, cross-app processes.

ActivepiecesActivepieces

Open-source, AI-native automation platform you can self-host or run in the cloud

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MakeMake

Make is a visual workflow automation platform that connects over 3,000 apps and services, enabling users to build automated workflows without extensive coding

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Votes00
PricingFreemiumFreemium
CategoryAutomationAutomation
Tags
automationopen-sourceself-hostedai-agentsno-code
automate-workflows
Best for
  • developers
  • technical teams
  • self-hosters
  • 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
  • Open-source MIT-licensed core
  • Self-hostable free with no run caps
  • Native AI agent and text steps
  • Bring your own AI key to cut costs
  • Visual flow builder with code steps
  • 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
  • Self-hosting requires technical setup and upkeep
  • Cloud AI credits need monitoring
  • Connector library may miss some niche apps
  • No native mobile app
  • Advanced use has a learning curve
  • 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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