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Lutra vs Make

LutraMake

Bottom line: Lutra for data analysts; Make for operations and marketing teams automating multi-step, cross-app processes.

LutraLutra

Turn plain-English instructions into automated data workflows

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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
workflow-automationdata-extractionai-agentsspreadsheetsenrichment
automate-workflows
Best for
  • Data analysts
  • Operations teams
  • Researchers
  • 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
  • Natural-language workflow creation
  • Code-backed, inspectable workflows
  • Strong at data extraction and enrichment
  • Integrates with common productivity suites
  • Reusable and schedulable workflows
  • 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
  • Custom pricing above entry tier
  • Fewer app integrations than iPaaS tools
  • No mobile app
  • Learning curve for complex logic
  • Less suited to broad app orchestration
  • 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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