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Apify vs Zapier

ApifyZapier

Bottom line: Apify for developers and technical data teams; Zapier for non-technical teams that need to automate across many apps.

ApifyApify

Cloud platform for web scraping and browser automation at scale

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ZapierZapier

Zapier is an automation platform that connects apps and workflows, now including AI orchestration capabilities through Zapier MCP (Model Context Protocol)

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Votes00
PricingFreemiumFreemium
CategoryAutomationAutomation
Tags
web-scrapingbrowser-automationdeveloper-toolsdata-extractionproxies
automate-workflows
Best for
  • Developers and technical data teams
  • High-volume or complex scraping
  • Teams needing managed proxies at scale
  • Non-technical teams that need to automate across many apps
  • Small and medium businesses consolidating SaaS workflows, Marketing, sales, and RevOps teams automating handoffs
  • Teams adding AI orchestration to existing app stacks
Pros
  • Large marketplace of prebuilt Actors
  • Powerful for custom, code-based scrapers
  • Managed proxies and anti-blocking tools
  • Scheduling, storage, and datasets built in
  • Robust API and JS/Python SDKs
  • Connects to an enormous catalog of apps — over 9
  • 000 integrations — making it the most reliable choice when you need to bridge tools that don't otherwise communicate.
  • Genuinely no-code, with a guided trigger-and-action builder that lets non-technical users ship working automations in minutes rather than days.
  • Consolidates multiple workflow building blocks — Zaps, Tables, Forms, Canvas, and AI Agents — into one platform, reducing the need to stitch together separate tools.
  • Zapier MCP and native AI steps turn the platform into a practical execution layer for AI assistants, letting LLMs trigger real actions across thousands of apps.
Cons
  • Usage-based credit + proxy billing is hard to forecast
  • Store Actors add their own separate pricing
  • Steeper learning curve than no-code tools
  • No self-hosted/on-prem option
  • Costs can climb at high volume
  • Pricing is metered by tasks, and costs can climb quickly as workflows grow in volume or complexity, making spend harder to predict for high-throughput use.
  • The shift to model-based pricing for AI steps — where advanced and premium models cost multiples of the base rate — adds another variable that can inflate bills for AI-heavy workflows.
  • Complex branching logic, custom code, and high-performance needs can hit a ceiling, where developer-focused or self-hosted automation tools offer more control.
  • The free tier's task allowance is modest and best suited to experimentation, so most real-world business usage requires a paid plan.

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