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Side-by-side features, use cases and pricing — because the right pick depends on your job and budget, not just the ranking.
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Apify
✓ verifiedFreemium
Full-stack platform for web scraping, data extraction, and automation; category leader.
4.4M visits/mo2.0K saves
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Watsonx.data
✓ verifiedFree trial
IBM's open, hybrid data lakehouse that connects, governs and optimizes enterprise data to make it AI-ready across clouds and on-premises.
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Qoder
✓ verifiedFreemium
Agentic AI platform with a coding desktop app, CLI, and cloud agents for autonomous software development and office work.
2.7M visits/mo32K saves
Pricing
No public pricing
Free: $0/mo ($5 included usage)
Starter: $29/mo ($26/mo billed annually)
Scale: $199/mo ($179/mo billed annually)
Business: $999/mo ($899/mo billed annually)
Free trial available
No public pricing
Free trial available
No public pricing
Free trial available
Free Plan: $0 one-time
Hobby: $16/month
Standard: $83/month
Growth: $333/month
Auto Recharge Credits: $11/mo for 1000 credits
Credit Pack: $9/mo for 1000 credits
Enterprise Plan: Contact for Pricing
Core features
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- ✦Web scraping
- ✦Data extraction
- ✦Browser automation
- ✦AI agents
- ✦Anti-blocking
- ✦Proxy rotation
- ✦Open-source tools (Crawlee)
- ✦Ready-made tools and code templates
- ✦Open hybrid data lakehouse
- ✦Connects data across clouds and on-prem
- ✦Governance, lineage and access controls
- ✦Business-context enrichment
- ✦AI-ready data for analytics and models
- ✦Multi-agent collaboration for end-to-end tasks
- ✦Persistent memory and custom rules
- ✦Extensible skills and plugins
- ✦Rich context across code, images, and directories
- ✦Automatic codebase documentation generation
- ✦Terminal-native CLI and JetBrains IDE plugin
- ✦Cloud-hosted agents for enterprise use
- ✦Web scraping
- ✦Web crawling
- ✦Data extraction in Markdown, JSON, and screenshot formats
- ✦Dynamic content handling
- ✦Rotating proxies
- ✦Rate limits management
- ✦Open-source availability
- ✦Media Parsing
Use cases
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- →Data for generative AI
- →Lead generation
- →Market research
- →Sentiment analysis
- →Unifying fragmented enterprise data
- →Governing data for AI workloads
- →Moving AI pilots to production
- →Powering analytics with trusted data
- →Autonomous feature development in large codebases
- →Terminal-based AI pair programming
- →Cross-department task automation for legal, finance, HR
- →Onboarding developers to unfamiliar codebases
- →Powering AI assistants with real-time web content
- →Enhancing sales data with web information
- →Adding scraping capabilities to code editors
- →Enabling customers to build AI apps with web data
- →Extracting comprehensive information for in-depth research
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