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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.

Apify logo
Apify
✓ verifiedFreemium

Full-stack platform for web scraping, data extraction, and automation; category leader.

4.4M visits/mo2.0K saves
Super Annotate logo
Super Annotate
✓ verifiedPaid

Enterprise data-annotation and evaluation platform pairing a labeling tool with a managed expert annotator workforce.

406K visits/mo
Angular.dev logo
Angular.dev
✓ verifiedFree

Google's open-source TypeScript framework for building scalable web apps, featuring signals, reactivity and first-party tooling.

1.1M visits/mo
Google Opal logo
Google Opal
✓ verifiedFree

Google Labs experiment for building and sharing AI mini-apps from natural-language prompts, no coding required.

2.1M visits/mo
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

No public pricing

No public pricing

Core features
  • Web scraping
  • Data extraction
  • Browser automation
  • AI agents
  • Anti-blocking
  • Proxy rotation
  • Open-source tools (Crawlee)
  • Ready-made tools and code templates
  • Customizable multimodal annotation editors for image, video, text and audio
  • Support for RLHF preference data, SFT datasets, RAG and agent evaluation workflows
  • Managed expert annotator workforce option
  • Data curation, exploration and analytics tools
  • Team and project management with SSO on higher tiers
  • Integrations with AWS, GCP, Databricks, Snowflake and others
  • Signals-based fine-grained reactivity
  • Built-in control flow and deferrable views
  • Server-side rendering and hydration
  • First-party routing, forms and dependency injection
  • AI-forward tooling and MCP resources
  • In-browser tutorials and playground
  • Build AI mini-apps from natural-language prompts
  • Visual editor for prompt/tool workflows
  • Share created apps with others
  • No-code AI app prototyping
Use cases
  • Data for generative AI
  • Lead generation
  • Market research
  • Sentiment analysis
  • Building large-scale labeled datasets to train computer vision or NLP models
  • Running human evaluation and RLHF pipelines for LLM fine-tuning
  • Auditing and scoring AI agent decisions with human review
  • Building scalable single-page apps
  • Enterprise web application development
  • Performance-critical front ends
  • Learning modern web development
  • Prototyping an AI workflow quickly
  • Sharing a custom AI mini-app
  • Automating a task with chained prompts
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