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Pricing
No public pricing
No public pricing
Basic: Free
Cloud - Android: $250 / device / mo
Cloud - iOS: $250 / device / mo
Cloud - Web: $125 / browser / mo
Enterprise: Custom
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
- —
- ✦Fast tensor operations
- ✦Differentiable tensors for gradient-based optimization
- ✦Network connectivity
- ✦Integration with Bun and Flashlight
- ✦Support for GPU computation with CUDA (Linux) and CPU computation (macOS)
- ✦All-in-one IDE built specifically for testers
- ✦Visual testing to find elements and build tests visually
- ✦Cross-platform testing for iOS, Android, and Web apps (including various frameworks like React Native, Flutter, SwiftUI, etc.)
- ✦AI-Assisted testing with MaestroGPT for command generation and support
- ✦Element inspector for easy identification of UI elements
- ✦Action recording to automatically generate test commands
- ✦Integration into CI/CD pipelines for shift-left testing
- ✦Parallel test execution on enterprise-grade cloud infrastructure
- ✦Test reporting and deep analysis of flows
- ✦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
- —
- →Creating and manipulating datasets
- →Training small machine learning models
- →Implementing advanced training and inference logic
- →Building applications that require tensor computations
- →Automating end-to-end UI tests for mobile and web applications.
- →Creating and running tests in minutes without extensive setup.
- →Scaling test execution with cloud-based parallel runs.
- →Enabling non-technical team members to write and maintain tests.
- →Integrating automated tests into continuous integration workflows (e.g., pre-release, nightly, pull request checks).
- →Ensuring quality control and catching issues early in the development lifecycle.
- →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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