Average Ratings 0 Ratings
Average Ratings 0 Ratings
Description
Phoenix serves as a comprehensive open-source observability toolkit tailored for experimentation, evaluation, and troubleshooting purposes. It empowers AI engineers and data scientists to swiftly visualize their datasets, assess performance metrics, identify problems, and export relevant data for enhancements. Developed by Arize AI, the creators of a leading AI observability platform, alongside a dedicated group of core contributors, Phoenix is compatible with OpenTelemetry and OpenInference instrumentation standards. The primary package is known as arize-phoenix, and several auxiliary packages cater to specialized applications. Furthermore, our semantic layer enhances LLM telemetry within OpenTelemetry, facilitating the automatic instrumentation of widely-used packages. This versatile library supports tracing for AI applications, allowing for both manual instrumentation and seamless integrations with tools like LlamaIndex, Langchain, and OpenAI. By employing LLM tracing, Phoenix meticulously logs the routes taken by requests as they navigate through various stages or components of an LLM application, thus providing a clearer understanding of system performance and potential bottlenecks. Ultimately, Phoenix aims to streamline the development process, enabling users to maximize the efficiency and reliability of their AI solutions.
Description
Tracetest is a powerful open-source testing framework that empowers developers to design and execute both end-to-end and integration tests by utilizing OpenTelemetry traces. This tool not only verifies the final results but also scrutinizes each stage of the workflow, guaranteeing that every part of a distributed system operates as intended. It integrates effortlessly with popular testing frameworks such as Cypress, Playwright, k6, and Postman, thus improving testability and transparency without necessitating any modifications to the existing codebase. By employing trace data, Tracetest uncovers problems like improper service interactions or performance hurdles that may go unnoticed with conventional testing approaches. Additionally, it works well with a wide range of observability platforms and can be seamlessly integrated into CI/CD pipelines to facilitate ongoing testing practices. Furthermore, Tracetest provides synthetic monitoring features, which help in the early identification of performance issues, ensuring that user experiences remain unaffected. This multifaceted tool not only enhances testing rigor but also promotes greater confidence in the reliability of distributed systems.
API Access
Has API
API Access
Has API
Integrations
GitHub
OpenTelemetry
Vercel
Arize AI
Cloudflare Workers
Codestral
CrewAI
Cypress
Dynatrace
Grafana
Integrations
GitHub
OpenTelemetry
Vercel
Arize AI
Cloudflare Workers
Codestral
CrewAI
Cypress
Dynatrace
Grafana
Pricing Details
Free
Free Trial
Free Version
Pricing Details
Free
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
Arize AI
Country
United States
Website
docs.arize.com/phoenix
Vendor Details
Company Name
Tracetest
Country
United States
Website
tracetest.io
Product Features
Product Features
Software Testing
Automated Testing
Black-Box Testing
Dynamic Testing
Issue Tracking
Manual Testing
Quality Assurance Planning
Reporting / Analytics
Static Testing
Test Case Management
Variable Testing Methods
White-Box Testing