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Average Ratings 0 Ratings

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ease
features
design
support

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Write a Review

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

Scorable is an innovative platform utilizing AI for evaluation and monitoring, specifically crafted to assist developers in assessing, regulating, and enhancing the performance of applications developed with large language models. The platform empowers teams to construct personalized automated evaluators, often termed AI "judges," which evaluate the responses of AI systems to users and determine if the outputs align with established quality metrics such as accuracy, relevance, helpfulness, tone, and adherence to policies. Developers can articulate their measurement objectives in straightforward language, and Scorable then creates a customized evaluation framework that tests AI outputs against specific contextual criteria, moving beyond standard benchmarks. These evaluators can be seamlessly integrated into the application's code, enabling continuous oversight of AI systems, including chatbots, retrieval-augmented generation (RAG) systems, or autonomous agents, even while they are functioning in live production settings. This capability ensures that developers maintain high standards for AI performance over time and can swiftly adapt to evolving requirements.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

Python
Slack
Amazon Bedrock
Codestral
Codestral Mamba
CrewAI
GitHub
Guardrails AI
JupyterLab
LangChain
LlamaIndex
Ministral 3B
Mistral 7B
Mistral Large
Mistral NeMo
Mixtral 8x22B
Mixtral 8x7B
Model Context Protocol (MCP)
TypeScript
Vercel

Integrations

Python
Slack
Amazon Bedrock
Codestral
Codestral Mamba
CrewAI
GitHub
Guardrails AI
JupyterLab
LangChain
LlamaIndex
Ministral 3B
Mistral 7B
Mistral Large
Mistral NeMo
Mixtral 8x22B
Mixtral 8x7B
Model Context Protocol (MCP)
TypeScript
Vercel

Pricing Details

Free
Free Trial
Free Version

Pricing Details

$19 per month
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

Scorable

Country

Finland

Website

scorable.ai/

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