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What is Scorable?

Scorable is a cutting-edge platform that leverages artificial intelligence for evaluation and monitoring, designed specifically to aid developers in measuring, managing, and improving the performance of applications built with large language models. This platform enables teams to create tailored automated evaluators, often referred to as AI "judges," which assess the responses generated by AI systems and evaluate whether these outputs meet predefined quality metrics such as accuracy, relevance, helpfulness, tone, and compliance with policies. Developers can express their evaluation goals in simple terms, allowing Scorable to design a bespoke assessment framework that tests AI outputs against particular contextual standards, extending beyond conventional benchmarks. Furthermore, these evaluators can be easily integrated into the application's source code, facilitating ongoing oversight of AI systems, such as chatbots, retrieval-augmented generation (RAG) systems, or autonomous agents, even during their operation in live environments. This functionality guarantees that developers uphold rigorous standards for AI performance over time and are able to quickly adjust to changing needs, thereby fostering a more responsive approach to application development and deployment. In addition, Scorable's adaptability ensures that as technology evolves, developers are equipped with the tools necessary to maintain optimal performance and quality in their AI applications.

What is Arize Phoenix?

Phoenix is an open-source library designed to improve observability for experimentation, evaluation, and troubleshooting. It enables AI engineers and data scientists to quickly visualize information, evaluate performance, pinpoint problems, and export data for further development. Created by Arize AI, the team behind a prominent AI observability platform, along with a committed group of core contributors, Phoenix integrates effortlessly with OpenTelemetry and OpenInference instrumentation. The main package for Phoenix is called arize-phoenix, which includes a variety of helper packages customized for different requirements. Our semantic layer is crafted to incorporate LLM telemetry within OpenTelemetry, enabling the automatic instrumentation of commonly used packages. This versatile library facilitates tracing for AI applications, providing options for both manual instrumentation and seamless integration with platforms like LlamaIndex, Langchain, and OpenAI. LLM tracing offers a detailed overview of the pathways traversed by requests as they move through the various stages or components of an LLM application, ensuring thorough observability. This functionality is vital for refining AI workflows, boosting efficiency, and ultimately elevating overall system performance while empowering teams to make data-driven decisions.

Media

Media

Integrations Supported

Python
Slack
Okta
TypeScript

Integrations Supported

Python
Slack
APIFuzzer
Amazon Bedrock
Codestral Mamba
Conda
Databricks
Haystack
JavaScript
LlamaIndex
Ministral 8B
Mistral NeMo
Mixtral 8x22B
Mixtral 8x7B
OpenAI
Pixtral Large
Vercel

API Availability

Has API

API Availability

Has API

Pricing Information

$19 per month
Free Version

Pricing Information

Free
Free Version

Supported Platforms

SaaS
On-Prem

Supported Platforms

SaaS

Customer Service / Support

Web-Based Support

Customer Service / Support

24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Online Training

Training Options

Documentation Hub

Company Facts

Organization Name

Scorable

Company Location

Finland

Company Website

scorable.ai/

Company Facts

Organization Name

Arize AI

Company Location

United States

Company Website

docs.arize.com/phoenix

Categories and Features

Agentic AI

Not specified

AI Governance

Not specified

AI Guardrails

Not specified

AI Observability

Not specified

LLM Evaluation

Not specified

Categories and Features

AI Observability

Not specified

LLM Evaluation

Not specified

Semantic Layer

Not specified

Telemetry

Not specified

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