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What is Zipkin?

It assists in gathering timing details that are crucial for identifying latency problems in service architectures. Its capabilities include both the collection and retrieval of this vital information. With a trace ID from a log, you can seamlessly access the related data. In cases where a trace ID is unavailable, queries can be conducted using multiple parameters such as service names, operation titles, tags, and duration. Furthermore, important data is presented in a summarized format, showcasing the time allocation for each service as well as the success or failure rates of operations. The Zipkin user interface is equipped with a dependency diagram that visualizes the number of traced requests handled by each application, making it easier to spot general trends, including error patterns and interactions with legacy services. This visualization aids in pinpointing specific issues within the system. Ultimately, this tool not only streamlines the troubleshooting process but also deepens the understanding of service interactions in intricate architectures. The insights gained can lead to more informed decision-making and improvements in service performance over time.

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

GitHub
ActiveMQ
ContextForge MCP Gateway
RabbitMQ

Integrations Supported

GitHub
APIFuzzer
CoLab
Codestral
Codestral Mamba
Conda
Databricks
Gemini Enterprise Agent Platform
Guardrails AI
JavaScript
LlamaIndex
Ministral 3B
Ministral 8B
Mistral 7B
Mistral AI
OpenAI

API Availability

API Availability

Has API

Pricing Information

Pricing not provided

Pricing Information

Free
Free Version

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

Web-Based Support

Customer Service / Support

24 Hour Support
Web-Based Support

Training Options

Documentation Hub

Training Options

Documentation Hub

Company Facts

Organization Name

Zipkin

Company Website

zipkin.io

Company Facts

Organization Name

Arize AI

Company Location

United States

Company Website

docs.arize.com/phoenix

Categories and Features

Categories and Features

AI Observability

Not specified

LLM Evaluation

Not specified

Semantic Layer

Not specified

Telemetry

Not specified

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