Arize AI provides an AI observability and evaluation platform that enables developers to monitor, troubleshoot, and optimize large language models (LLMs) through performance tracing, data visualization, and automated evaluation workflows. The platform addresses issues of model performance degradation and data drift, ensuring that AI applications operate effectively and deliver reliable outcomes.
Funding
$61M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
AI applications, particularly those powered by large language models (LLMs), often suffer from performance degradation, data drift, and unexpected behaviors in production. Identifying and resolving these issues requires extensive manual effort, hindering the ability to iterate and improve AI-powered products effectively. Current monitoring solutions lack the necessary tools for tracing, evaluating, and troubleshooting complex AI workflows.
Solution
Arize AI offers an AI observability and evaluation platform designed to help developers monitor, troubleshoot, and optimize LLMs and other AI models. The platform provides end-to-end tracing, data visualization, and automated evaluation workflows to address model performance degradation and data drift. By leveraging these capabilities, AI engineers can quickly identify bottlenecks in LLM calls, understand agentic paths, and ensure AI behaves as expected. Arize AI enables proactive safeguards over AI inputs and outputs, surfacing insights and streamlining the process of identifying and correcting errors, ultimately improving the reliability and effectiveness of AI applications.
Target Audience
Arize AI targets AI developers, data scientists, and machine learning engineers building and deploying AI-powered applications, including those using LLMs, who need to monitor, troubleshoot, and optimize model performance in production.
Features
- End-to-end tracing to visualize and debug data flow in generative AI applications
- Automated monitoring and dynamic dashboards to surface key metrics such as hallucination or PII leaks
- AI-powered workflows to analyze and refine the performance of generative applications
- Native support for experiment runs to accelerate iteration cycles for LLM projects
- Prompt playground and management for testing changes to LLM prompts with real-time performance feedback
- OpenTelemetry integration for robust, standardized instrumentation across the AI stack
- Open-source LLM evaluations library and tracing code for seamless integration
- AI-driven similarity search to find and analyze clusters of data points