What is Velvet?
Velvet is a developer gateway designed to analyze, evaluate, and monitor AI-powered features. Acquired by Arize in 2025, Velvet now integrates into Arize's unified AI platform, offering a seamless experience for AI evaluation and observability. The tool enables developers to accelerate the development of AI applications and agents, then perfect them in production. With Velvet, teams can trace LLM calls, evaluate model performance, and optimize AI applications in real time. It provides deep insights into AI behavior, helping to identify issues like hallucinations, latency, and cost inefficiencies. Velvet supports open-source Phoenix for LLM tracing and evaluation, allowing for easy experimentation and optimization. Additionally, it integrates with LiteLLM, a gateway that provides model access, fallbacks, and spend tracking across over 100 LLMs, all in the OpenAI format. This makes Velvet highly versatile for various use cases, including chatbots, content generation, and data analysis. Technical details include support for multiple LLM providers, real-time monitoring, and comprehensive evaluation metrics. Velvet is ideal for AI engineers, data scientists, and product teams looking to improve AI reliability and performance. By joining Arize, Velvet now offers enterprise-grade scalability and security, making it suitable for large-scale deployments. The platform helps reduce development time, improve model accuracy, and lower operational costs. Use cases include monitoring AI agents in customer service, evaluating generative models for content creation, and optimizing recommendation systems. Velvet's integration with Arize provides a unified view of AI operations, from development to production. Overall, Velvet empowers teams to build better AI faster and with confidence.
Use Cases
Key Features
LLM call tracing model performance evaluation real time monitoring hallucination detection latency analysis cost optimization open source Phoenix support LiteLLM integration multi provider support comprehensive evaluation metricsWho is it for?
AI engineers, data scientists, product teams, developers, DevOps engineers, SREs
