Velvet
About Velvet
Velvet is designed for engineers working with OpenAI and Anthropic APIs, providing an innovative logging system that warehouses every API request into a PostgreSQL database. By leveraging these logs, users can analyze and fine-tune their AI features, enhancing efficiency while reducing operational costs.
Velvet offers a free tier for up to 10,000 requests per month, allowing users to explore its capabilities without any commitment. Upgrading to paid tiers unlocks additional features, increased usage limits, and premium support, offering excellent value tailored to meet the needs of growing engineering teams.
Velvet features a user-friendly interface that ensures an optimal browsing experience. The layout is intuitive, enabling easy navigation through logs, data storage, and experiment frameworks. Unique functionalities like smart caching and customizable logging enhance usability, making Velvet a robust tool for engineers.
How Velvet works
Users engage with Velvet by first creating an account and accessing comprehensive documentation. The onboarding process involves setting the base URL to Velvet's gateway and connecting to either Velvet's database or their own. With just two lines of code, users can start logging requests, optimizing features, and conducting experiments. The platform's design enables engineers to seamlessly analyze and evaluate their AI models through insightful logs and enhanced observability.
Key Features for Velvet
Logging API Requests
Velvet's logging feature is crucial for engineers, capturing every API request to a PostgreSQL database. This allows in-depth analysis and evaluation of AI models, providing essential insights to optimize performance. By leveraging these logs, users can fine-tune their features effectively and with confidence.
Smart Caching System
Velvet implements a smart caching system that reduces latency and operational costs. This feature allows users to handle repeated requests efficiently, ensuring swift responses to identical queries. With Velvet’s intelligent caching, engineers gain significant performance benefits and improved cost management for their AI operations.
Experiment Framework
The experiment framework within Velvet empowers engineers to run targeted tests on their AI models. Users can select specific datasets to evaluate performance, enabling a structured approach to optimize outputs at scale. This feature enhances the analytical capabilities of Velvet, supporting innovation and model fine-tuning.
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