Store your LLM requests, responses, and parameters. Analyze, optimize, and fine-tune your AI features.
Store your requests, responses, and parameters to Postgres.
Natively query costs, performance, features, endpoints, and models.
Send raw LLM logs to any platform your team wants to use.
Experiment with prompts, RAG, and models to optimize responses.
Automatic scaling, rate limiting, caching, and error handling.
Use your data to fine-tune your own model when you're ready.
Capture every raw LLM log at scale. Use data to evaluate performance, trace problems, optimize cost, and fine-tune your own models.
Store LLM requests, responses, and parameters to your own PostgreSQL instance. Get a queryable table to analyze and optimize over time.
Unlock an evaluation loop to build consistent and trustworthy features. Resolve issues, evaluate models, and train AI specific to your system.
Use Velvet to identify and export a fine-tuning dataset.
AI-powered B2B search engine logged 1,500 requests per second.
Lessons learned using LLMs to automate data workflows.