Show HN: Warehouse OpenAI requests to your own database
usevelvet.com
We originally built an AI SQL editor, but realized that customers were using it to monitor their AI requests in production. We had already built an AI request warehousing tool internally to debug our SQL editor and gave some customers access.
A few days into testing this idea, our pilot customer launched [1] and we began warehousing 1,500 requests per second. We worked closely with their engineering team in the following weeks, completely re-architecting Velvet for scale and additional features (such as Batch support). Along the way, other companies began seeking out Velvet to get visibility into their own LLM requests.
We’re launching our AI gateway as a self-serve product today, but our pilot customers are already warehousing over 3 million requests per week - so the system is stable and performant.
What makes Velvet unique is that you own the data in your own database. Also, we’re the first proxy that gives visibility into OpenAI batch calls - so you can observe and monitor async calls that save you money.
Some technical notes:
- Supports OpenAI and Anthropic endpoints
- Data is formatted as JSON and logged to your own PostgreSQL instance (can add support for other databases for paying customers).
- You can include queryable metadata in the header, such as user ID, org ID, model ID, and version ID.
- Built on Cloudflare workers, which keeps latency minimal (using our caching feature will reduce latency overall)
- Built for security + starting process of SOC II soon
Why warehouse your requests?
- Understand where money is spent. Use custom headers to calculate the cost per customer, model, or service.
- Download real request/response data, so you can evaluate new models (e.g., re-running requests with a cheaper mini model)
- Monitor time to completion of batch jobs. (e.g., OpenAI says 24 hours, but our customers average 3-4 hours)
- Export a subset of example requests for fine-tuning
It’s just a 2 line code change to get started.
Try a sandbox demoing the logging proxy here: https://usevelvet.com/sandbox
More details in our docs https://docs.usevelvet.com