Show HN: Velvet – Store OpenAI requests in your own DB

Hey HN! We’re Emma and Chris, founders of Velvet (https://www.usevelvet.com).

Velvet proxies OpenAI calls and stores the requests and responses in your PostgreSQL database. That way, you can analyze logs with SQL (instead of a clunky UI). You can also set headers to add caching and metadata (for analysis).

Backstory: We started by building some more general AI data tools (like a text-to-SQL editor). We were frustrated by the lack of basic LLM infrastructure, so ended up pivoting to focus on the tooling we wanted. So many existing apps, like Helicone, were hard to use as power users. We just wanted a database.

Scale: We’ve already warehoused 50m requests for customers, and have optimized the platform for scale and latency. We’ve built the proxy on Cloudflare Workers, and latency is nominal. We’ve built some “yak shaving” features that were really complex such as decomposing OpenAI Batch API requests so you can track each log individually. One of our early customers (https://usefind.ai/) makes millions of OpenAI requests per day, up to 1500 requests per second.

Vision: We’re trying to build development tools that have as little UI as possible, that can be controlled entirely with headers and code. We also want to blend cloud and on-prem for the best of both worlds — allowing for both automatic updates and complete data ownership.

Here are some things you can do with Velvet logs:

- Observe requests, responses, and latency

- Analyze costs by metadata, such as user ID

- Track batch progress and speed

- Evaluate model changes

- Export datasets for fine-tuning of gpt-4o-mini

(this video shows how to do each of those: https://www.youtube.com/watch?v=KaFkRi5ESi8)

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To see how it works, try chatting with our demo app that you can use without logging in: https://www.usevelvet.com/sandbox

Setting up your own proxy is 2 lines of code and takes ~5 mins.

Try it out and let us know what you think!

Show HN: Velvet – Store OpenAI requests in your own DB
usevelvet.com

This seems to require sharing our data we provide to OpenAI with yet another party. I don't see any zero-retention offering.

The self-serve version is hosted (it’s easy to try locally), but we offer managed deployments where you bring your own DB. In this case your data is 100% yours, in your PostgreSQL. That’s how Find AI uses Velvet.

Seems neat - I'm not sure if you do anything like this but one thing that would be useful with RAG apps (esp at big scales) is vector based search over cache contents. What I mean is that, users can phrase the same question (which has the same answer) in tons of different ways. If I could pass a raw user query into your cache and get back the end result for a previously computed query (even if the current phrasing is a bit different than the current phrasing) then not only would I avoid having to submit a new OpenAI call, but I could also avoid having to run my entire RAG pipeline. So kind of like a "meta-RAG" system that avoids having to run the actual RAG system for queries that are sufficiently similar to a cached query, or like a "approximate" cache.