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…
In plain words
Velvet is a proxy service that intercepts OpenAI API calls and stores requests and responses directly in a user's PostgreSQL database. This allows developers to query and analyze their LLM usage patterns using SQL rather than relying on third-party interfaces. The platform supports adding custom headers for caching and metadata enrichment, and is built on Cloudflare Workers to minimize latency overhead. It is designed for developers and teams who need direct database access to their AI infrastructure logs.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
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) -- 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!
More ai this month
the category →
I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
AI · 17d ago · simedw.com
Astute▲585Automate your B2B brand going viral, with new media creators
AI · 18d ago · company-app.joinastute.com
Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
AI · 27d ago · cactuscompute.com
Launched alongside, September 2024
the whole month →
BeforeSunset AI 2.0▲1,267Personalized AI daily planning that suits your life
AI · 2024 · beforesunset.ai
