Warehouse every AI request to your database. Query logs to analyze usage and costs, evaluate models, and generate datasets. Install the proxy with 2 lines of code. It’s free to get started, and you own your data.
Hey Product Hunt 👋 Emma and Chris here from Velvet. We built an AI gateway to warehouse OpenAI and Anthropic requests to your database. Engineers use Velvet logs to analyze usage and costs, evaluate models, and generate datasets.
Our first product was an AI SQL editor. It worked really well, but we had limited insights into what happened between our app and OpenAI. We started storing requests to our database, giving us the ability to query data directly. It gave us so much more control over developing our AI features.
We didn’t think of it as a product until one of our customers (Find AI) asked to use it. We warehoused over 3 million requests for them in the first week, logging up to 1,500 requests per second during their launch. Now we have many startups using Velvet daily for caching, evaluations, and analysis of opaque endpoints like the Batch API.
It's easy to get started! Once set up, we warehouse every request to your database.
Requests are formatted as JSON. You can include custom metadata in the header - like user ID, org ID, model ID, and version ID. This means you can run complex SQL queries unique to your app. Granularly calculate costs, evaluate new models, or identify datasets for fine-tuning.
We'd love to hear from you! Email [email protected] with any feedback or questions.
Velvet launched on Product Hunt on August 28th, 2024 and earned 212 upvotes and 40 comments, placing #8 on the daily leaderboard. Warehouse every AI request to your database. Query logs to analyze usage and costs, evaluate models, and generate datasets. Install the proxy with 2 lines of code. It’s free to get started, and you own your data.
On the analytics side, Velvet competes within Software Engineering, Artificial Intelligence and Data & Analytics — topics that collectively have 514.7k followers on Product Hunt. The dashboard above tracks how Velvet performed against the three products that launched closest to it on the same day.
Who hunted Velvet?
Velvet was hunted by Emma Lawler. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Emma Lawler
Hey Product Hunt 👋 Emma and Chris here from Velvet. We built an AI gateway to warehouse OpenAI and Anthropic requests to your database. Engineers use Velvet logs to analyze usage and costs, evaluate models, and generate datasets.
Our first product was an AI SQL editor. It worked really well, but we had limited insights into what happened between our app and OpenAI. We started storing requests to our database, giving us the ability to query data directly. It gave us so much more control over developing our AI features.
We didn’t think of it as a product until one of our customers (Find AI) asked to use it. We warehoused over 3 million requests for them in the first week, logging up to 1,500 requests per second during their launch. Now we have many startups using Velvet daily for caching, evaluations, and analysis of opaque endpoints like the Batch API.
Try a sandbox demo → usevelvet.com/sandbox (no signup needed)
Read the docs → docs.usevelvet.com
Get started with two lines of code 🧑🏻💻
It's easy to get started! Once set up, we warehouse every request to your database.
Requests are formatted as JSON. You can include custom metadata in the header - like user ID, org ID, model ID, and version ID. This means you can run complex SQL queries unique to your app. Granularly calculate costs, evaluate new models, or identify datasets for fine-tuning.
We'd love to hear from you! Email [email protected] with any feedback or questions.
Watch a demo video to learn more.