Show HN: Warehouse OpenAI requests to your own database Today we’re launching Velvet, an AI gateway for warehousing OpenAI and Anthropic requests to your PostgreSQL instance. 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://ift.tt/J9VCxIz More details in our docs https://ift.tt/bdfK06w [1] https://ift.tt/6gTRLV3 https://ift.tt/f0cgUCp August 28, 2024 at 10:21PM
Show HN: Warehouse OpenAI requests to your own database https://ift.tt/4QN8wzZ
Related Articles
Show HN: Loglayer: A fluid logging interface for JavaScript loggers https://ift.tt/AgMxfSOShow HN: Loglayer: A fluid logging interface for JavaScript loggers Th… Read More
Show HN: An AI logo generator that can also generate SVG logos https://ift.tt/klHKeoYShow HN: An AI logo generator that can also generate SVG logos Hey eve… Read More
Show HN: MamaRap – AI-Generated Personalized Music Videos for Mothers https://ift.tt/c3wt8RSShow HN: MamaRap – AI-Generated Personalized Music Videos for Mothers … Read More
Show HN: I made a calculator builder to increase engagement and conversions https://ift.tt/19VsxeNShow HN: I made a calculator builder to increase engagement and conver… Read More
Show HN: I created 3,800+ Open Source React Icons (Beautiful, Rounded Style) https://ift.tt/MxRvVIdShow HN: I created 3,800+ Open Source React Icons (Beautiful, Rounded … Read More
Show HN: A multi-modal and AI first Knowledge Management System https://ift.tt/s7SgGU2Show HN: A multi-modal and AI first Knowledge Management System User V… Read More
Show HN: AI Runner – my personal opensource, local, multi-modal, AI assistant https://ift.tt/K9TrRUoShow HN: AI Runner – my personal opensource, local, multi-modal, AI as… Read More
Show HN: A web debugger an ex-Cloudflare team has been working on for 4 years https://ift.tt/quA7PVDShow HN: A web debugger an ex-Cloudflare team has been working on for … Read More
0 Comments: