Silicon Data's Pitch to Make AI Costs Predictable

When Chinese AI startup DeepSeek claimed it had trained a GPT-4-class model for just $6 million, the AI community was quick to express skepticism. A 67-billion-parameter model developed for a fraction of what OpenAI or Google might spend? Unlikely, many, including Elon Musk, argued. Yet whether the number was accurate or inflated almost didn’t matter. The deeper problem was that no one could say with certainty what a model like that should cost to build in the first place.

AI has become one of the most resource-intensive technologies in history, but the financial infrastructure underpinning it remains opaque and unstructured. As companies pour billions into training frontier models and building out data center infrastructure, there's a fundamental lack of transparency around core inputs, especially when it comes to GPUs, the computational engines that power it all. What is the going rate for GPU compute? How does that rate vary by region, cloud provider, or chip model? These are critical questions that many in the industry struggle to answer.

Silicon Data Aims to Make GPU Costs Transparent

This is exactly the challenge Silicon Data is setting out to solve. Founded in 2024 and based in New York, the startup recently raised $4.7 million in seed funding led by DRW and Jump Trading Group, with participation from several other strategic investors. The company is building what it hopes will become the backbone of financial intelligence in the AI infrastructure market: real-time data, benchmarks, and tools that bring transparency to GPU pricing and compute infrastructure costs.

At the center of Silicon Data’s product offering is the H100 Rental Index, the first daily benchmark that tracks the cost of renting NVIDIA’s H100 GPUs, which are among the most in-demand chips for AI workloads. The index is built using pricing data sourced across hyperscalers, neocloud platforms, and private infrastructure providers. It adjusts for a wide range of variables, including hardware configuration, platform pricing, and location. As CEO Carmen Li puts it, “This is the first step, to help market participants gain transparency into the convoluted world of AI costs.”