Mithril (formerly ML Foundry, formerly Foundry) operates as a GPU aggregator, or what they term an “AI omnicloud.” Their core philosophy is that the primary problem in the GPU market is one of price discovery and market inefficiency. Their solution is to create a “fluid market” through aggregation and abstraction, allowing costs to adjust dynamically to reflect increased supply. We completely disagree with both the premise and solution.
The premise that the GPU market lacks price discover is flawed and represents a fundamental misunderstanding of the market. In our experience, over 90% of the GPU cloud rental volume is done on long-term contract between enterprises with a standard 25% down and monthly payments through the end of the term. In other words, a typical B2B transaction.
The reason for this, which has been detailed throughout this report, is that not all GPUs are deployed equally. GPU compute is not a commodity. Mithril, and other companies trying to aggressively financialize the GPU market as though it is crude oil or lumber, is solving for price per GPU-hr as the only variable. This is an important criteria, but it is just one the 129 criteria that we use to assess a provider’s quality, and is often a poor proxy for the realized TCO of a cluster.
By building abstractions on top of an aggregated and abstracted “roll-of-the-dice” marketplace of underlying providers, Mithril places the entire operational burden on the end user. As a provider, Mithril has no control over their customer’s support experience, orchestration software preferences, networking and storage performance, monitoring experience, or, most critically, the reliability and security posture of the cluster.
With that said, even if GPU compute was a liquid, commoditized market, we would expect the winner to have open access to many GPU providers, real-time data feeds on availability, forecasts for upcoming supply, proxy information for realized quality from end users… but unfortunately we are left with this instead: