Fair comment, especially as you mentioned in cases where capacity is fixed as part of a reservation. With fixed reservations, we've seen examples where basic monitoring doesn't always tell the full story such as where instances appear running, but sm activity is near 0. We've also heard from teams using on-demand capacity across clouds that they haven't yet stitched together their monitoring to see exactly who is using what, and where, in a single dashboard. That's something that we help provide insights into with our monitoring dashboards.
I’m not running fleets of gpus so I’m not your target market, but can you tell me more about who is doing this type of large scale training outside of companies like anthropic, OpenAI, xai…. The big teams. I’m just curious?
I/m no expert in this area, but I doubt the longer term plan is build a viable business by selling the product. In a prediction market I'd bet that coming out of AWS they saw this problem first hand and acquisition (really acquihire) is the outcome. Based on the way Amazon operates they should also know it's a. hired by someone else, or b. have your producted copied/cloned by AWS.
Thanks, really fair feedback. Honestly we're still landing on exact tiers as we work with our early customers. The model is per GPU under management, lower at scale. Happy to chat if you want to learn more — founders@usechamber.com
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> most teams we talk to can't even tell you how many GPUs are in use right now
how can this be? isn’t this a trivial metric to pull from any clouds monitoring service?
to get the good ones (H100+) you generally have to reserve them, a fixed cost you pay monthly and can’t pretend to not know