Comment by zozbot234
4 hours ago
> But also, the latest DeepSeek is 1.6T parameters. “Choosing” to run this locally is a choice that comes with a seven digit price tag
Unless you're specifically thinking about running the model at stock precision in a datacenter environment and generating ~100 tok/s or more on a 24/7 basis (the equivalent of a >$1000/mo spend even on the cheapest third-party APIs), that's very likely off by multiple orders of magnitude. Even then, experimentation can be done with cheap neoclouds on a pay-as-you-go basis.
I’m aware. The context of the discussion here is choosing DeepSeek over a US hosted model from Google, Anthropic or OpenAI.
The equivalent comparison would be running it at full frontier quality.
If you want less than frontier quality, there’s tons of great open weight models other than DeepSeek.
> cheap neoclouds
Again, fails the compliance checkbox.
> Again, fails the compliance checkbox.
OK, then the not-so-cheap hyperscalers that these enterprises are already relying on. E.g. AWS Bedrock will run these models. It's silly to insist on all three of your checkboxes being ticked anyway - U.S. proprietary models don't give you that because the frontier ones are super expensive and the mini models have only barely acceptable cost.
What’s considered expensive in the procurement process is not necessarily the TCO, but often just the year one cost. Which is part of the reason why pay as you go SaaS is so successful.
Yeah, Bedrock would be the answer to run DeepSeek in the enterprise. But with the options on Bedrock, DeepSeek fighting for a position somewhere in the middle of the cost/quality spectrum. Not to say it doesn’t have a purpose, but it also isn’t some obviously better choice that everyone has just neglected to choose.
Azure serves DeepSeek V4 Pro, about 10X cheaper than GPT-5.5.
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