inference — Running a trained model to get answers — the phase where AI is actually used, as opposed to trained.
Why it matters
If you depend on an inferenceRunning a trained model to get answers — the phase where AI is actually used, as opposed to trained.Full definition → provider, this breaks down what each uptime tier really guarantees and the failure domains it must survive, giving you concrete questions to ask before committing to an SLA.
Key quotes
“Hitting 1M tokens per minute per GPU at 200 TPS, or sub-50ms TTFT on voice models with custom kernels, leaves limited slack. Adding reliability to a system like that is exponentially harder with each nine.”
Together AI
“a redundant system that isn’t regularly tested under real conditions is like calling a player off the bench who hasn’t practiced in six weeks.”
Together AI
“A provider renting capacity from a hyperscaler or neocloud doesn’t own their failure domains. When something breaks at the power or cooling layer, they’re filing a ticket with whoever does.”
Together AI
“The key word here is reserved. Not “we can route traffic there” but “we have room sitting idle there right now.””
Together AI
“We measure at inference completion, not at the gateway. A request that reaches the load balancer but fails at the GPU is downtime in our accounting.”
