Together AI, an inference provider for teams like Cursor and Decagon, outlines distinct failure modes across compute (VRAM ECC errors, thermal throttling), network (switch, transceiver issues), storage, and software layers for GPU inference workloads. The company explains that achieving 99.9% uptime requires surviving full data center failures with continuous live traffic to redundant facilities, while 99.99% demands multi-region deployment with reserved capacity. Together AI emphasizes its chip-to-token visibility and infrastructure ownership as key to delivering these SLAs, contrasting with providers renting hyperscaler capacity.
GPU inference reliability is exponentially harder than traditional services, with each 'nine' of uptime requiring distinct architectural solutions and direct infrastructure ownership.