Together AI· Inference platform

What does 99.9% uptime mean for inference?

TL;DR The short version: Each reliability tier maps to a specific failure domain, and each one requires its own architecture to survive it. Roughly speaking: 99% means your architecture can survive node-level failures: GPU hardware faults, driver crashes, thermal events. Getting there generally takes automated health checking, node draining, and fast replica replacement within a single DC. 99.9% means your architecture can survive a full data center failure. That usually means model weights deployed across two facilities, enough capacity on each side to absorb the full load, and live traffic routing to both, not a cold standby. 99.99% means your architecture can survive a regional outage. That typically calls for multi-region deployment with AZ redundancy and reserved failover capacity. Reliability numbers are easy to publish. What’s hard is explaining what they mean: which failure domains the architecture actually covers, whether the provider controls the infrastructure at those layers, and what happens when something breaks at 3 a.m. Together runs inference for teams like Cursor, Decagon, Cartesia, and Yutori. …

Announced

16 Jul 2026 00:00 UTC