The bottom line: Availability guarantees for inference services are only meaningful when it is clear which failure domains they actually cover.
Together AI examines the infrastructure requirements behind common availability guarantees for AI inference services. CTOs should question these figures and understand which failure scenarios each level covers.
Availability guarantees such as 99%, 99.9%, or 99.99% uptime are frequently published by inference providers, but say little if it is not specified which failure scenarios are covered by them. Each tier requires different technical safeguards and presupposes that certain sources of error are managed.
For CTOs, it is critical to understand which failure domains a provider can actually survive with its reliability promise: data center outages, network partitions, hardware failures, or software bugs. A service with 99.9% availability can handle failures differently than one with 99.99%, and both can face different critical infrastructure events.
Before an organization commits to a provider, the following should be clarified: Which failure scenarios are actually covered? Is the guarantee based on measurements in production or on theoretical models? Is there geographic redundancy, and under what conditions is it activated? How is availability measured and proven? These questions determine whether an availability guarantee is viable for the respective use case.
Source: www.together.ai · Published July 16, 2026
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