ScitiX Launches Production Inference Platform for Enterprise AI

ScitiX's entry into the production inference market reflects a wider shift in how enterprises approach AI deployment. Rather than treating inference as a supporting function bolted onto existing ML pipelines, the company is positioning it as the operational core—the component that ultimately determines whether AI workloads run reliably, cost-effectively, and compliantly at scale. This framing is significant because it acknowledges a real pain point: organizations experimenting with models in the lab often struggle when moving to production, where multiple models need to coexist, failures must be gracefully handled, and data residency or zero-retention policies become non-negotiable.

The platform's neutrality—serving as a routing layer rather than a model monopoly—is a deliberate architectural choice that addresses customer lock-in risk. By supporting open-source, fine-tuned, and third-party models through a standardized API, ScitiX is positioning itself not as a model provider but as the orchestration backbone. The operational metrics the company reports (1 trillion tokens daily, 99.9% uptime, >90% cache hit rate) suggest the system is built to handle production scale from day one. The fact that RadixArk, already running SGLang in production, has chosen to migrate demanding scenarios to ScitiX implies the platform delivers material improvements in responsiveness and reliability over what the market was offering before.