Together AI· Inference platform

Foundational research powering efficient inference at scale

AI has spent years in the spotlight for training: the massive, GPU-intensive process of building models. But for most teams deploying AI today, ongoing inference costs are what actually shape their unit economics. Estimates put inference at 80-90% of the total lifetime cost of a production AI system , simply because it runs continuously across every user query, agent step, and API call. And while training is a bounded investment, inference scales with every new user and use case you ship. At NVIDIA GTC 2026, NVIDIA CEO Jensen Huang framed this shift plainly: “People pay for information, but people mostly pay for work. Agentic systems get work done.” That shift from AI as a novelty to AI as a workhorse is exactly what’s reshaping infrastructure priorities. For Together AI, none of this is new. The inference imperative is what we’ve been building for. Our CTO Ce Zhang covered these dynamics in depth at GTC, sharing hard-won lessons from running some of the most demanding production inference workloads in the industry. Why inference is a different kind of hard …

Announced

04 May 2026 00:00 UTC