More than 60% of enterprises have experimented with AI agents, but fewer than 10% in any business function have scaled them. Inference, the process by which users interact with AI models and get results, is the biggest gap. Under real traffic, workloads that ran fine in a prototype start to strain on latency, cost, and compliance.
This guide is for enterprise AI leaders planning production AI. It shows how your use cases turn into concrete inference requirements, so you can pick infrastructure that scales with your roadmap rather than limiting it.
You’ll learn:
- How inference workloads differ from AI training, and why that changes your infrastructure plans
- How to balance accuracy, latency, and cost for your applications and agents
- Which requirements matter most for regulated and multimodal industries like financial services, healthcare, and media
- The biggest inference challenges and how to get ahead of them
- How CoreWeave Inference supports enterprise AI products from prototype to production to iterating the next version.
