Three AI Inference Execution Paths: How to Match Your Workload to a Solution

Inference isn’t one-size-fits-all.

 No two AI initiatives run  inference the same way. A team iterating on a new model, a team serving a customer-facing agent under SLA, and a team that wants to own every layer of the stack all need different things—yet they all need the same basic production requirements: capacity guarantees, observability, predictable cost, and expert support.

In this 30-minute briefing, CoreWeave VP of Product Urvashi Chowdhary walks through CoreWeave Inference’s three execution paths—Serverless Inference, Dedicated Inference, and Inference on CKS—how they’re all powered by the same award-winning CoreWeave architecture, and how to match your workload to the right path so you can get the performance you need with economics that make sense for your team.

In this webinar, we’ll cover: 

• Why production inference isn’t one-size-fits-all
• CoreWeave Inference’s  three execution paths—Serverless Inference, Dedicated Inference, and Inference on CKS—and what kinds of workloads best fit each one
• How CoreWeave lets you move from prototype to production scale without replatforming or losing execution clarity
• How full-stack optimization shows up in independent benchmarks, including Artificial Analysis and MLPerf
• What to bring to your CFO and CISO: the framework to find the most economical and performant inference solution for your workload

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