Key Takeaways:
- Inference—not training—is now the primary driver of real business value in AI, and requires a fundamentally different approach to infrastructure and operations.

- The biggest barrier to successful AI adoption is the “production gap,” where systems that perform well in testing break down under real-world conditions like scale, latency, and variability.

- Modern AI workloads—especially agentic and multi-step systems—introduce new challenges such as bursty demand, observability blind spots, and unpredictable costs that traditional environments aren’t designed to handle.

- Bridging the gap between experimentation and production requires rethinking how performance, reliability, and control are managed across the full AI stack.

- CoreWeave is purpose-built for production AI, with differentiated full-stack optimization from metal to model and third-party validation including MLPerf v6.0 leadership, a SemiAnalysis ClusterMAX Platinum rating, and NVIDIA Exemplar Cloud validation.