Open Models Can Win on Cost and Control | CoreWeave Blog

This post contributes to the ML Inference Benchmarking thread by shifting the focus from pure performance benchmarks to a cost and control analysis of open-weight models versus closed-source models. It introduces the concept that for many production workloads, 'good enough' performance from open models is sufficient, and the primary differentiator becomes cost and control. It details the cost savings achievable with open models (e.g., $1/million tokens vs. $50/million for closed models) and the advantages of self-hosting and fine-tuning for control and customization. The post also highlights the critical role of robust infrastructure in realizing these cost savings, implicitly reinforcing the need for CoreWeave's platform.