title: "Shared vs. Dedicated AI Inference: Embed & Rerank"

The choice between shared and dedicated AI inference is determined by several key factors for specific workloads like embedding and reranking. Traffic patterns, latency requirements, and overall cost must be carefully evaluated to select the appropriate deployment model. Shared inference can be cost-effective for sporadic traffic, while dedicated inference provides consistent performance for high-volume or low-latency needs. This decision framework helps balance performance with budget for AI model deployment.