Shared or Dedicated Inference for Embed & Rerank
Cohere, Friday, October 9th, 2026
Cohere explains how request shape and traffic patterns, not headline price, decide between shared and dedicated inference for Embed and Rerank.
Cohere offers a framework for choosing between shared, consumption-based inference and dedicated, provisioned inference for embedding and reranking, built around request profile, traffic pattern, utilization and deployment choice.
Because a few large embedding requests can use far more compute than thousands of small search queries, requests per minute alone is a poor sizing metric.
Dedicated inference usually suits steady, predictable workloads and consumption-based inference suits bursty traffic, with the break-even point depending on request size, token volume and rerank candidate count.