Field noteOct 10, 20262 min read1 source

Shared vs. Dedicated AI Inference: Embed & Rerank

In one minute

Cohere’s article argues that inference economics for Embed and Rerank should be sized around the work a request represents, rather than request rate alone.

Thesis

For embedding and reranking deployments, request shape and traffic regularity are more useful sizing inputs than RPM alone.

Observed facts

Cohere describes a catalog indexing example with about 100 descriptions of roughly 200 tokens each in one embedding request.
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Cohere estimates a reranking example with 50 candidate documents at about 11,000 tokens, and contrasts roughly 4,400 tokens at 20 candidates with about 22,000 at 100.
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Mechanism

Reranking processes a query against each candidate, increasing work as candidate depth grows; bulk embedding requests likewise represent more work per call. Traffic regularity affects utilization of provisioned capacity.

Why now

Teams choosing between shared consumption-based and dedicated provisioned inference need to model workload composition and utilization rather than compare headline prices or peak request rates alone.

Implications

Measure request size, rerank candidate depth, traffic regularity, and latency targets before choosing deployment mode.
Treat Cohere’s examples as sizing guidance, not a universal cost crossover or independently verified benchmark.

Evidence boundary

The supplied public source is a Cohere-authored operational article with illustrative workloads and recommendations; it does not establish independently verified performance results or a universal break-even threshold.

Watch conditions

Watch for published workload-specific break-even data linking request shape and traffic patterns to observed cost, throughput, and latency.

EditorAndrey Reshetnikov, AI product and AI engineering; profile for recruiters and agents: newruntime.com/owner-profile.json

Evidence / sources

1cohere.com/blog/shared-or-dedicated-inference-for-embed-rerank

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