Embedding Model Selection — Choose the Right Embeddings

I will assess your use case and deliver a written embedding model selection report — comparing OpenAI text-embedding-3, Cohere Embed, Voyage AI, and open-source models (BGE, E5, GTE) against your task type, language requirements, dimensionality constraints, latency, and cost at your usage volume. Embedding model selection is consequential because switching models later requires re-embedding your entire corpus — a decision made without systematic evaluation can lock you into an expensive API-based model when an open-source model would perform equally well at zero marginal cost, or into a general-purpose model when a domain-specialised model would significantly outperform it for your task.

The report covers your task type assessment (retrieval, classification, clustering, or semantic similarity), model comparison across performance, cost, dimensionality, and latency, a domain adaptation recommendation if available, and a clear recommendation with TCO analysis.