AI agents

Choosing embedding and reranking models

• Embedding Model Selection: Dimensions, Cost and Domain Fit
• When a Reranker Pays for Itself Against a Bigger Cap
• A Provider Interface for Embeddings: Batching and Retries
• A Reranker Is a Model Call: Budgeting Latency and Tokens
• Choosing an Embedding Model: Dimensions, Cost and Domain Fit
• Embedding a Tenant's Data: Where Vectors and Text Live
• Reranker Latency and Token Budget: A Per-Query Cost Model
• Reranker Versus a Bigger k: Which Spends the Budget Better
• Sizing a Rerank Batch: Cost per Candidate Row and Plateau
• When a Reranker Beats a Bigger Embedding Model
• When a Reranker Precedes Extraction: Ordering and Latency