Building an Embedding Pipeline: Model Selection and Cost Optimization

What are embeddings?

Embeddings are numerical representations of text that capture semantic meaning. They're used for:

• Semantic search: Finding similar documents based on meaning
• Recommendations: Suggesting related content
• Clustering: Grouping similar documents together
• RAG: Retrieving relevant context for LLMs

Key cost factors for embeddings

Embedding costs depend on:

• Input tokens only: No output token cost
• Document volume: Number of documents to embed
• Document length: Tokens per document
• Re-embedding frequency: How often you need to update embeddings

Cost estimation example

Let's estimate costs for a typical embedding workload:

Note: This is a simplified estimate. Actual costs may vary based on document length, model choice, and other factors.

Model selection for embeddings

What to look for

• Low input price: Since only input tokens are charged
• Good performance: High-quality embeddings for your use case
• Dimensionality: Balance between accuracy and storage costs
• Context window: Large enough for your documents

Top embedding models by cost

Cost optimization tips

• Batch processing: Embed multiple documents together for efficiency
• Caching: Cache embeddings to avoid re-embedding unchanged documents
• Dimensionality reduction: Use lower-dimensional embeddings when possible
• Incremental updates: Only re-embed changed documents
• Right-size your model: Use smaller models for less critical applications

Architecture patterns

Simple embedding pipeline

For most applications, a simple embedding pipeline works well: embed documents, store vectors, and search for similar documents.

Advanced embedding pipeline

For complex applications, consider: hybrid search (keyword + semantic), re-ranking, chunking strategies, and vector database optimization.

Compare embedding models

Ready to compare embedding models side by side? Use our tools: