Embedding API Cost Ranking
Every model ranked by cost for a typical embedding workload: 10,000 docs/day, 500 tokens per doc. Embedding-only models use input pricing; generative models also include a small output cost.
Top Picks by Scale
Strategy: Hybrid Embedding + Generation
For RAG pipelines, you need both embeddings AND generation. Use cheap embeddings + tiered generation to minimize total cost.
Embeddings are almost always the cheapest part of a RAG pipeline — typically under 2% of total cost. Don't optimize embeddings at the expense of quality; focus on the generation tier routing instead.
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Open Savings Calculator →Key Factors When Choosing an Embedding API
- Dedicated vs generative models: Dedicated embedding models (text-embedding-3-small, Cohere Embed) are purpose-built and cheapest. Generative models (GPT, Gemini, Claude) can embed but cost more. Use dedicated models for pure embedding workloads.
- Dimension count: Higher dimensions = better quality but more storage. text-embedding-3-small uses 1,536 dimensions. Cohere Embed v4 supports 1,024. Balance quality vs vector database storage cost.
- Batch processing: Most embedding APIs support batch requests (100-1,000 docs per call). Batch calls are 50-80% cheaper per document than single-document calls.
- Vector database cost: The embedding API cost is usually tiny compared to vector database hosting (Pinecone, Weaviate, Qdrant). Optimize storage dimensions and index type to reduce DB costs.
- Re-embedding frequency: Embed once, query many. Don't re-embed unchanged documents. Use content hashing to detect when re-embedding is needed.
- Dimensionality reduction: OpenAI's text-embedding-3 models support matryoshka dimensions — you can use 256 or 512 dimensions instead of 1,536 with minimal quality loss, cutting storage by 66-83%.
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