Cheapest AI API for Embeddings
Find the cheapest AI API for text embeddings. We ranked 42 models by cost — from $0.002 per 1,000 documents.
Calculate Your Embedding Cost
Enter your document volume to see the cheapest models for your embedding workload.
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.
Find the cheapest model for your embedding workload
Enter your usage and see all 42 models ranked by cost. Free, no signup.
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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