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.
Stop guessing — get exact Embeddings API costs
No signup required to 95-model comparison, migration code snippets, PDF reports, price alerts, and cost monitoring. ✅ All tools free.
Free Tools →Find the cheapest model for your embedding workload
Enter your usage and see all 95 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%.
Related Tools
- Free MCP Server — Query live pricing data in Claude Code, Cursor
- other AI tools
- Savings Calculator — See how much you can save by switching models
- Cost Explorer — See all 95 models ranked by your usage
- Cheapest AI API for RAG — Full RAG pipeline cost comparison
- Cost Optimizer — Get a personalized savings report
- State of AI API Pricing 2026 — 95 models compared, 5 key trends, 40-96% savings strategies
- Cheapest AI API Finder — Find the absolute cheapest model
- Migration Checklist — 9 provider migration routes with code examples
- Deprecation Tracker — 6 deprecated models and migration paths
- Budget Planner — Describe your app, get instant cost estimates
Related Reading
- Best AI API for RAG — Full RAG use-case guide with model recommendations
- Best AI API for Data Extraction — Data extraction model comparison
- Cheapest LLM APIs in 2026 — Full ranking of every model
- Cheapest AI API for RAG — RAG-specific cost comparison