What is the cheapest embedding API?

OpenAI text-embedding-3-small is the cheapest embedding API at $0.02 per 1M tokens. For 1 million documents at 500 tokens each, it costs just $10 to index. Google text-embedding-004 offers a free tier for low-volume use.

How much does embedding cost per document?

At 500 tokens per document: OpenAI small ($0.02/1M): $0.00001/doc, OpenAI large ($0.13/1M): $0.000065/doc, Cohere v3 ($0.10/1M): $0.00005/doc, Google v4: Free for low volume. That's $10, $65, $50, and ~$0.50 respectively for 1M documents.

Is OpenAI embedding cheaper than Cohere?

Yes, OpenAI text-embedding-3-small ($0.02/1M) is 80% cheaper than Cohere embed-v3 ($0.10/1M). However, Cohere supports 100+ languages natively, while OpenAI small is primarily English-optimized. For multilingual needs, Cohere may be better value despite higher per-token cost.

How do I reduce embedding costs?

5 strategies: 1) Use text-embedding-3-small ($0.02/1M) — 85% cheaper than large. 2) Reduce dimensions to 1024d (67% storage savings). 3) Optimize chunk size to 256-512 tokens. 4) Batch API calls (2048 inputs per request). 5) Cache embeddings to avoid re-embedding.

Cheapest AI Embedding API

Find the cheapest embedding model for your use case. 6 models ranked by cost with an interactive calculator. From $0.02/1M tokens — or free.

Use case:

Scale:

Tokens per document ~375 words per 500 tokens

Number of documents

Queries per day

Re-embedding frequency How often documents change

Cheapest Embedding Models

All Embedding Models Ranked by Cost

Sorted cheapest first. Adjust inputs above to see your personalized ranking.

#	Model	Provider	Price/1M	Dimensions	Languages	Indexing Cost	Monthly Query Cost	Total Monthly

How to Choose the Cheapest Embedding API

The cheapest embedding API depends on your language requirements, quality needs, and scale. Here's the decision framework:

For English-Only RAG

OpenAI text-embedding-3-small ($0.02/1M) is the clear winner. It's 85% cheaper than the large model with 90% of the quality. At 1,536 dimensions, it provides excellent retrieval accuracy for most English use cases. Start here and upgrade only if retrieval quality is insufficient.

For Multilingual RAG

Cohere embed-v3 ($0.10/1M) supports 100+ languages natively with 1,024 dimensions. While 5x more expensive than OpenAI small, it's the only model that delivers consistent quality across languages. For global applications, the multilingual premium is worth it.

For Prototyping

Google text-embedding-004 (Free tier) is unbeatable for prototyping and low-volume use. The free tier handles thousands of documents. Graduate to OpenAI small when you need production reliability.

For High-Quality Search

OpenAI text-embedding-3-large ($0.13/1M) with 3,072 dimensions delivers the best retrieval quality. Worth the premium for legal, medical, or financial applications where accuracy matters more than cost.

Embedding Cost Optimization Checklist

Start with text-embedding-3-small — cheapest and good enough for 90% of use cases
Reduce dimensions — use 1024d instead of 3072d for 67% storage savings
Optimize chunk size — 256-512 tokens balances quality and cost
Batch API calls — 2,048 inputs per request, 10-20x faster
Cache embeddings — don't re-embed unchanged documents
Monitor usage — embedding is 5-15% of RAG costs, but scales with document count

Need to estimate your full RAG pipeline cost?

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