What Is Embedding Cost?
How AI Vector Embeddings Are Priced in 2026
Embeddings convert text into numerical vectors for semantic search and RAG systems. They're priced per token — and are dramatically cheaper than LLM inference. This guide explains embedding pricing, which provider to choose, and how to estimate costs for your knowledge base. Last verified: 2026-04-01.
What Are Embeddings and Why Do They Cost Money?
An embedding model takes text and converts it into a high-dimensional vector (typically 768–3,072 numbers) that captures semantic meaning. Similar concepts end up with similar vectors, enabling semantic search.
Embeddings are used in:
- RAG (Retrieval-Augmented Generation): Embedding queries and documents to find relevant context for LLMs
- Semantic search: Finding similar content without exact keyword matches
- Recommendation systems: Finding similar users, products, or articles
- Duplicate detection: Identifying near-duplicate content
The cost comes from running the embedding model on GPUs — but embedding models are much smaller than LLMs, so they're dramatically cheaper.
Embedding Provider Pricing (2026)
| Provider / Model | Price/1M tokens | Dimensions | Notes |
|---|---|---|---|
| OpenAI text-embedding-3-small | $0.020 | 1,536 | Best cost/quality for most use cases |
| Cohere embed-v4 (English) | $0.100 | 1,024 | Best for enterprise RAG; native reranking |
| OpenAI text-embedding-3-large | $0.130 | 3,072 | Higher accuracy for complex retrieval |
| Google text-embedding-004 | $0.025 | 768 | Strong multilingual support |
| Voyage AI voyage-3 | $0.060 | 1,024 | Anthropic-recommended for Claude RAG |
| Self-hosted (nomic-embed, BGE) | ~$0.000 | 768–1,024 | Compute cost only; free at small scale via Ollama |
Two Distinct Embedding Costs
Embedding costs split into two phases with very different patterns:
1. Ingestion (one-time or periodic)
Embedding your knowledge base when building the vector index. This is a one-time cost, repeated only when documents change.
| Knowledge base size | Tokens | Cost (embed-3-small) | Cost (embed-3-large) |
|---|---|---|---|
| 100 docs (company wiki) | 500K | $0.01 | $0.07 |
| 5,000 docs (large knowledge base) | 25M | $0.50 | $3.25 |
| 50,000 docs (enterprise content) | 250M | $5.00 | $32.50 |
| 1M documents (large corpus) | 5B | $100 | $650 |
Ingestion is a negligible cost even for very large knowledge bases. A 50,000-document library costs $5 to embed — less than a cup of coffee.
2. Query embedding (ongoing per search)
Each user query must also be embedded to find similar vectors. Short text, very cheap:
| Volume | embed-3-small cost | Monthly (30 days) |
|---|---|---|
| 1,000 queries/day (50 tokens avg) | $0.001/day | $0.03/mo |
| 10,000 queries/day | $0.01/day | $0.30/mo |
| 100,000 queries/day | $0.10/day | $3.00/mo |
| 1M queries/day | $1.00/day | $30/mo |
Query embedding cost is negligible vs LLM inference cost at every scale. 1M daily queries = $30/month on embeddings vs hundreds or thousands on LLM.
Embeddings Are Not Your Bottleneck
In a RAG system, the cost breakdown is typically:
- LLM inference: 95–99% of total cost
- Vector DB (Pinecone, etc.): 1–4% of total cost
- Embeddings: 0.1–1% of total cost
Optimizing your embedding model choice will have minimal impact on total costs. Focus optimization effort on LLM model selection and context size instead.
Self-Hosted Embeddings: When It Makes Sense
Open-source embedding models (nomic-embed-text, BGE-M3, all-MiniLM) run locally at zero API cost:
- Data sovereignty: Documents never leave your infrastructure — required for many legal/medical use cases
- Offline environments: Air-gapped systems where API calls aren't possible
- Very high ingestion volume: Re-embedding a 100M-document corpus regularly — API cost at $0.02/M still = $2,000 per full re-index
For typical SaaS applications with <10M tokens/month in embeddings, OpenAI text-embedding-3-small at $0.02/M is the easiest and most cost-effective choice.
Calculate Your RAG Infrastructure Cost
Embeddings are just one piece — see the full RAG cost breakdown including vector DB and LLM inference.
AI RAG Cost Guide 2026