How to Choose the Right Embedding Model for RAG
Your embedding model choice affects retrieval quality, storage costs, and query latency. Here's how to pick the right one for your RAG pipeline — with real cost comparisons.
Why Your Embedding Model Matters
In a RAG (Retrieval-Augmented Generation) pipeline, the embedding model converts your documents and queries into vector representations. The quality of these embeddings directly determines whether your system retrieves the right context — and whether your LLM generates accurate answers.
A poor embedding choice means:
- Irrelevant retrieval: Wrong documents fed to the LLM, leading to hallucinations
- Higher costs: Larger dimensions = more storage, more compute, higher vector DB bills
- Slower queries: Larger embeddings take longer to compute and search
Embedding Models Compared
| Model | Provider | Cost/1M tokens | Dimensions | Max Tokens | Best For |
|---|---|---|---|---|---|
| text-embedding-3-small | OpenAI | $0.02 | 1536 | 8191 | Best value |
| text-embedding-3-large | OpenAI | $0.13 | 3072 | 8191 | Highest quality |
| embed-english-v3.0 | Cohere | $0.10 | 1024 | 512 | Search & clustering |
| embed-multilingual-v3.0 | Cohere | $0.10 | 1024 | 512 | Multilingual |
| embedding-001 | $0.00 | 768 | 2048 | Free tier | |
| Llama Embed | Together.ai | $0.00 | 4096 | 512 | Self-hosted |
Cost Analysis: Embedding 1M Documents
Let's calculate the cost to embed 1 million documents averaging 500 tokens each (500M total tokens):
| Model | Cost per 1M tokens | Cost for 500M tokens | Storage (1M docs) |
|---|---|---|---|
| OpenAI small | $0.02 | $10.00 | ~6 GB |
| Cohere | $0.10 | $50.00 | ~4 GB |
| OpenAI large | $0.13 | $65.00 | ~12 GB |
| $0.00 | $0.00 | ~3 GB |
Key insight: OpenAI's text-embedding-3-small at $0.02/1M tokens is the best value for most use cases. Google's embedding-001 is free but has a smaller context window (2048 tokens).
Quality vs Cost: When Does It Matter?
Use OpenAI small ($0.02) when:
You want the best balance of quality and cost. 1536 dimensions handles most RAG tasks well. Perfect for chatbots, Q&A, and document search.
Use OpenAI large ($0.13) when:
Retrieval quality is critical. Legal, medical, or financial RAG where wrong context = real consequences. 3072 dimensions capture more nuance.
Use Cohere ($0.10) when:
You need built-in search optimization or multilingual support. Cohere's models are specifically tuned for search and clustering tasks.
Use Google ($0.00) when:
Budget is the top priority and your documents are short (<2048 tokens). Good for prototyping and low-stakes applications.
Total RAG Cost: Embeddings + Vector DB + Generation
Embeddings are just one part of your RAG pipeline cost. Here's a full breakdown for a system processing 10,000 queries/day:
| Component | Budget Stack | Mid-Tier Stack | Premium Stack |
|---|---|---|---|
| Embedding (query + doc) | $0.60/mo | $3.00/mo | $3.90/mo |
| Vector DB (Pinecone/Weaviate) | $0/mo (free tier) | $70/mo | $200/mo |
| LLM generation | $15/mo (Flash) | $150/mo (Sonnet) | $450/mo (GPT-5.5) |
| Total | ~$16/mo | ~$223/mo | ~$654/mo |
Key takeaway: Embedding costs are a small fraction (1-5%) of total RAG costs. Don't over-optimize embeddings at the expense of retrieval quality — the LLM generation cost dwarfs embedding costs.
Dimension Reduction: A Cost Trick
OpenAI's embedding-3 models support dimension reduction without retraining. You can reduce from 3072 to 256 dimensions with minimal quality loss:
Dimension Reduction Impact
| Dimensions | Storage (1M docs) | Quality Impact |
|---|---|---|
| 3072 (full) | ~12 GB | Baseline |
| 1536 | ~6 GB | Negligible |
| 512 | ~2 GB | ~2-3% accuracy drop |
| 256 | ~1 GB | ~5-8% accuracy drop |
If you're on a tight budget, use 512 dimensions from text-embedding-3-large. You get 97% of the quality at 1/6 the storage cost.
5-Step Decision Framework
- Start with OpenAI text-embedding-3-small ($0.02/1M tokens) — it's the default for a reason: great quality, low cost, 1536 dimensions
- Test retrieval quality — measure recall@10 on your actual data. If it's below 90%, upgrade to text-embedding-3-large
- Check document length — if documents exceed 8191 tokens, split them or use Cohere (512 token limit but search-optimized)
- Consider multilingual needs — Cohere embed-multilingual-v3.0 handles 100+ languages; OpenAI is primarily English
- Optimize dimensions — use dimension reduction to cut storage costs without meaningful quality loss
Calculate your RAG pipeline cost: Use our free calculator to estimate embedding + generation costs for your specific workload.
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