Embedding Models Compared: Picking the Right Vectorizer
Embedding models compared: OpenAI, Cohere, open-source options. Dimensions, cost, retrieval quality, and how to choose for your RAG pipeline.
An embedding model converts text into vectors for semantic search and RAG. The model you choose affects retrieval quality, index size, and cost — so picking one deserves more than a default.
How to evaluate embedding models
- Retrieval quality on your domain: test with real queries.
- Embedding dimensions: affects vector index size and memory.
- Token limits and chunk-size compatibility.
- Cost per million tokens to embed the corpus and queries.
- Latency: matters for user-facing semantic search.
Managed embedding APIs
Managed APIs (OpenAI, Cohere, and others) are easy to start with: call, get vectors, done. You trade vendor dependency and per-token cost for zero infra. Good for fast iteration.
Open-source options
Open-source embedding models (e.g., sentence-transformers family) run on your own infra. No per-call fees, full control, and strong quality on many languages. You pay in GPU time and ops.
Why dimensions matter
Higher dimensions usually mean better quality but bigger indexes and slower search. Many modern models support Matryoshka-style dimension reduction so you can trade a little recall for much smaller storage.
Recommendation
FAQ
What is the best embedding model?+
There is no universal best. Evaluate candidates on your domain with real queries. Managed APIs are easiest; open-source models save money at scale.
How much do embedding models cost?+
Managed APIs charge per million tokens for corpus and query embedding. Open-source models cost GPU time. Corpus size and re-index frequency drive the bill.
Do embedding dimensions affect cost?+
Indirectly. More dimensions mean bigger indexes and more memory per vector, which raises storage and search cost. Use dimension reduction where possible.
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