Why your embedding model choice matters
In any retrieval-augmented generation (RAG) or semantic search system, the embedding model
is the foundation: it converts text into vectors so that similar meanings sit close
together. If the embeddings are weak, no amount of clever prompting or reranking downstream
will recover the relevant documents that were never retrieved. The leading options —
OpenAI’s text-embedding-3 family, Cohere’s embed models, Google’s embedding APIs, and a
deep open-source ecosystem — are compared primarily on retrieval quality, then on cost,
latency, dimensionality, and multilingual coverage.
The benchmark to know: MTEB
The Massive Text Embedding Benchmark (MTEB) is the standard yardstick. It scores models across dozens of datasets spanning retrieval, semantic similarity, clustering, classification, and reranking, producing a single comparable leaderboard. Use it as a starting filter, not gospel: a model that tops MTEB on English retrieval may underperform on your specific domain (legal, code, biomedical) or language. Always validate the shortlist on a sample of your own data, because real-world relevance is what counts.
Hosted APIs: OpenAI, Cohere, Google
OpenAI’s text-embedding-3-small and -large are popular defaults: strong quality,
simple API, competitive pricing, and support for dimension truncation to trade accuracy for
storage. Cohere’s embed models are notable for strong multilingual performance and a
dedicated reranking model that pairs well for two-stage retrieval. Google’s embedding
APIs integrate cleanly with the rest of its AI stack and offer solid multilingual coverage.
All three remove infrastructure burden and scale on demand; the trade-offs are per-call cost,
rate limits, and sending your text to a third party.
Open-source models: control and zero marginal cost
The open-source ecosystem — models you can run via libraries like Sentence Transformers — now includes options that rank competitively on MTEB. Their advantages are decisive for certain use cases: no per-call cost (run unlimited embeddings on your own GPU), full data privacy (text never leaves your infrastructure), and no rate limits. The cost is operational: you provision and maintain GPU servers, manage scaling, and own uptime. For high-volume pipelines, privacy-sensitive data, or cost-constrained startups, self-hosting frequently wins on total cost of ownership.
How to choose for your system
Start with quality on your own data, not just the leaderboard: embed a representative sample
and measure retrieval relevance. Then weigh the practical axes — cost (per-token API vs
GPU hosting), latency (matters for live search, less for batch indexing), dimensions
(smaller is cheaper to store and search; use truncation if the model supports it),
multilingual needs, and max input length. Critically, commit to one model across both
indexing and querying — switching means re-embedding your whole corpus. For most teams
shipping fast, a hosted API like OpenAI’s text-embedding-3 is the pragmatic default; for
scale, privacy, or cost, a strong open-source model is the long-term winner.