The best Embedding Models
Every option ranked — open-source, self-hostable, and commercial — by our transparent Sovereignty Score, with honest trade-offs so you choose what fits you, not us.
The models that turn text into vectors for RAG and semantic search — hosted APIs and open models you can run yourself, compared on quality, price, and data control.
L4Models & tooling — layer 4 of the AI stack- 1
Nomic Embed
Top pickOpen sourceThe sovereign default — Apache-2.0, one command in Ollama, up to 8k context with rotary scaling.
Free — runs locally via Ollama or sentence-transformers; your hardware is the cost · in our OpenAI Embeddings API comparison →
What is Nomic Embed? →92sovereignty - 2
BGE-M3
Open sourceThe multilingual workhorse — 100+ languages, MIT, three retrieval modes.
Free (MIT) — GPU recommended for throughput; ~$0.001/1M tokens at spot-GPU scale · in our OpenAI Embeddings API comparison →
What is BGE-M3? →90sovereignty - 3
Jina Embeddings v4
CommercialStrong open weights — but read the license before shipping.
Free for research; commercial use via paid API or license · in our OpenAI Embeddings API comparison →
What is Jina Embeddings v4? →55sovereignty - 4
Voyage AI
CommercialThe benchmark leader — hosted accuracy worth paying for.
voyage-4-lite $0.02/1M · voyage-3-large $0.18/1M tokens · in our OpenAI Embeddings API comparison →
What is Voyage AI? →38sovereignty - 5
Cohere Embed 4
CommercialThe enterprise multilingual option with a 128k context.
$0.12/1M tokens; private-deployment options for enterprise · in our OpenAI Embeddings API comparison →
What is Cohere Embed 4? →36sovereignty
What will the GPU cost? Compare every company’s hourly price
Is it free?
The free tier, its real limits and where you start paying — read from each vendor's own pricing page and dated.
Run these yourself
What each one actually needs — real RAM, honest running cost, and the setup time nobody quotes.
- Self-hosting Nomic Embed Around 1 GB of RAM or VRAM for one request at a time (estimate from the 274–547 MB weights; no figure is published). Batching long inputs raises it.
- Self-hosting BGE-M3 Roughly 3–4 GB of RAM on CPU, or 2–3 GB of VRAM with use_fp16=True, for one request at a time (estimate; no figure is published). Encoding at the full 8,192-token max_length in large batches needs far more, which is why the card tells you to lower max_length if you can.
Replacing a specific tool?
Head-to-head comparisons for each popular embedding models product.
Straight head-to-heads
Two embedding models tools, side by side — verified facts and a plain verdict.