Voyage AI embeddings and rerankers vs ZeroEntropy zerank and zembed

Voyage AI embeddings and rerankers

200 million free tokens per current model, then $0.02 to $0.12 per million. Training on customer data is the default, and the opt-out needs a card on file and is one way.

ZeroEntropy zerank and zembed

All four models now open weights under Apache 2.0 on Hugging Face. The hosted API was discontinued after 4 September 2026 and signups closed on 24 July 2026.

Voyage AI embeddings and rerankers

1. Opt the organisation out of training before sending anything private. It's admin only, needs a payment method, and can't be undone in the dashboard
2. Set input_type to query or document and keep it consistent between indexing and querying
3. Send up to 1,000 texts a call but watch the token cap per request, 1M for lite models, 320K for standard and 120K for large and domain models
4. Ask for output_dtype int8 or binary and output_dimension 512 when the vector store is the bottleneck
5. Use rerank-3-lite over the top 100 from a cheap first pass, at $0.02 per million tokens

ZeroEntropy zerank and zembed

1. Don't call api.zeroentropy.dev. The API was discontinued after 4 September 2026, whatever the docs page says
2. Self-host zerank-2 or zembed-1 from Hugging Face under Apache 2.0 if you want the same model
3. Pick a hosted reranker elsewhere if you can't self-host. ZeroEntropy's own guide names Cohere and Voyage
4. Re-embed the corpus if you move off zembed-1. Vectors from another model aren't compatible