ZeroEntropy's zembed-1 embedding model converts documents into dense vectors for semantic search and retrieval-augmented generation.
zembed-1 is ZeroEntropy's state-of-the-art embedding model, designed to encode text into high-quality dense vectors that capture semantic meaning. It is built for production retrieval systems where precise document matching directly affects downstream generation quality.
The model integrates with the ZeroEntropy API and can be deployed through Hugging Face, supporting developers who need reliable embeddings for RAG pipelines, recommendation engines, and agentic search workflows.
| Attribute | Embedding Model | Command Models | OpenAI API | Qdrant Vector Database | |
|---|---|---|---|---|---|
| Provider | |  |  |  | |
| Founded | 2024 | 2019 | 2015 | 2021 | |
| Sells To | Developers, Enterprise | Enterprise | Enterprises, Individuals | — | |
| Pricing Model | SaaS | Recurring, SaaS | Subscription, Usage-based | — | |
| Ownership | Private | Venture-backed | Private | Venture-backed |
Provider
Founded 2024
Sells To Developers, Enterprise
Pricing Model SaaS
Ownership Private
Provider
Founded 2019
Sells To Enterprise
Pricing Model Recurring, SaaS
Ownership Venture-backed
Provider
Founded 2015
Sells To Enterprises, Individuals
Pricing Model Subscription, Usage-based
Ownership Private
Provider
Founded 2021
Sells To —
Pricing Model —
Ownership Venture-backed
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