ZeusDB Vector Database
ZeusDB Vector Database: A focused Python vector database for small projects needing basic ANN search. Ranked #25 of 27 in Vector Databases by our editors (6.9/10); pricing: Free plan; best for small Python projects needing basic ANN search.
At a glance
- Editor score6.9 / 10
- PricingFree plan
- Best forSmall Python projects needing basic ANN search
- Free planYes
- Paid fromFree
- DeploymentBoth
- Founded2012
- Facts checked22 Sep 2026
Where it wins
- HNSW indexing supports approximate nearest-neighbor search.
- Product Quantization can compress vectors and preserve index state.
- Metadata filtering, persistence, batch queries, logging, and metrics are included.
Where it doesn't
- The Python-only API limits SDK and language choices.
- Hybrid and sparse search are outside its verified scope.
- Managed Enterprise availability may be limited while it is in development.
Our verdict on ZeusDB Vector Database
ZeusDB Vector Database is a Rust-powered, open-source vector database for machine-learning and AI workloads. Its Python API supports adding vectors, similarity searches, batch queries, metadata filtering, and persistence. The product is aimed at semantic search, retrieval-augmented generation, recommendations, and other embedding-based applications. Its self-hosted scope makes it a practical fit for small Python projects that need basic approximate nearest-neighbor search without adopting a broader platform.
The Open Source plan is free and self-hosted. It includes HNSW approximate nearest-neighbor indexing, optional Product Quantization, typed metadata fields and operators, persistence for indexes and metadata, structured logging, metrics, and community support. The Enterprise plan adds managed hosting, SSO and RBAC, audit trails and encryption, automated backups and restore, observability and SLAs, and priority support. The managed Enterprise edition is in development, so organizations evaluating it for production operations should account for that availability stage.
ZeusDB connects with LangChain and LlamaIndex, giving Python teams a route into common AI application frameworks. Its focused feature set covers core vector retrieval needs, including compression and metadata-aware search, but the published positioning does not verify hybrid or sparse search. Teams that want a narrow, self-hosted Python component should consider ZeusDB; teams needing broader language support, verified hybrid or sparse retrieval, or an established managed deployment should evaluate an alternative.
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ZeusDB Vector Database pricing
All 2 ZeusDB Vector Database plans and prices →
ZeusDB Vector Database fact sheet
| Free plan | Yes |
|---|---|
| Paid from | Free |
| Deployment | Both |
| Hybrid search | Yes |
| Metadata filtering | Yes |
| Sparse vectors | Not verified |
| Maximum dimensions | Not verified |
| Index types | HNSW |
| SDK languages | Python, Rust |
| Deployment | Cloud, Self-hosted |
| Support | Community, Docs |
| Built for | Small business, Mid-market, Enterprise (editorial estimate) |
| Integrations | 6 integrations: LangChain, LlamaIndex, Hugging Face, Anthropic, OpenAI, Semantic Kernel |
| Pricing | Free plan (source) |
| Website | zeusdb.com |
| Facts checked | 22 Sep 2026 |
ZeusDB Vector Database integrations
ZeusDB Vector Database lists 6 integrations on its own site.
- LangChain
- LlamaIndex
- Hugging Face
- Anthropic
- OpenAI
- Semantic Kernel
Alternatives to ZeusDB Vector Database
- ElasticsearchA broad hybrid-search platform for teams adding vectors to mature analytics stacks.9.3
- Vertex AI Vector SearchA managed Google Cloud option for teams building dense, sparse, and hybrid search.9.2
- VikingDBA cloud vector database with dense, sparse, hybrid, filtered, and multimodal retrieval.9.1
See all ZeusDB Vector Database alternatives →
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Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026
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