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EmergentDB vs ZeusDB Vector Database

EmergentDB

emergentdb.com

ZeusDB Vector Database

zeusdb.com

Side by side

FigureEmergentDBZeusDB Vector Database
Price from$2/moFree
Free planYesYes
Free trial——
Open source——
WebYes—
Windows—Yes
macOS—Yes
LinuxYesYes
APIYesYes
Self-hostedYesYes
Plans published52
Spec score · Vector Databases8.4 (#5)7.5 (#19)
SupportThe Community plan includes community support.The open-source plan is community supported, while the enterprise plan lists priority support.

Plans

EmergentDB

  • CommunityFree
  • Launch$24 a year
  • Launch$29 a month
  • Scale$66 a year
  • Scale$79 a month

EmergentDB pricing →

ZeusDB Vector Database

  • Open SourceFree
  • EnterprisePriced on request

ZeusDB Vector Database pricing →

In their own words

EmergentDB

EmergentDB is an open-source, self-hosted vector database under the AGPL-3.0 license, also available as managed cloud. It automatically selects a search strategy for a dataset, without requiring manual tuning. Vector insertion and search are available through REST API endpoints; vectors can also be inserted with the TypeScript SDK, using embeddings from any source. Listed index types are Flat, HNSW, IVF, PQ, and Emergent, and SDK languages include TypeScript and Python. The free Community plan includes unlimited vectors, queries, and writes, plus community support and the Bolt-lite engine. Managed Launch is listed at $24 per year, billed annually, with a 500K-vector limit; Scale is $66 per year, billed annually, with a 2.5M-vector limit and dedicated instance. The site lists ISO 27001, SOC 2 Type II, GDPR, HIPAA-ready status, and US, EU, and Asia-Pacific data residency. It also reports a crash-safe write-ahead log and performance figures for specified workloads.

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ZeusDB Vector Database

ZeusDB Vector Database is a vector database for AI workloads including semantic search, retrieval-augmented generation, and recommendations. Its free, self-hosted Open Source plan provides a Python API and approximate nearest-neighbor search with HNSW graphs. Optional Product Quantization compresses vectors for memory efficiency. Metadata filtering is supported, and indexes, metadata, and quantized vectors can be saved and reloaded. Hybrid search is listed. Published wheels support Linux x86_64 and aarch64, macOS Apple Silicon, and Windows x86_64; the package page lists Python 3.10 through 3.14. LangChain and LlamaIndex integrations are available with vector-store filtering and persistence features. Hugging Face, Anthropic, OpenAI, and Semantic Kernel integrations are in development. ZeusDB lists an Enterprise plan with managed deployment, SSO, RBAC, audit trails, encryption, automated backups and restore, observability, SLAs, and priority support. The managed enterprise edition is in development. The company says it was founded and launched its first site in 2012.

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