KDB.AI vs ZeusDB Vector Database

KDB.AI is a vector database and search engine for building AI applications with real-time data. Its hybrid search combines dense and sparse vector search with metadata filtering and temporal similarity search. It supports retrieval-augmented generation using unstructured text, video, audio, and image data. Listed vector indexes include Flat, qFlat, IVF, IVFPQ, HNSW, qHNSW, and BM25. Documentation lists integrations with LangChain, Vector IO, and LlamaIndex, while native NVIDIA cuVS integration enables GPU-accelerated vector search. Deployment options include a managed Cloud Starter Edition and a self-managed Server Standard Edition. The free Cloud Starter Edition provides 4 GB of memory per instance, 30 GB of storage, and a 10 MB query size. Its agreement makes no commitment to availability or uptime and provides the service without warranty. The cloud FAQ points users to a Slack community or [email protected] for assistance. The maker presents research assistants, portfolio management, real-time alpha and beta extraction, and image processing as use cases. KDB.AI is offered through API, web, and self-hosted platforms.

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.