Actian VectorAI DB

Actian VectorAI DB is a vector database built for AI agents, giving them persistent memory and low-latency retrieval on infrastructure you own.

Most vector databases were designed for cloud-native deployments and human-scale query patterns. VectorAI DB is built for the concurrent, high-frequency behavior of agent fleets and runs consistently across edge, on-premises, air-gapped, and cloud environments without architecture changes. Pinecone is cloud-only. Qdrant and Weaviate support self-hosted deployment but are designed primarily for cloud-scale workloads, not edge and disconnected environments.

VectorAI DB integrates natively with LangChain, LlamaIndex, and Hugging Face. Python and JavaScript SDKs are available. Community plugins extend support to CrewAI, LangGraph, and Mastra. Full integration docs are at docs.vectoraidb.actian.com .

VectorAI DB delivers 745.2 QPS on 10M vectors at 768 dimensions, more than 22x faster than Milvus and Qdrant on identical hardware. It is built for the parallel, high-frequency query behavior that agent fleets generate, not for the sequential patterns of human-facing applications.

The free tier is the VectorAI DB Community Edition. Free forever, no billing setup. It includes 5K vector capacity, deploys on local development machines, and comes with community support.

VectorAI DB supports two index types: HNSW (Hierarchical Navigable Small World) and IVF (Inverted File). Both can be paired with vector quantization, which compresses each vector into a smaller representation to reduce the index footprint further.

IVF is not a replacement for HNSW. It is a second option, and the right choice depends on what on your deployment constraints. Use HNSW when recall at low latency is the priority and memory is not the constraint. It is the default for most server-side workloads. Use IVF when the index has to fit in a smaller footprint, or when the data changes constantly. HNSW’s layered graph costs more memory per vector and is expensive to repair on every insert and delete. IVF’s flat clustered storage uses less memory per vector and its cluster assignments are cheaper to rebuild or partially update.

VectorAI DB is model-agnostic and works with embeddings from any provider: OpenAI, Anthropic, Cohere, Hugging Face, or any custom model you bring.

Yes. VectorAI DB stores vector embeddings from text, images, audio, and video.

Yes. Transact through your existing AWS agreement and draw down committed spend, including EDP commitments, without starting a new vendor review. View on AWS Marketplace.