Deploy AI agents with persistent memory. Ship them anywhere.
What developers are building with VectorAI DB
CrewAI + VectorAI DB
Isolated, persistent memory for every agent in a multi-agent crew.
LlamaIndex + VectorAI DB
Local RAG pipeline with no cloud vector service. Runs on-premises or at the edge.
Trace
Semantic search for missing persons cases. Bridges the vocabulary gap between families and forensic records, fully on-device.
Revvec
Air-gapped RAG pipeline for defense, aerospace, and pharma. Runs on a single Mac; no internet required.
FixFirst
Offline maintenance copilot for industrial technicians. Search manuals and incident history by error code, photo, or voice note.
LangChain + VectorAI DB
Agent memory that persists across sessions without cloud dependencies.
LangGraph + VectorAI DB
Agent state that survives disconnected environments.
Most vector databases were built for human query patterns. AI agents are different.
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Numbers that hold up under load
QPS
745.2 QPS on 10M vectors at 768 dimensions. 22x faster than Milvus and Qdrant.
retrieval accuracy
Latency p99
13ms p99 at 99% recall on self-hosted hardware where your agents run.
What you get
Memory that survives every session
Recall across sessions, scaled with usage, inside your perimeter.
Monitor it from the tools you already run
Native Prometheus metrics in your existing Grafana dashboards.
Parallel queries without the slowdown
Parallel reasoning steps and memory lookups, recall intact.
Ship the same build everywhere
Laptop to data center with zero rewrites, any hardware.
Pass review without re-architecting
GDPR, HIPAA, and residency hold by where the data sits.
Watch VectorAI DB run
Watch a full agent deployment from zero to first query.
Get started with your framework
Drop it into the stack you already work in.
Use the VectorAI DB Python SDK directly for any custom agent stack. You bring the embedding model; VectorAI DB handles storage and search.
# Install
pip install actian-vectorai
from actian_vectorai import VectorAIClient
client = VectorAIClient(host="localhost:8080")
client.create_collection("agent-memory", dim=768)
results = client.query(
collection="agent-memory",
vector=embeddings,
top_k=10
)Run it however you ship
Python and JavaScript SDKs, a REST API, and a single Docker container that runs the same on a laptop, an edge box, or a Kubernetes cluster.
How VectorAI DB compares
An honest look at where each vector database runs and how it performs. Throughput measured on 10M vectors at 768 dimensions, with identical hardware.
VectorAI DB |
Pinecone |
Qdrant |
Weaviate |
|
|---|---|---|---|---|
Throughput at 10M vectors |
745 QPS |
~33 QPS |
~34 QPS |
Self-hosted varies |
Runs on resource-constrained edge hardware (Jetson, Pi) |
Yes |
No |
Limited |
Limited |
Same API from prototype to production |
Yes |
Cloud-only |
Yes |
Yes |
On-premises and air-gapped deployment |
Yes |
No |
Yes |
Yes |
Fully managed cloud option |
Self-managed |
Yes |
Cloud |
Cloud |
Who builds on VectorAI DB
Teams shipping where the data lives and the network can not always reach.
Patient data stays inside your perimeter throughout every query.
Deploy to: Hospital DCs, clinic servers, research
Consistent across hybrid, edge, and multi-site infrastructure regardless of connectivity, compliance, or hardware constraints.
Deploy to: Hybrid, edge + cloud, multi-site
FAQ
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 HNSW and other ANN indexing methods for low-latency, high-accuracy search at scale.
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.
Start free, no billing setup
Everything you need to prototype agent memory on your own machine, today.
Billing-free access
Download and start using VectorAI DB immediately. No billing setup required.
Vector indexing and search
ANN indexing (HNSW), vector search APIs, and monitoring to support prototypes up to 5,000 vector embeddings.
Fast local setup
Install, index data, and run queries in minutes with clear docs, quickstarts, and code samples.
