Local-first deployment
Run vector search where your application runs, including edge devices, on-premises systems, and disconnected environments. The product is designed to avoid cloud round-trips for latency-sensitive AI workloads.
VectorAI DB
VectorAI DB is Actian’s enterprise vector database for local-first AI search and retrieval. It supports RAG, semantic search, and AI deployments across edge, on-premises, hybrid, and cloud environments.
Overview
VectorAI DB is Actian’s enterprise vector database for AI applications that need to run locally rather than depend on a cloud service. It is positioned for semantic search, hybrid search, RAG pipelines, and AI agents that need low-latency retrieval across edge, on-premises, hybrid, and cloud environments.
The product page emphasizes portable deployment, predictable performance, and data control. Actian says VectorAI DB can run offline and sync when connected, supports local-first retrieval, and is intended for workloads where cloud latency, third-party processing, or connectivity assumptions are a problem.
Core capabilities
High-throughput retrieval
The homepage cites 1k QPS at 10M vectors, 99% recall at scale, and 13 ms p99 latency. These figures are presented as production performance characteristics for real-time retrieval.
Semantic and hybrid search
Actian describes support for semantic and hybrid search close to the data, which fits RAG pipelines and AI agents that need low-latency retrieval from local data sources.
Embedding-model flexibility
The pricing and FAQ content state that VectorAI DB is model-agnostic and can work with embeddings from OpenAI, Anthropic, Cohere, Hugging Face, and custom models.
ANN indexing support
The pricing page lists HNSW under ANN indexing methods, and the product page says the database supports modern ANN indexing for low-latency, high-accuracy search at scale.
Multiple deployment tiers
Pricing information shows support for Community, Starter, Growth, Enterprise, and Edge editions, with deployment paths that include local dev machines, servers, VMs, enterprise infrastructure, and devices or embedded systems.
Common use cases
RAG and semantic search
Build retrieval systems that need fast, predictable access to local data without sending queries to a cloud service. This fits chat, search, and agent workflows that depend on low-latency vector retrieval.
Edge and embedded AI
Run AI applications on edge devices or embedded hardware where connectivity is unreliable or unavailable. The product page specifically references devices such as NVIDIA Jetson and Raspberry Pi.
Healthcare data search
Keep patient or regulated data on-premises while enabling AI-assisted search and decision support. Actian highlights healthcare deployments in hospital data centers, clinic servers, and research facilities.
Manufacturing and industrial environments
Support factory, plant-floor, or other disconnected industrial systems that need vector search without assuming internet access. The site cites predictive maintenance, quality inspection, and production optimization.
Distributed platform deployments
Manage vector search across distributed sites such as retail branches or multi-region infrastructure. The pricing page positions the product for hybrid environments and multi-site deployments.
Pros and Cons
Pros
- Designed for local-first and on-premises AI deployments, including edge and disconnected environments.
- Supports semantic and hybrid search close to the data, which suits RAG and agent workflows.
- Published product materials describe low-latency and high-throughput retrieval at scale.
- Model-agnostic embedding support broadens fit across commercial and open-source model stacks.
- Pricing materials show a free Community Edition and a 30-day full-featured trial for evaluation.
Cons
- The public materials provide limited detail on APIs, integrations, and operational tooling beyond supported SDK languages and deployment targets.
- Some capability claims on the homepage are presented as performance highlights rather than full benchmark methodology, so buyers may want to validate them in their own environment.
- Pricing is tiered and includes sales-assisted options for enterprise and edge use, so not every deployment path appears to be self-serve.
FAQ
What is VectorAI DB used for?
VectorAI DB is a portable, local-first vector database for AI systems that run outside a cloud-only architecture. It is designed for semantic and hybrid search close to the data, including edge, on-premises, hybrid, and cloud deployments.
Who is VectorAI DB for?
VectorAI DB is positioned for edge AI engineers, manufacturing teams, healthcare organizations, and platform engineers that need vector search in local, on-premises, disconnected, or distributed environments.
Is there a free version or trial?
The pricing page says VectorAI DB offers a free Community Edition, a 30-day full-featured trial that starts when you sign up, and paid tiers for production use, higher capacity, and commercial redistribution. It also states that enterprise and edge options are available through sales.
What kinds of embeddings and indexing does it support?
Yes. The product FAQ says VectorAI DB supports modern ANN indexing methods, including HNSW, and is model-agnostic, so it can work with embeddings from providers such as OpenAI, Anthropic, Cohere, Hugging Face, and custom or fine-tuned models.
Where can VectorAI DB be deployed?
The pricing page says VectorAI DB supports local development machines, smaller and larger servers or VMs, enterprise infrastructure, and embedded or edge deployments. It also notes support for self-managed cloud, bare metal, on-prem infrastructure, and devices or embedded systems.
Quick Facts
- Category
- Vector database
- Primary use
- RAG, semantic search, and AI agents
- Deployment
- Edge, on-premises, hybrid, cloud, and embedded environments
- Pricing path
- Free Community Edition, 30-day trial, and paid tiers
- Vendor
- Actian
- Source domain
- actian.com
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