1
Milvus
Milvus is an open-source vector database designed for massive-scale embedding vectors, offering high scalability and GPU acceleration for search operations.
AI Tool
Actian VectorAI DB Review
Actian VectorAI DB is an enterprise vector database designed for edge and on-premises deployment, enabling retrieval-augmented generation (RAG) and semantic search without cloud latency or per-query fees.
shipped Apr 29, 2026aipaid
1Actian VectorAI DB was officially launched on April 28, 2026, at the AI Dev 26 x SF conference.
2In VDBBench testing, it achieved 745.2 queries/second, outperforming Milvus and Qdrant Local by over 22 times in throughput.
3The database supports deployment configurations aligned with HIPAA, ISO/IEC 27001:2022, and SOC 2 Type II compliance standards.
4It offers a free 30-day trial and a free Community Edition for developers, supporting up to 5,000 vectors.
Actian VectorAI DB at a Glance
About Actian VectorAI DB
Pricing Plans
Standard
Contact for pricing / annual
- • Enterprise-grade performance
- • On-premises deployment
- • No per-query fees
- • Data privacy and security
Leadership
Investors
HCL Technologies, Sumeru Equity Partners
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2
Qdrant
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3
Weaviate
Weaviate is an open-source, AI-native vector database featuring a graph-like class schema, robust hybrid search capabilities (combining vector similarity with keywords), and a modular design.
4
Oracle AI Vector Search
Oracle AI Vector Search integrates vector data types, indexes, and SQL operators natively within Oracle Database 23ai, enabling RAG and semantic search directly on private business data.
Connect
𝕏
X / Twitter@actiancorp
overview
What is Actian VectorAI DB?
Actian VectorAI DB is a vector database tool developed by Actian that enables enterprises in regulated, disconnected, and edge environments to run retrieval-augmented generation (RAG) and semantic search without cloud latency, per-query fees, or data exposure. It is a portable vector database for production AI in regulated, disconnected, and edge environments, delivering low-latency vector search. The tool stores, indexes, and manages dense vector representations of unstructured data, including text, images, audio, and video, facilitating AI systems to retrieve information based on semantic meaning and context. This capability is crucial for applications requiring fast, local-first vector search across embedded systems, edge devices like NVIDIA Jetson and Raspberry Pi, on-premises infrastructure, and cloud deployments.
quick facts
Quick Facts
| Attribute | Value |
|---|---|
| Developer | Actian |
| Business Model | subscription-saas |
| Pricing | Free Community Edition (up to 5,000 vectors), paid tiers (contact for pricing) |
| Platforms | Web, On-premises |
| HQ | Boulder, Colorado, USA |
| Funding | Acquired (by HCL Technologies, Sumeru Equity Partners) |
features
Key Features of Actian VectorAI DB
Actian VectorAI DB provides a suite of features designed for enterprise AI workloads, particularly in environments with strict data governance and performance requirements. Its architecture supports efficient data management and retrieval for AI applications across diverse deployment scenarios.
- 1Fast columnar analytics for efficient data processing.
- 2Edge and on-premises deployment capabilities, enabling local data processing.
- 3Elimination of cloud latency for real-time AI applications.
- 4Ensured data privacy through local data ownership and deployment.
- 5Flexible deployment environments, including embedded systems and air-gapped facilities.
- 6Retrieval-augmented generation (RAG) support for grounding LLMs in proprietary data.
- 7Semantic search capabilities for context-aware information retrieval.
- 8Low-latency vector search, critical for real-time AI assistants and agents.
- 9Compliance alignment with HIPAA, ISO/IEC 27001:2022, and SOC 2 Type II standards.
- 10No training on user data, ensuring data confidentiality.
use cases
Who Should Use Actian VectorAI DB?
Actian VectorAI DB is primarily targeted at developers and enterprises operating in regulated, disconnected, and edge computing environments. Its design addresses specific challenges faced by organizations requiring secure, low-latency AI capabilities outside of traditional cloud-native paradigms.
- 1**Enterprises in regulated industries**: Organizations in manufacturing, healthcare, financial services, and government agencies requiring stringent data sovereignty and compliance (e.g., HIPAA, ISO 27001, SOC 2) for AI applications.
- 2**Developers building AI for edge and disconnected environments**: Teams deploying AI on embedded devices, edge servers, or in air-gapped facilities where cloud connectivity is limited or undesirable, such as for real-time anomaly detection in industrial IoT.
- 3**Organizations implementing RAG pipelines**: Enterprises seeking to ground large language models (LLMs) in proprietary, sensitive data for AI assistants and agents, ensuring contextual accuracy and data privacy.
- 4**Teams requiring fast semantic retrieval**: Applications demanding low-latency vector search for recommendation systems, multimodal search, or real-time contextual information retrieval.
- 5**Companies avoiding cloud vendor lock-in**: Businesses prioritizing data ownership and predictable performance without incurring per-query fees or relying solely on cloud infrastructure for their vector database needs.
pricing
Actian VectorAI DB Pricing & Plans
Actian VectorAI DB operates on a paid model, offering a free entry point for developers and structured tiers for enterprise use. A free 30-day trial is available for evaluation. The Community Edition provides a perpetual free option for developers, supporting up to 5,000 vectors. Specific pricing for enterprise versions is not publicly disclosed and requires direct contact with Actian sales for a quote, tailored to deployment scale and feature requirements.
- 1Free 30-day trial: Full access for evaluation.
- 2Community Edition: Free, supports up to 5,000 vectors.
- 3Standard: Contact for pricing (annual subscription).
competitors
Actian VectorAI DB vs Competitors
Actian VectorAI DB differentiates itself in the vector database market through its emphasis on portability, local-first deployment, and predictable performance in regulated and edge environments. While many competitors focus on cloud-native or open-source solutions, Actian VectorAI DB targets scenarios where data residency, low latency, and compliance are paramount.
1
Milvus is an open-source vector database designed for massive-scale embedding vectors, offering high scalability and GPU acceleration for search operations.
Milvus can be self-hosted on-premises, aligning with Actian VectorAI DB's focus on local and edge deployments, and also offers a managed cloud service (Zilliz Cloud). It targets enterprise-grade RAG and semantic search applications requiring high performance and scalability for billions of vectors.
2
Qdrant is an open-source vector database written in Rust, known for its real-time embedding search capabilities, rich JSON-based payload filtering, and strong API stability for local-to-production parity.
Similar to Actian VectorAI DB, Qdrant supports self-hosting via Docker for on-premise deployments and also provides a managed cloud option. Its emphasis on real-time search and consistent API behavior makes it suitable for production AI workloads in various environments.
3
Weaviate is an open-source, AI-native vector database featuring a graph-like class schema, robust hybrid search capabilities (combining vector similarity with keywords), and a modular design.
Weaviate offers self-hosted deployment options, providing an on-premise alternative to Actian VectorAI DB. Its comprehensive feature set, including hybrid search and an AI-first design, caters to complex RAG and semantic search applications with a focus on flexibility and rich data modeling.
4
Oracle AI Vector Search↗
Oracle AI Vector Search integrates vector data types, indexes, and SQL operators natively within Oracle Database 23ai, enabling RAG and semantic search directly on private business data.
While Actian VectorAI DB is a dedicated vector database, Oracle AI Vector Search is a feature within an established enterprise database, appealing to organizations already using Oracle for on-premise data management. It provides a solution for data sovereignty and RAG on private data within existing Oracle infrastructure.
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Frequently Asked Questions
+What is Actian VectorAI DB?
Actian VectorAI DB is a vector database tool developed by Actian that enables enterprises in regulated, disconnected, and edge environments to run retrieval-augmented generation (RAG) and semantic search without cloud latency, per-query fees, or data exposure. It is a portable vector database for production AI in regulated, disconnected, and edge environments, delivering low-latency vector search.
+Is Actian VectorAI DB free?
Actian VectorAI DB offers a free 30-day trial for evaluation and a free Community Edition that supports up to 5,000 vectors for developers. Enterprise pricing for additional versions is available upon contact with Actian sales.
+What are the main features of Actian VectorAI DB?
Key features of Actian VectorAI DB include fast columnar analytics, edge and on-premises deployment, no cloud latency, data privacy, flexible deployment environments, retrieval-augmented generation (RAG) support, semantic search capabilities, and low-latency vector search. It also supports compliance with HIPAA, ISO/IEC 27001:2022, and SOC 2 Type II standards.
+Who should use Actian VectorAI DB?
Actian VectorAI DB is designed for developers and enterprises in regulated industries (e.g., manufacturing, healthcare, financial services, government agencies), those deploying AI in disconnected or edge environments, organizations implementing RAG pipelines with proprietary data, and teams requiring fast semantic retrieval for AI applications like anomaly detection and recommendation systems.
+How does Actian VectorAI DB compare to alternatives?
Actian VectorAI DB differentiates itself by focusing on portability and local-first deployment for regulated and edge environments, offering predictable performance without cloud latency or per-query fees. In VDBBench testing, it achieved 745.2 queries/second, outperforming open-source alternatives like Milvus and Qdrant Local by over 22 times in throughput. Unlike cloud-native solutions, it prioritizes data ownership and consistent performance across diverse infrastructure, contrasting with dedicated cloud services or features within existing databases like Oracle AI Vector Search.
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