Actian VectorAI DB

VectorAI DB: High-Performance Vector Database for Edge and On-Premises AI

In the rapidly evolving landscape of artificial intelligence, the ability to process data where it is generated has become a critical competitive advantage. VectorAI DB is a specialized vector database built specifically for edge and on-premises environments. While traditional cloud-based vector databases often struggle with network latency and data residency issues, VectorAI DB runs where cloud databases can’t, ensuring reliable RAG (Retrieval-Augmented Generation) and semantic search capabilities on embedded devices, factory floors, and disconnected environments.

What’s VectorAI DB?

VectorAI DB is an enterprise-grade vector database solution developed by Actian, designed to empower developers and engineers to deploy AI applications in challenging environments. Trusted by 25 of the Fortune 100, VectorAI DB allows organizations to build once and deploy everywhere—from a Raspberry Pi to high-end enterprise edge servers.

Unlike standard cloud-only architectures, VectorAI DB addresses the specific needs of "edge cases" where internet connectivity is unreliable or non-existent. It provides the backbone for portable AI, enabling local search and data processing that meets the most stringent compliance and performance standards.

Overcoming the Limitations of Cloud Vector Databases

Many organizations find that cloud-centric architectures block the growth of their AI initiatives. VectorAI DB was engineered to solve three primary hurdles:

1. Network Latency: Cloud round-trips can add 200-400ms to every query. VectorAI DB eliminates this overhead, making it possible to build sub-100ms real-time applications.
2. Compliance Barriers: Regulated industries must adhere to HIPAA and GDPR. VectorAI DB keeps data on-premises, removing the risks associated with third-party cloud processing.
3. Connectivity Constraints: In air-gapped facilities or remote environments, cloud databases simply fail. VectorAI DB provides a consistent architecture that works offline and syncs when a connection is restored.

Key Features of VectorAI DB

VectorAI DB is built for performance that holds up in production environments, ensuring that your AI applications remain fast, accurate, and scalable.

High-Speed Performance at Scale

• 1.9K QPS at 10M Vectors: VectorAI DB is built for real-time AI applications that cannot afford to wait, handling high-volume query loads with ease.
• Sub-15ms Local Queries: By eliminating the need for network round-trips, VectorAI DB delivers p99 latency of just 13 milliseconds, providing consistent performance from prototype to full-scale production.

Accuracy Without Tradeoffs

• 99% Recall at Scale: As your dataset grows, VectorAI DB maintains high accuracy, ensuring that your semantic search results remain reliable regardless of data volume.

Versatile Deployment Options

• Embedded and Edge Optimized: Deploy VectorAI DB on resource-constrained devices like NVIDIA Jetson and Raspberry Pi, or on industrial edge servers.
• Air-Gapped and Disconnected Readiness: VectorAI DB thrives in facilities without internet access, providing full functionality in air-gapped or remote environments.

Developer-Centric Architecture

• Unified Architecture: Use the same architecture from prototype to production. VectorAI DB eliminates the need for environment-specific rewrites, allowing for a seamless transition from a small-scale test to a global enterprise deployment.
• Multi-Language Support: Build applications using your language of choice with the support of comprehensive documentation and resources.

Use Cases for VectorAI DB

VectorAI DB is utilized across various sectors to enable AI where traditional databases cannot reach.

Edge AI Engineers

Engineers building autonomous systems, robotics, and IoT applications require high-performance vector search on resource-constrained hardware. VectorAI DB can be deployed directly to:

• NVIDIA Jetson
• Raspberry Pi
• Industrial Edge Servers

Manufacturing Teams

In the manufacturing sector, VectorAI DB enables AI in disconnected factory environments. This is essential for:

• Predictive Maintenance: Analyzing sensor data locally to prevent machine failure.
• Quality Inspection: Running real-time computer vision models with local vector search.
• Production Optimization: Managing plant floor data in air-gapped facilities.

Healthcare Organizations

For healthcare providers, data privacy is paramount. VectorAI DB facilitates HIPAA-compliant AI by keeping patient data on-premises for:

• Clinical Decision Support: Assisting doctors with real-time data retrieval.
• Medical Imaging: Enhancing search and categorization of complex medical files.
• Secure Record Search: Ensuring data residency within hospital data centers or clinic servers.

Platform Engineers

Platform engineers managing distributed sites need a way to synchronize vector search across various locations. VectorAI DB supports:

• Hybrid Environments: Bridging the gap between the edge and the cloud.
• Multi-Site Infrastructure: Managing data across retail locations, branch offices, and multi-region deployments.

How to Use VectorAI DB

VectorAI DB is designed to take you from installation to production in a matter of minutes.

1. Sign Up & Install: Begin by signing up for access and exploring the documentation to understand the installation requirements for your specific hardware (e.g., Raspberry Pi, Jetson, or server).
2. Choose Your Language: VectorAI DB allows you to build apps using the programming languages you are already comfortable with.
3. Build and Prototype: Develop your application locally using the same architecture that will be used in production.
4. Deploy Locally: Deploy the database to your edge device or on-premises server.
5. Sync and Scale: If your environment is hybrid, utilize the sync-when-connected features to maintain data consistency between the edge and the data center.

"Your VectorDB runs where cloud databases can’t. Deploy reliable RAG and semantic search on embedded devices and disconnected environments."

FAQ

What is VectorAI DB? VectorAI DB is a specialized vector database designed by Actian for edge and on-premises use, providing high-performance search capabilities for AI applications in environments where the cloud is not an option.

How is VectorAI DB different from other vector databases like Qdrant and Milvus? Unlike many other vector databases that are cloud-first, VectorAI DB is optimized for resource-constrained edge devices and disconnected, air-gapped environments, prioritizing local low latency and data residency.

What indexing algorithms does VectorAI DB support? VectorAI DB supports high-performance indexing algorithms designed to maintain a 99% recall rate at scale. For specific technical details on current algorithms, please refer to the official documentation.

What embedding models does VectorAI DB support? VectorAI DB is designed to be flexible, allowing developers to work with a wide variety of embedding models suitable for their specific AI applications.

Does VectorAI DB support multi-modal embeddings? Yes, VectorAI DB is built to handle the complexities of modern AI, including multi-modal data requirements for advanced semantic search and RAG applications.