Data for AI

AI tools, including chatbots and agents, have different data requirements than traditional analytics products, such as reports and dashboards. AI applications require far more data to deliver accurate, trustworthy outputs than reports and dashboards that humans can pore over. In addition, they need to be fed data at exponentially higher speeds than data products to remain relevant.

As a result, AI tools necessitate sources that can deliver fresh, relevant data at high volume.

Vector databases were first developed in the early 2000s but remained a niche until OpenAI's November 2022 launch of ChatGPT sparked exploding interest among enterprises in AI development.

Vector indexing enables the discovery and operationalization of unstructured data, which makes up most data and can help provide the data volume AI tools need. In addition, vector databases enable similarity searches, which further help the data discovery process, and they can process data, including streaming data, at high speeds to ensure that the data sent to AI applications is up to date.

Given their value to AI pipelines, vendors including AWS, Databricks, Dremio, Google Cloud, Microsoft, MongoDB, Oracle and Snowflake all added vector search and storage capabilities in 2023 and 2024. More recently, Teradata added vector indexing capabilities.

Actian's vector database, first unveiled in January, is designed to feed AI pipelines with high volumes of relevant data and enable organizations with specific requirements to have greater control over their AI systems than when using cloud-based data sources.

Following its initial introduction, VectorAI DB was refined by improving performance and consistency at scale, expanding deployment options and strengthening the experience for developers, according to Emma McGrattan, Actian's chief technology officer.

Customer feedback and market trends, meanwhile, helped drive Actian to develop the database, she continued, noting that the vendor chose not to rush a vector database into production when interest in their capabilities spiked. Instead, it opted to wait until enterprises were better prepared to deploy AI at scale.

"The impetus for developing Actian VectorAI DB was the convergence of a market shift, customer pressure and a clear architectural gap," McGrattan said. "Enterprises are no longer asking if AI works. They are asking whether it can run where their data resides and under the constraints they operate in."

Potential scenarios that could necessitate users embedding Actian VectorAI DB in AI applications rather than deploying a cloud-based vector database include the following:

• Healthcare organizations dealing with patient data across a network of hospitals, clinics and research laboratories that need to make sure such data remains private.
• Government agencies managing sensitive information or classified data that needs to remain under full control at all times to eliminate exposure risks.
• Financial institutions that need to remain compliant with regulations in the different locales they operate while still running fraud detection and risk management analysis.
• Manufacturers embedding AI in machines and IoT sensors.

Stephen Catanzano, an analyst at Omdia, a division of Informa TechTarget, like Aslett, noted that portable vector databases are valuable given their flexibility.

"I view the portability of a vector database as significant because it allows seamless deployment across diverse environments … without requiring re-architecting," he said. "This flexibility is crucial for scenarios where cloud-based databases fall short, such as in regulated industries, disconnected environments or latency-sensitive applications."

Consequently, Actian's new database is a significant addition for the vendor's customers, Catanzano continued.

"Actian VectorAI DB is an important development for Actian users as it addresses critical gaps in deploying AI in regulated and edge environments," he said. "It enables users to perform high-performance vector searches locally, ensuring low latency and compliance with stringent data sovereignty requirements. This capability was previously challenging with Actian's existing offerings."

Competitive standing

While valuable to Actian customers, VectorAI DB's launch comes long after many of the vendor's competitors released their own vector database tools. More than 80% of the vendors assessed in ISG's annual Buyers Guide for Data Platforms provide robust vector storage and RAG capabilities, according to Aslett.

However, there remains room for vendors to distinguish themselves by supporting hybrid vector and text indexing and offering varied deployment options, he continued.

"Actian's approach of taking vector database functionality to embedded systems and edge devices is a differentiator," Aslett said.

Catanzano likewise noted that Actian's new vector database is distinct from those that were released shortly after ChatGPT's launch.

"Actian's VectorAI DB appears to go beyond merely catching up," he said. "Its focus on portability, compliance and performance in edge and hybrid environments differentiates it from many existing solutions. By addressing real-world challenges like data residency, security and scalability, Actian demonstrates innovation rather than simply following the trend."

Next steps

Following VectorAI DB's launch, Actian will continue to focus on adding foundational AI capabilities, including providing functionality that reduces the time it takes to set up AI workloads -- which can take days -- and offering further deployment flexibility, according to McGrattan.

"Actian VectorAI DB is not a standalone story," she said. "It connects to how we support agent development, database automation and AI-ready data across the operational and analytical data planes. The goal is to give customers a cohesive foundation for building and running agentic systems rather than a collection of disconnected tools."

Aslett noted that Actian has reinvigorated its platform capabilities with the addition of the Actian Data Intelligence Platform, along with other capabilities such as data observability. Meanwhile, its ongoing focus on fostering AI development is appropriate, he continued.

"The recent launch of MCP Servers and VectorAI DB demonstrate that it is delivering on its roadmap to address evolving data platform requirements driven by generative and agentic AI," Aslett said.

Catanzano, meanwhile, suggested that Actian could expand its offering by integrating with AI frameworks, improving its developer tools to further enable AI development and introducing managed services for hybrid data environments.

"These steps could help attract new users while deepening its value proposition for existing customers, particularly in industries with complex compliance and performance needs," he said.

Eric Avidon is a senior news writer for Informa TechTarget and a journalist with more than three decades of experience. He covers analytics and data management.