The vendor's update takes a three-pronged approach to storing and searching vectorized data to make building AI tools trained on massive amounts of relevant information faster.
Couchbase on Tuesday launched a new version of its database platform, highlighted by improved vector indexing and retrieval capabilities aimed at making it faster to develop enterprise-grade AI applications.
Agents, chatbots and other AI tools require high volumes of high-quality data to be accurate. Without data volume and quality, including near-real-time data that provides the most current information, such applications can't be trusted to inform business decisions.
To provide the requisite amount of data for AI development, and do so as quickly as possible, Couchbase 8.0 features a three-pronged approach to vector indexing and retrieval. Vectors are numerical representations of data, including unstructured data such as text and images that make up the vast majority of all data, enabling users to discover relevant data through similarity searches.
The latest version of Couchbase's database platform adds Hyperscale Vector Index to power searches larger than 1 billion vector index records, Composite Vector Index to discover relevant vectors for a particular application and Search Vector Index to enable searches across different data types.
Given that the update improves Couchbase's support for developing AI applications, it is a significant release, according to Devin Pratt, an analyst at IDC.
"Earlier versions offered basic vector search, but 8.0 introduces new indexing methods that allow developers to store and query billions of vectors … that help AI systems understand context and similarity," he said. "This enables organizations to manage transactional, analytical and AI data in one environment rather than relying on separate systems."
Based in San Jose, Calif., Couchbase is a NoSQL database vendor that competes with specialized companies such as MongoDB and Redis, along with tech giants such as AWS, Google, Microsoft and Oracle that offer database platforms.
Added database capabilities
Vector search and storage became critical database capabilities following OpenAI's November 2022 launch of ChatGPT, which significantly improved generative AI (GenAI) technology.
Earlier versions offered basic vector search, but 8.0 introduces new indexing methods that allow developers to store and query billions of vectors … that help AI systems understand context and similarity.
Devin PrattAnalyst, IDC
GenAI has the potential to make enterprise employees better informed and more efficient. As a result, enterprises quickly began increasing their investments in GenAI development following ChatGPT's launch.
In response to customers building GenAI tools -- which have now evolved to include agentic AI capabilities -- many data management vendors built environments to simplify combining proprietary data with large language models (LLMs) and other GenAI capabilities. For database specialists, that included adding or upgrading vector search and storage capabilities.
Couchbase first unveiled vector database capabilities in preview in February 2024 before making them generally available in May 2024. Couchbase's three-pronged approach to vector indexing and search aims to improve the vendor's previous vector database capabilities to enable customers to successfully develop AI tools.
Despite surging interest in AI development, an overwhelming percentage of AI projects never make it into production. Among the most significant factors is the lack of access to high-quality data.
Couchbase's latest database platform update is designed to simplify discovering relevant data and do so at enterprise scale. Combining Hyperscale vector Index, Composite Vector Index and Search Vendor Index, Couchbase 8.0 aims to enable developers and engineers to search billions of vectors in milliseconds to feed models and applications with high-quality, real-time data.
By reducing queries to milliseconds, Couchbase is adding valuable capabilities, according to Pratt.
"Speed directly impacts how useful and natural AI-powered systems feel to end users," Pratt said. "Even small delays can interrupt the flow of interaction or limit scalability. For enterprises, faster retrieval translates to more responsive digital experiences and higher productivity from AI-powered systems."
Meanwhile, Couchbase's three-pronged approach to vector search and storage could help differentiate the vendor from its peers, he continued.
"Couchbase's design stands out because it brings vector search, traditional database operations and mobile data together in one platform," Pratt said. "Instead of relying on multiple databases for transactions, analytics and AI, users can manage all three within a single environment. … This integrated design reduces complexity [and] improves performance consistency."
Matt Aslett, an analyst at ISG Software Research, similarly noted the value of Couchbase 8.0 for current customers while pointing out that the speed it enables is a way for the vendor to stand out in a competitive market.
"Support for storing and indexing vectors has become a table stakes feature, but data platform providers are able to differentiate by providing high-performance retrieval to meet the demand of real-time inference as well as multiple indexing approaches that can be matched to different application requirements and architectural approaches," he said.
Couchbase first released vector search and storage capabilities to address customer needs, according to Matt McDonough, the vendor's senior vice president of product.
Since then, he noted that Couchbase has observed challenges relating to the scale and complexity of vector searches, leading to the additions of Hyperscale Vector Index and Composite Vector Index. Meanwhile, the vendor's emphasis on speed is aimed at meeting the needs of users who expect instant results.
"When you're dealing with AI applications in production at scale, those milliseconds add up fast and become the difference between a system users will actually adopt versus one they abandon," McDonough said.
Beyond improved vector indexing and search capabilities, Couchbase's latest database platform update includes new security and compliance tools.
Lack of confidence around security and compliance is another factor contributing to the failure rate of AI projects. To mitigate such concerns, Couchbase 8.0 supports native data at rest encryption (DARE) to automatically encrypt data when it's first stored to protect sensitive information from unauthorized access.
In addition, the update adds cross data center replication to ensure that non-sensitive data is properly replicated across regions and intelligent auto-failover capabilities to maintain operations during system failures.
"Couchbase 8.0 is an important update of the company's data platform, facilitating support for AI workloads through a combination of indexing and retrieval enhancements as well as replication and security capabilities," Aslett said.
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Looking ahead
As Couchbase plots its roadmap, the database vendor's focus is on continued improvement to help customers build AI applications that scale and act as intended, according to McDonough.
Specific plans include investing in its core server platform, improving security, extending AI to edge devices, forming partnerships that add AI capabilities and continuing to enhance vector search and storage.
"Our focus is to be the leading developer-focused cloud-to-edge data platform, empowering enterprises to build and operate AI-driven applications with scalability, performance and reliability," McDonough said.
Continuing to add features that better enable customers to create cutting-edge applications, according to Pratt.
"Couchbase is on the right path," he said. "Its focus on unifying data management and AI capabilities puts it in a strong position, and future updates will likely make building and managing intelligent and agentic systems even easier for enterprises."
Eric Avidon is a senior news writer for Informa TechTarget and a journalist with more than 25 years of experience. He covers analytics and data management.
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