The only vector database with real-time indexing, hybrid search, and a familiar Data API—built for AI leaders scaling from POC to production with 95%+ accuracy.
Astra DB delivers advanced filtering, re-ranking, and real-time vector search, ensuring more relevant AI responses and minimizing hallucinations for high-stakes AI applications.
Scale AI applications seamlessly across regions with real-time indexing, automated scaling, and cross-region replication—built on the same technology that powers Netflix and the largest AI-driven platforms.
Accelerate development with a MongoDB-compatible API that supports vector and non-vector data—eliminating the need for multiple databases while enabling hybrid search and structured data filtering.
Developers build, test, and deploy AI applications faster with the Langflow AI app builder and largest agent ecosystem, for seamless retrieval-augmented generation and GenAI workflows.
Enhance your AI/ML applications and ecosystem with contextual data insights and automations.
Vector databases like DataStax Astra DB (built on Apache Cassandra) are designed to provide optimized storage and data access capabilities specifically for vector embeddings, which is the mathematical representation of data. Vector databases provide multi-dimensional representation of structured and unstructured data and enable functions like vector search on large corpora of data.
Vector search associates similar mathematical representations of data, and vector representations, converting queries into the same vector representation. With both query and data represented as vectors, finding related data becomes a function of searching for any data representations that are the closest to your query representation, known as nearest neighbors. Vector databases provide the storage and retrieval of data representations for vector search called vector embeddings. Since data is represented across multiple dimensions, vector databases need to be highly scalable and highly performant.
The concept of nearest neighbor is at the core of how vector search works and there are a number of different algorithms that can be used for finding nearest neighbors depending on how much compute resources you want to allocate and/or how accurate you are looking for your result to be.
Vector search doesn’t have a concept of where the data is stored so can be used for cloud-based or on-premise data environments. Solutions like Astra DB are built to provide a cloud -native data platform ideally suited for building generative AI applications powered by vector search, however, on-premise solutions like DataStax Enterprise (DSE) are also being used for vector search capabilities.
Cloud-based solutions tend to be more commonly deployed as they provide the scalability for additional storage and compute resources on demand depending on the application's requirements.
Vector search is not limited by a specific industry and can be leveraged by use cases across all industries. Building recommendation engines using vector search offers improved customer engagement and visibility. Vector search can also be used to build natural language processing chatbots that interact with product documentation in real time to provide the right answer at the right time.
Vector search is the latest approach to data organization and access and allows applications the ability to leverage generative AI across all industries.
Vector search can be used on small, medium, or large data sets interchangeably. However, the important thing to remember is that with small datasets a lot of the compute and storage overhead can be maintained in the application space. For medium to large datasets applications, you should leverage a high-performance vector database like Astra DB, allowing for the decoupling of data storage from the application. This allows for applications to reuse and leverage vector data across multiple application instances and frees up resources in the application.
One of the primary differences between DataStax vector search and other offerings in the market is that DataStax Astra DB is built on Apache Cassandra, which for over 15 years has been used to provide a highly scalable, highly performant approach to unstructured data storage and retrieval via NoSQL functionality. Most of the solutions in the market today are single-solution approaches to providing vector databases for vector storage. DataStax provides a proven/hardened solution to handling the massive scalable and performance demands generative AI applications need.
In addition, while many solutions are available for vector search, DataStax Astra provides a completely integrated platform for building generative AI applications. More than just a vector database, more than just vector search, DataStax Astra provides the ability to leverage orchestration frameworks like LlamaIndex and LangChain to simplify the generative AI application development and enable end-to-end vector lifecycle management.
Astra DB supports hybrid search (vector + metadata), enables real-time indexing while ingesting, and eliminates the need for a separate metadata database—unlike Pinecone, which requires an additional database for filtering and ranking.
Astra DB enables simultaneous query and update operations, ensuring your AI models access the most relevant data without delays from re-indexing.
Astra DB provides a familiar Data API that supports vector and non-vector data, making it easy to build AI applications like RAG, agentic AI, and LLM-powered search—all without requiring multiple databases.
Langflow is an AI app builder that makes it dramatically easier to build agentic AI and RAG applications with drag-and-drop development, rapid testing and iteration, and reusable components to connect to any model, API or data source.
Astra DB offers enterprise-grade security features, including encryption, role-based access control, and compliance certifications. These protections ensure that vector search applications meet industry standards for data privacy and integrity in production environments.
Traditional keyword search relies on exact matches between text queries and indexed documents, whereas vector search leverages numerical representations to find semantically similar results. This makes vector search more effective for applications like AI-powered recommendations and natural language understanding.
Vector search enhances RAG applications by enabling fast and accurate retrieval of relevant data from large corpora. By converting both queries and documents into vector embeddings, AI models can generate responses that are more contextually relevant and informed by real-time data.
Yes, vector search supports multimodal applications by representing different types of data—such as text, images, and audio—as vectors in a shared high-dimensional space. This enables advanced use cases like cross-modal retrieval, image-based search, and AI-powered video indexing.
Optimizing vector search performance involves selecting efficient indexing techniques, balancing accuracy with computational efficiency, and leveraging approximate nearest neighbor (ANN) algorithms. Astra DB provides built-in optimizations for handling large-scale, high-dimensional data efficiently.
