What is a vector database?
A vector database is a type of database management system designed to handle and retrieve vector embeddings efficiently. Vector embeddings are numerical representations of data, capturing the semantic meaning of text, images, and other data types. Unlike traditional databases that rely on structured data formats, vector databases are optimized for similarity search tasks. They enable fast and accurate search queries over large datasets by leveraging indexing and retrieval algorithms.
These databases are crucial in applications involving machine learning and AI, such as recommendation systems, image recognition, and natural language processing. By storing data as vectors, these databases facilitate quick comparisons and nearest-neighbor searches, which are essential for these applications. The choice of a suitable vector database can significantly impact the performance and scalability of such systems.
This is part of a series of articles about vector databases
Vector database services on Azure
Microsoft Azure is a leading cloud provider, offering numerous data management services. Here are the primary Azure solutions that can be used for vector database applications:
1. Azure AI Search
Azure AI Search is a fully managed search service that supports building rich search experiences. It integrates with AI capabilities to enhance search results using natural language processing and machine learning models. This service includes vector search capabilities, allowing it to index and search through large datasets of vector embeddings efficiently. Azure AI Search supports features, such as faceting, filtering, and sorting.
Azure AI Search leverages high performance indexing techniques, which ensure that the most relevant results are retrieved quickly. These features make it suitable for search applications that require both traditional keyword-based searches and vector-based searching.
2. Azure Cosmos DB for MongoDB
Azure Cosmos DB for MongoDB offers a managed service for users familiar with MongoDB, providing compatibility with existing MongoDB applications and tools. This service ensures low-latency data access and high availability, essential for applications that require real-time data interaction. By supporting vector embeddings, Azure Cosmos DB for MongoDB enables similarity searches, crucial for modern AI-driven applications.
Furthermore, the service provides automatic scaling and global distribution capabilities. Existing MongoDB users can migrate to Azure Cosmos DB with minimal adjustments, leveraging the familiar MongoDB APIs. This makes it an excellent choice for organizations looking to integrate vector database capabilities into their MongoDB-based applications without significant overhead.
3. Azure Cosmos DB for PostgreSQL
Azure Cosmos DB for PostgreSQL, a managed PostgreSQL service, offers support for storing and querying vector embeddings, making it suitable for applications involving complex similarity searches. It provides low latency, high availability, and global distribution, ensuring efficient data access and interaction across different geographical locations.
The service also includes indexing techniques to speed up query performance, allowing for the quick retrieval of relevant data points. This integration makes it possible to leverage PostgreSQL’s data management features alongside Cosmos DB’s global scalability and resilience.
4. Azure Database for PostgreSQL
Azure Database for PostgreSQL is a fully managed relational database service based on the open-source PostgreSQL. It supports storing vector embeddings and performing efficient similarity searches, making it versatile for a variety of applications. This service features automatic scaling, high availability, and security measures, ensuring reliable and secure data operations.
The managed nature of this service means developers can focus on building applications without worrying about the underlying infrastructure. Additionally, it integrates with other Azure services, enabling solutions that span data storage, AI, and machine learning.
Multi-cloud options for vector database managed services on AWS
Navigating the world of open source vector databases available in offerings such as PostgreSQL, OpenSearch, ClickHouse and Cassandra becomes much simpler with managed service options. With a managed service, the ability to scale databases effortlessly as data grows, removes the worrying about the underlying hardware or complex configurations. This approach significantly reduces operational burden, eliminating the need for constant maintenance, updates, and troubleshooting. Choosing a managed service that spans multiple cloud providers, enables flexibility, cloud-portability and avoids vendor lock-in for a production-ready environment.
NetApp Instaclustr is one such option for organizations that require augmentation of their teams or fully managed services for open source technologies, including those that provide vector database capabilities. Instaclustr empowers organizations with world-class expertise for many popular open source technologies. Instaclustr includes services and support for pure open source PostgreSQL, OpenSearch, ClickHouse, and Cassandra providing the robust infrastructure needed to handle demanding vector workloads.
Related content: Read our guide to vector search
Vector database services on Instaclustr
1. Instaclustr for PostgreSQL
PostgreSQL is celebrated for its stability, flexibility, and strong community support. With the addition of extensions like pgvector, it transforms into a capable vector database, blending the familiarity of SQL with advanced search capabilities.
Instaclustr for PostgreSQL provides a fully managed solution that makes it easy to deploy and scale delivering the benefits of a robust relational database alongside the tools needed for vector similarity search. This is ideal for applications where vector data is closely tied to structured business data, allowing organizations to run complex queries that combine both.
2. Instaclustr for OpenSearch
When the primary need is lightning-fast search and real-time analytics, OpenSearch is a top contender. Originally designed for text search, its capabilities have expanded to include a powerful k-Nearest Neighbor (k-NN) search feature, making it an excellent choice for vector workloads.
Instaclustr for OpenSearch delivers a fully managed, production-ready cluster optimized for high-performance vector search. It’s ideal for applications that need to sift through millions of vectors in milliseconds, such as semantic search engines, product recommendation systems, and log analysis.
3. Instaclustr for ClickHouse
For applications dealing with massive datasets and requiring extreme analytical performance, ClickHouse is a phenomenal choice. This open source columnar database is built to process analytical queries at incredible speeds, and its vector search capabilities make it a strong option for large-scale AI workloads.
Instaclustr for ClickHouse provides a managed environment that harnesses this power without the administrative overhead. Its columnar storage format is highly efficient for storing and querying large volumes of numerical data, including vector embeddings. This makes it a great fit for use cases like large-scale anomaly detection, real-time analytics on streaming data, and complex business intelligence.
4. Instaclustr for Cassandra: Distributed scale and high availability
Apache Cassandra® is a master of distributed data management, renowned for its fault tolerance and linear scalability. When paired with vector search capabilities, it becomes an unstoppable force for global-scale applications that require constant uptime and low-latency performance.
Instaclustr for Cassandra offers a battle-tested, fully managed Cassandra solution that is ready for vector data needs. Integrating vector search functionality, enables the creation of AI applications on a database designed for massive scale and resilience. This is perfect for systems that need to serve vector searches across multiple geographic regions with no single point of failure.
Related content: Read our guide to vector database use cases
Ready to Experience the Instaclustr Advantage?
Whether you’re looking to transition from an Azure cloud environment or exploring vector database management options, Instaclustr provides a compelling solution. To learn more about vector search and Instaclustr:
Tips from the expert
Ritam Das
Solution Architect
Ritam Das is a trusted advisor with a proven track record in translating complex business problems into practical technology solutions, specializing in cloud computing and big data analytics.
In my experience, here are tips that can help you better leverage vector databases on Azure:
- Understand Your Objectives: Broadly speaking, vector searching has a limited set of optimal use-cases like recommendation systems, classification tasks, and AI chatbots (think RAG and semantic search). Understand what it is you’re trying to accomplish and move accordingly. You might use ANN algorithms for faster similarity searches, especially in large datasets as these algorithms can drastically reduce search time while maintaining accuracy. However, your choice of ANN algorithm will be use-case and data volume dependent. Familiarize yourself with the different Azure services as they will provide different native algorithms more suited to one use-case over another.
- Combine traditional and vector searches: Utilize a hybrid approach by combining vector-based searches with traditional keyword-based searches to improve the relevance and richness of search results. This can be particularly useful in applications like recommendation systems. Many traditional databases are adding in new data types to support vector searching. Simply add a column to your existing data model.
- Optimize vector dimensionality: Experiment with different dimensionalities for your vector embeddings to find the balance between accuracy and performance. Higher dimensions can capture more information but may also increase computation and storage costs.
- Exploit data locality: When using globally distributed databases like Apache Cassandra or Cosmos DB, strategically place data close to your primary user base to minimize latency. Use Cosmos DB’s multi-region write capabilities for high availability and low-latency access.
- Implement tiered storage solutions: Use tiered storage solutions to store frequently accessed vectors in faster, more expensive storage, and less frequently accessed vectors in cheaper, slower storage. Azure provides various storage options that can be integrated for this purpose.
