Vector databases in Azure: 6 service options and how to get started

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.

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 NoSQL

Azure Cosmos DB for NoSQL is a globally distributed, multi-model database service designed for large-scale applications. It offers low latency, high availability, and automatic scaling, making it a versatile choice for applications requiring real-time data access. Cosmos DB supports the storage and retrieval of vector embeddings, enabling efficient similarity searches. It simplifies managing NoSQL databases with its fully managed environment and integrates with other Azure services.

The service supports multiple data models, including document, key-value, graph, and wide-column. Cosmos DB’s global distribution capabilities ensure data is available wherever your users are, providing a consistent and responsive experience. Its integration with Azure’s suite of AI and machine learning tools makes it an option for vector database needs.

3. 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.

4. 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.

5. 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.

6. Azure SQL Database

Azure SQL Database is a fully managed platform as a service (PaaS) relational database, optimized for modern application development. While traditionally a relational database, Azure SQL Database supports extensions that can handle vector embeddings and similarity searches. This makes it useful for applications requiring querying capabilities alongside traditional relational data management.

Azure SQL Database offers features like intelligent performance, security, and high availability. These capabilities ensure that applications can scale and maintain data integrity and security. The integration with Azure’s ecosystem allows for easy use of additional services like AI and analytics, enabling data-driven solutions that incorporate both structured and vector data.

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:

1. 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.
2. 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.
3. 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.
4. Exploit data locality: When using globally distributed databases like 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.
5. 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.

Best practices for getting started with vector databases on Azure

1. Choosing the Right Vector Database

When selecting a vector database on Azure, consider the specific needs and constraints of your application. Here are some key factors to guide your decision:

• Application type: For applications that require scalability and global distribution, such as eCommerce platforms or social networks, Azure Cosmos DB for NoSQL is ideal. It provides low latency and multi-region writes, which ensure fast and reliable data access globally. If your application demands both relational data and vector capabilities, Azure Cosmos DB for PostgreSQL combines PostgreSQL’s relational database features with Cosmos DB’s scalability and resilience.
• Search capabilities: For applications that rely heavily on search functionalities, Azure AI Search is an optimal choice. It supports complex queries, natural language processing, and integrates AI models to enhance search accuracy and relevance. This is particularly useful for applications like recommendation systems and personalized content delivery.
• Existing infrastructure: If your current infrastructure includes MongoDB or PostgreSQL, leveraging Azure Cosmos DB for MongoDB or Azure Database for PostgreSQL allows for a transition with minimal code changes. This can significantly reduce migration time and costs while enabling your applications to benefit from Azure’s vector search capabilities.
• Performance and cost: Evaluate the performance requirements and cost constraints of your application. Services like Azure SQL Database, while traditionally relational, can be extended to handle vector embeddings and may be more cost-effective for smaller projects with less intensive vector search needs.

2. Setting Up Your Environment

To set up your environment for using a vector database on Azure, follow these steps:

• Provision resources: Use the Azure portal to create the necessary database resources. For example, if you’re using Azure Cosmos DB for NoSQL, configure the database account, containers, and throughput settings according to your expected workload.
• Network configuration: Set up your virtual networks to ensure secure communication between your application and the database. Configure firewall rules and private endpoints to restrict access to your database. This is crucial for protecting sensitive data and preventing unauthorized access.
• Resource group and region selection: Organize your resources into logical groups using Azure Resource Groups. Choose regions that are geographically close to your user base to minimize latency and improve performance. Consider setting up multiple regions for disaster recovery and high availability.
• Environment automation: Use Azure Resource Manager (ARM) templates or Terraform scripts to automate the deployment and configuration of your environment. This ensures consistency and allows you to quickly replicate environments for development, testing, and production.

3. Scale Your Vector Database

Scaling your vector database on Azure involves both vertical and horizontal scaling strategies:

• Vertical scaling: Increase the computational resources (CPU, memory) of your database instance to handle higher loads. This can be done through the Azure portal or programmatically using the Azure CLI or SDKs. Monitor performance metrics to determine when vertical scaling is necessary.
• Horizontal scaling: Distribute your data across multiple nodes or partitions to enhance performance and reliability. Azure Cosmos DB supports automatic partitioning, which allows you to scale out by adding more partitions as your data grows. Ensure that your partitioning strategy is based on a key that evenly distributes the data to avoid hotspots.
• Elastic scaling: Use the auto-scaling capabilities provided by Azure services to automatically adjust resources based on demand. Configure scaling policies to increase or decrease throughput in response to real-time traffic patterns. This helps optimize costs by only using resources when needed.
• Load balancing: Implement load balancing strategies to distribute traffic evenly across database instances. Azure provides built-in load balancing solutions, such as Azure Load Balancer and Traffic Manager, to ensure high availability and resilience.
• Performance tuning: Regularly review and optimize your database configuration and queries. Utilize indexing, caching, and query optimization techniques to maintain high performance as your database scales. Perform periodic maintenance tasks such as index rebuilding and data compaction to ensure optimal performance.

4. Authentication and Authorization

Securing your vector database involves authentication and authorization mechanisms:

• Azure Active Directory (AAD): Use AAD for managing user access to your vector database. It provides a centralized identity management solution that supports multi-factor authentication and conditional access policies.
• Role-based access control (RBAC): Define and assign roles to users and applications based on their access needs. Azure RBAC allows you to grant granular permissions, ensuring that users only have access to the resources they need. Regularly review and update RBAC policies to reflect changes in your team and application requirements.
• Managed identities: Use managed identities for your Azure resources to handle authentication without the need for hard-coded credentials. This simplifies the management of secrets and improves security by eliminating the risk of credential leakage.
• Network security: Implement network security measures such as Virtual Network (VNet) service endpoints and Private Link to restrict access to your database. This ensures that traffic to your database only comes from trusted networks.
• Data encryption: Enable data encryption at rest and in transit to protect sensitive information. Azure provides built-in encryption features, such as Transparent Data Encryption (TDE) and TLS/SSL, to ensure that your data remains secure.

5. Index Optimization

Optimizing indexes is critical for enhancing query performance in vector databases:

• Choosing the right index type: Select the appropriate index type based on your query requirements. For spatial queries, use spatial indexes; for text-based searches, use inverted indexes. For vector similarity searches, use hash-based or tree-based indexes (e.g., Annoy, Faiss).
• Index maintenance: Regularly update and rebuild indexes to reflect changes in your data. Stale indexes can lead to suboptimal query performance. Schedule index maintenance tasks during off-peak hours to minimize the impact on your application.
• Indexing strategies: Implement indexing strategies that align with your query patterns. For example, if your application frequently searches for similar items based on vector embeddings, create composite indexes that combine vector fields with other relevant attributes.
• Monitoring index performance: Use Azure Monitor to track index performance metrics such as query execution time and index utilization. Identify and address any performance bottlenecks by adjusting your indexing strategy or configuration.
• Query optimization: Optimize your queries to leverage indexes effectively. Use query hints and execution plans to understand how indexes are being used and make necessary adjustments. Avoid full table scans by ensuring that your queries are index-friendly.

Instaclustr: A managed alternative for vector databases beyond the Azure Cloud

While running a vector database in an Azure cloud environment can offer numerous benefits, such as scalability, reliability, and integration with other Azure services, some organizations may seek alternatives for various reasons. Instaclustr provides a compelling alternative by offering fully managed services beyond Azure, featuring:

• Expertise in Managing Vector Databases: Specializing in open-source databases such as Apache Cassandra and Apache Kafka—widely recognized as vector databases—Instaclustr’s team of seasoned database administrators and engineers ensures your vector databases operate with optimal performance, scalability, and reliability.
• Offloading Database Management Tasks: Instaclustr allows you to concentrate on your core business objectives while they handle the intricacies of database management. From provisioning and configuration to monitoring, maintenance, and troubleshooting, they manage it all seamlessly.
• Flexible Deployment Options: Not confined to a single cloud environment, Instaclustr offers the flexibility to diversify cloud providers or meet specific platform requirements. Their multi-cloud and hybrid cloud solutions empower you to run your vector databases according to your unique needs.
• Advanced Features: Instaclustr goes beyond basic database management by providing advanced functionalities such as automated backups, disaster recovery, and scalability options. These features guarantee high availability and ensure your vector database can scale effortlessly

In summary, Instaclustr provides a comprehensive and flexible solution for managing vector databases. Their expertise, flexibility, and advanced features empower you to leverage the benefits of a fully managed solution while maintaining control over your cloud environment.

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:

• Vector Search in Apache Cassandra® 5.0
• Use Your Data in LLMs With the Vector Database You Already Have: The New Stack