Database Selection for Engineering Projects

*When I wrote the comparison in 2023 the database landscape underwent significant changes due to AI integration. This is my updated evaluation of these platforms for 2025.*

Choosing appropriate database solutions continues to be vital for engineering projects to achieve success as AI integration becomes mandatory. This article compares four database systems which include SQL IaaS and Azure SQL Database and Cosmos DB and PostgreSQL on Azure. The evaluation of these platforms focuses on their main benefits including AI features to assist you in selecting proper architectural solutions for contemporary engineering work.

 SQL IaaS

The database platform SQL IaaS (Infrastructure as a Service) runs SQL Server instances on Azure virtual machines while offering SQL Server environments with cloud infrastructure advantages. SQL Server 2025 is currently available in public preview for this option which comes with advanced artificial intelligence features. The main benefits of using SQL IaaS include:

• The system allows engineers to customize their database configurations as well as operating system settings for specific project demands.
• SQL Server databases can easily transition to cloud infrastructure using a direct migration path.
• Users have the capability to adapt computing and storage capabilities according to their workload requirements.
• The system features built-in vector search and LLM connectivity through T-SQL for native AI integration.
• The system allows users to create their own settings while utilizing third-party tools that need operating system-level access.

Azure SQL Database

The Azure SQL Database functions as a completely managed relational database system which uses SQL Server technology to deliver optimal cloud performance. The service underwent major developments because of its enhanced AI integration features and its ability to scale better. The key features of Azure SQL Database include:

• Machine learning algorithms enable automatic performance optimization and scaling features in the system.
• The system maintains 99.99% SLA status and includes automatic backup features and built-in disaster recovery functionality.
• The service provides full compatibility between Azure AI services and Microsoft Fabric to support complete data workflow operations.
• Users can enable serverless compute features with automatic pause/resume functionality to reduce costs.
• The platform supports databases up to 100TB capacity through its hyperscale architecture which allows rapid database scaling.
• Users receive free access to ten General Purpose databases with 100,000 vCore seconds available each month.

 Cosmos DB

The Cosmos DB platform operates as a distributed database service which caters to AI-native applications that require worldwide reach and rapid data access. The service allows users to operate through SQL and MongoDB and Cassandra and Gremlin APIs. The following benefits characterize Cosmos DB:

• The system provides worldwide distribution with automatic multi-region data replication and ensures minimal latency for users.
• The system supports five different data patterns through document and key-value along with graph and columnar data models.
• The system ensures single-digit millisecond latency and high throughput through its complete set of service level agreements.
• The system provides native vector search functions through DiskANN indexing for managing large-scale operations.
• The system provides full-text search capabilities through BM25 ranking while also supporting vector and text search combinations.

The system provides direct Microsoft Fabric connections for instant analytics and AI workflow execution.

The system includes automated scaling functions which support both serverless and provisioned capacity configurations.

PostgreSQL on Azure

Engineers increasingly choose PostgreSQL as their open-source relational database management system because it provides advanced features together with AI integration capabilities. Azure Database for PostgreSQL delivers enterprise-class managed services together with upgraded AI functionality. The main advantages of PostgreSQL include:

• The system maintains full ACID compliance while providing a strong relational model to support complex transactional workloads.
• The system has an extensive extension library which includes PostGIS, TimescaleDB, and pg_vector for supporting AI operations.
• The system includes built-in support for JSON/JSONB data types and arrays as well as vector embeddings.
• The system provides complex query optimization mechanisms and execution planning functions.
• The system maintains complete standards compliance through its complete SQL feature set alongside PostgreSQL 17 compatibility.
• Azure Flexible Server deployment enables Microsoft Fabric mirroring for zero-ETL analytics.

The AI-ready extensions support semantic search functionality and machine learning model integration.

Key Similarities

The four databases share fundamental characteristics that support current application requirements even though they serve different functions.

The four database solutions integrate natively with Azure AI services and Microsoft Fabric to provide seamless AI-powered architectures.

• AI Capabilities: The platforms enable vector search capabilities and embedding support and large language model integration for developing intelligent applications.
• Enterprise Security: Security features include encryption at rest and in transit as well as Azure Active Directory integration and compliance certifications.
• Scalability Options: Each platform uses a different scaling method to manage workload expansion and AI processing requirements effectively.
• Managed Operations: The platforms offer different operational management levels which range from self-managed SQL IaaS to fully managed services with AI optimization.

 Choosing

Your selection of a database solution depends on your technical needs, AI requirements, and operational preferences. Teams who need SQL Server compatibility and minimal operational overhead and built-in intelligence should choose Azure SQL Database because it provides managed relational capabilities with automatic optimization and integrated AI services.

The combination of pgvector extension with PostgreSQL provides maximum flexibility and advanced AI features which makes it suitable for complex applications that need extensive customization as well as open-source solutions and sophisticated machine learning workflows. The database integrates with Microsoft Fabric to deliver smooth analytics and AI model deployment.

Cosmos DB stands out as the best option for globally distributed AI-native scenarios because it provides advanced vector search capabilities along with full-text indexing and hybrid search functionality which justifies its premium cost for applications that need global scale and diverse data models and sophisticated AI integration. The DiskANN indexing makes it particularly suitable for large-scale AI applications processing millions of vectors.

SQL IaaS with SQL Server 2025 provides complete control and cutting-edge AI capabilities including native vector search and direct LLM integration, suitable for organizations requiring specific configurations, OS-level access, or the latest AI features in a controlled environment.

Your decision should be based on the application data model’s complexity and AI requirements, as well as scaling and geographic distribution needs, in conjunction with team operational capabilities. Each database solution optimizes for specific strengths that meet the modern requirements of AI-powered applications, so select the solution that matches your engineering needs rather than searching for a single solution.