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Azure DocumentDB and Azure Cosmos DB are both powerful NoSQL database services designed to help you build successful applications with flexible JSON data models. While both serve the NoSQL database market, they're optimized for different use cases and architectural patterns. This guide helps you understand which service best fits your specific requirements.
Choose the right service for your needs
Both Azure DocumentDB and Azure Cosmos DB are designed to support your success with NoSQL databases. The key is selecting the service that aligns with your application's specific requirements and growth patterns.
When to choose Azure Cosmos DB
Azure Cosmos DB is optimized for scale-out scenarios where you need:
- Global distribution and high availability: Industry-leading 99.999% availability service level agreement (SLA) with automatic failover across multiple regions
- Massive scale and performance: Designed for applications that need to handle millions of operations per second with single-digit millisecond response times
- Instantaneous scaling: Automatic and transparent scaling that responds immediately to traffic spikes without warmup periods
- Mission-critical applications: Enterprise-grade security, compliance certifications, and guaranteed performance under extreme load
- Microservices architectures: Excellent for distributed systems that need consistent performance across multiple services and regions
- AI applications and vector search: Native vector database capabilities with support for embeddings, enabling retrieval-augmented generation (RAG), semantic search, and AI agent memory systems
- Generative AI workloads: Integrated vector search with multiple algorithms (inverted file (IVF), hierarchical navigable small world (HNSW), DiskANN) for storing and querying vector embeddings alongside operational data
Ideal use cases: High-traffic web apps, IoT data collection, real-time gaming, and global shopping sites. Also great for AI chatbots, AI agents, RAG apps, and semantic search. Perfect for any app that needs guaranteed performance worldwide.
When to choose Azure DocumentDB
Azure DocumentDB is optimized for scale-up scenarios where you need:
- Rich query capabilities: Advanced MongoDB Query Language (MQL) support with complex aggregation pipelines, joins, and analytical queries
- Familiar development experience: Full MongoDB wire protocol compatibility with existing drivers, tools, and development patterns
- Flexible data modeling: Built on PostgreSQL engine, providing robust support for complex document structures and advanced indexing
- Cost-effective scaling: vCore-based pricing model that scales vertically with predictable costs
- Advanced query features: Support for full-text search, geospatial queries, vector search, and sophisticated aggregation operations
Ideal use cases: Great for content systems, data analysis tools, and apps with complex queries. Also perfect for moving from MongoDB or when you need advanced document database features.
Architecture and scaling differences
Understanding the fundamental architectural approaches of each service helps explain their different strengths and optimal use cases.
Azure Cosmos DB (Scale-Out Architecture)
Azure Cosmos DB uses a Request Unit (RU) based model designed for elastic, horizontal scaling:
- Automatic partitioning: Data is automatically distributed across multiple partitions for optimal performance
- Granular throughput control: Scale in small increments as small as 1/100th of a traditional vCore
- Serverless options: Pay-per-operation pricing for variable workloads
- Multi-model support: Native support for document, key-value, graph, and column-family data models
Azure DocumentDB (Scale-Up Architecture)
Azure DocumentDB uses a vCore-based model optimized for vertical scaling and rich functionality:
- Dedicated compute resources: Familiar cluster tiers (M30, M40, M50+) with dedicated CPU, memory, and storage
- PostgreSQL foundation: Uses PostgreSQL's mature ecosystem and advanced features
- MongoDB compatibility: Complete wire protocol compatibility with MongoDB tools and applications
- Advanced indexing: Support for text indexes, geospatial indexes, vector indexes, and compound indexes
Feature comparison
The following table provides a side-by-side comparison of key capabilities to help you evaluate which service best meets your requirements.
| Azure Cosmos DB | Azure DocumentDB | |
|---|---|---|
| Availability SLA | 99.999% (multi-region) | 99.995% |
| Global distribution | Turn-key multi-region with automatic failover | Regional deployment with geo-replicas |
| Scaling model | Horizontal (RU-based) | Vertical (vCore-based) |
| Query complexity | Optimized for point reads and simple queries | Advanced aggregation pipelines and complex joins |
| MongoDB compatibility | Core MongoDB operations | Full MongoDB wire protocol and MQL features |
| Vector search | Native vector database with IVF, HNSW, DiskANN algorithms | Integrated pg_vector support for HNSW and IVF |
| AI/RAG applications | Built-in vector embeddings, AI agent memory, semantic caching | Vector search for RAG and semantic applications |
| Pricing model | Variable (RU-based) or serverless | Predictable (compute + storage) |
| Development model | Cloud-native applications | MongoDB application migration and development |
Performance characteristics
Each service excels in different performance scenarios based on their underlying architectures and optimization strategies.
Azure Cosmos DB strengths
- Ultra-low latency: Single-digit millisecond response times globally
- Instant scaling: Zero warmup time for traffic spikes
- Throughput guarantees: SLA-backed performance commitments
- Point read optimization: Fast single-document lookups by ID and partition key
Azure DocumentDB strengths
- Query performance: Optimized for complex aggregation operations and analytical queries
- Indexing flexibility: Advanced indexing strategies for diverse query patterns
- Transaction support: ACID transactions with MongoDB semantics
- Analytical workloads: Better suited for reporting and business intelligence scenarios
Integration and ecosystem
Both services offer rich integration capabilities, though they connect with different ecosystems and tools based on their architectural foundations.
Azure Cosmos DB integrations
Azure Cosmos DB has the following integrations:
- Azure Synapse Link: Analytics without extract, transform, and load (ETL) processes
- Azure Functions: Serverless computing with change feed triggers
- Power BI: Direct connectivity for business intelligence
- Azure AI Services: Native integration for AI and ML workloads
- Azure OpenAI: Seamless integration for embeddings generation and RAG applications
- LangChain and AI Frameworks: Built-in support for popular AI development frameworks
- Semantic Kernel: Integration for building AI agents with persistent memory systems
Azure DocumentDB integrations
Azure DocumentDB has the following integrations:
- MongoDB ecosystem: Full compatibility with existing MongoDB tools and libraries
- PostgreSQL extensions: Access to rich PostgreSQL extension ecosystem
- Azure services: Integration with Azure monitoring, security, and networking services
- Open source: Built on the open-source DocumentDB engine with MIT licensing
Migration considerations
When evaluating a move between services, consider your application's current architecture, performance requirements, and future growth plans.
Moving to Azure Cosmos DB
Consider Azure Cosmos DB if your current application:
- Experiences unpredictable traffic patterns requiring instant scaling
- Needs global distribution for user proximity
- Has simple query patterns focused on document lookups
- Requires guaranteed low-latency performance
Moving to Azure DocumentDB
Consider Azure DocumentDB if your current application:
- Uses complex MongoDB queries and aggregation pipelines
- Requires advanced indexing and query optimization
- Benefits from PostgreSQL's mature feature set
- Needs predictable, vCore-based pricing
Getting started
Taking the first step with either service is straightforward, with comprehensive resources available to support your journey. Both services offer comprehensive migration tools and support to help you transition successfully:
- Assessment tools: Evaluate your current workload and requirements
- Migration utilities: Native tools for data and application migration
- Documentation and samples: Extensive resources for both services
- Support options: Azure support plans to assist with your migration journey