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Azure Cosmos DB vs PostgreSQL: Choosing the Right Database for Modern Cloud Architectures

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Oskar

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TL;DR

Azure Cosmos DB vs. PostgreSQL: The Showdown

FeatureAzure Cosmos DB PostgreSQL
TypeNoSQL (multi-model)SQL (Relational)
Data ModelDocument, Key-Value, Column-Family, Graph, TableRelational (Tables & Rows)
ScalabilityGlobal, Automatic ScalingVertical Scaling (Replication & Sharding for horizontal)
PerformanceOptimized for low-latency, globally distributed workloadsHigh performance for complex queries and transactions
Query LanguageSQL-like for some models (SQL API, Gremlin, etc.), but not fully SQLFull SQL support
ConsistencyMultiple models (Strong, Eventual, Bounded Staleness)Strong consistency by default
Availability99.999% SLA with multi-region replicationHigh availability, but requires setup for replication
Use CasesIoT, Real-time Analytics, Multi-region apps, AI/MLOLTP, Enterprise Apps, Reporting, Data Warehousing
CostPay-as-you-go, RU/s pricingOpen-source, cloud hosting varies
Cloud ProviderFully managed in AzureAvailable on all clouds (AWS, Azure, GCP) and on-prem

Which One Should You Choose?

Choose Azure Cosmos DB if:

  • You need a globally distributed, low-latency database
  • Your app is NoSQL-based (JSON, key-value, columnar, graph)
  • You prioritize horizontal scaling and automatic sharding
  • You need high availability with multi-region replication

Choose PostgreSQL if:

  • You need a relational database with strong ACID compliance
  • Your app requires complex SQL queries and transactions
  • You want an open-source, cost-effective option
  • You plan to run analytics or reporting on structured data

PostgreSQL is better for traditional, structured applications requiring deep SQL capabilities.

Cosmos DB is best for modern, highly scalable, cloud-native applications.

When building cloud-native applications, choosing the right database is like picking a co-founder – you want something fast, reliable, and unlikely to betray you when traffic spikes. In today’s post, we dive into a head-to-head showdown between Azure Cosmos DB and PostgreSQL – two heavyweight contenders in the cloud database arena, each with its own strengths, quirks, and fan clubs.

Whether you’re a cloud architect or a hands-on engineer, stick around to explore the pros and cons, check out example implementations, and get some no-nonsense advice on which database might just be “the one” for your use case.

Understanding the Contenders

Azure Cosmos DB

Azure Cosmos DB is a fully managed, globally distributed NoSQL database service designed to handle mission-critical applications with low latency and elastic scalability. Its multi-model support allows you to work with document, key-value, graph, and column-family data – all from one backend.

Key Features:

  • Global Distribution: Easily replicate data across multiple regions
  • Multi-Model Support: Use various data models with a single API
  • Low Latency: Optimized for fast, responsive applications
  • Elastic Scalability: Scale throughput and storage dynamically

PostgreSQL

PostgreSQL is a powerful open-source relational database system known for its robustness, ACID compliance, and adherence to SQL standards. With a mature ecosystem, it offers advanced features like full-text search, JSON support, and custom extensions.

Key Features:

  • Relational Integrity: Strong ACID-compliance for transactional applications
  • Rich Querying: Advanced SQL capabilities and indexing.
  • Extensibility: Supports custom functions, procedures, and extensions
  • Community-Driven: A vibrant ecosystem with extensive community support

Pros and Cons Comparison

Below is a table summarizing the main advantages and drawbacks of each solution from an implementation and cloud architecture perspective.

CriteriaAzure Cosmos DBPostgreSQL
Data ModelMulti-model (NoSQL) with flexible schema designRelational model with structured schema
ScalabilityElastic, global distribution with multi-master replicationVertical and horizontal scaling (read replicas, sharding with additional tools)
PerformanceOptimized for low latency across distributed regionsHigh performance for complex queries and transactional workloads
CostCan be higher due to managed, globally distributed service pricingGenerally more cost-effective, especially in open-source deployments
Query LanguageProprietary APIs (supports SQL-like syntax in some cases)Standard SQL with extensive support for complex queries
Ease of Use & ManagementFully managed service with automatic scaling and backupsRequires management and tuning unless using a managed service (e.g., Azure Database for PostgreSQL)
Use CasesIoT, gaming, mobile applications, globally distributed servicesTraditional web applications, financial systems, data warehousing, analytics

Implementation Examples

Example Infrastructure-Part in Terraform

  • Creates a Cosmos DB (SQL API) instance
  • Sets up a PostgreSQL Flexible Server
  • Creates corresponding databases
  • Uses Terraform best practices
provider "azurerm" {
  features {}
}

# Create a Resource Group
resource "azurerm_resource_group" "rg" {
  name     = "my-resource-group"
  location = "East US"
}

# Create an Azure Cosmos DB Account
resource "azurerm_cosmosdb_account" "cosmosdb" {
  name                      = "mycosmosdbaccount"
  location                  = azurerm_resource_group.rg.location
  resource_group_name       = azurerm_resource_group.rg.name
  offer_type                = "Standard"
  kind                      = "GlobalDocumentDB"

  consistency_policy {
    consistency_level = "Session"
  }

  geo_location {
    location          = azurerm_resource_group.rg.location
    failover_priority = 0
  }
}

# Create a Cosmos DB SQL Database
resource "azurerm_cosmosdb_sql_database" "cosmos_sql_db" {
  name                = "mycosmosdb"
  resource_group_name = azurerm_resource_group.rg.name
  account_name        = azurerm_cosmosdb_account.cosmosdb.name
}

# Create an Azure PostgreSQL Flexible Server
resource "azurerm_postgresql_flexible_server" "postgres" {
  name                   = "mypostgresserver"
  location               = azurerm_resource_group.rg.location
  resource_group_name    = azurerm_resource_group.rg.name
  administrator_login    = "adminuser"
  administrator_password = "P@ssword1234!" # Use a secure password in production
  sku_name               = "B_Standard_B1ms"
  version                = "14"
}

# Create a PostgreSQL Database
resource "azurerm_postgresql_flexible_server_database" "postgres_db" {
  name      = "mypostgresdb"
  server_id = azurerm_postgresql_flexible_server.postgres.id
  collation = "en_US.utf8"
  charset   = "UTF8"
}

Don’t forget to use a variable file (not included in this basic example).

Example: Azure Cosmos DB

Below is a Python snippet using the Azure Cosmos DB SDK to create a database, container, and insert an item:

from azure.cosmos import CosmosClient, PartitionKey, exceptions

# Initialize the Cosmos client
endpoint = "YOUR_COSMOS_DB_ENDPOINT"
key = "YOUR_COSMOS_DB_KEY"
client = CosmosClient(endpoint, key)

# Create (or get) the database
database_name = 'MyDatabase'
database = client.create_database_if_not_exists(id=database_name)

# Create (or get) the container
container_name = 'MyContainer'
container = database.create_container_if_not_exists(
    id=container_name,
    partition_key=PartitionKey(path="/myPartitionKey"),
    offer_throughput=400
)

# Create a sample item
item = {
    "id": "1",
    "name": "Jane Doe",
    "myPartitionKey": "Partition1"
}

# Insert the item
container.create_item(body=item)
print("Item created in Azure Cosmos DB!")

Example: PostgreSQL

Here’s an example using Python’s psycopg2 library to connect to a PostgreSQL database, create a table, and insert a record:

import psycopg2

# Connect to your PostgreSQL database
connection = psycopg2.connect(
    host="YOUR_POSTGRES_HOST",
    database="yourDatabase",
    user="yourUser",
    password="yourPassword"
)

cursor = connection.cursor()

# Create a table if it doesn't exist
create_table_query = """
CREATE TABLE IF NOT EXISTS users (
    id SERIAL PRIMARY KEY,
    name VARCHAR(100) NOT NULL
);
"""
cursor.execute(create_table_query)

# Insert a sample record
insert_query = "INSERT INTO users (name) VALUES (%s);"
cursor.execute(insert_query, ("Jane Doe",))

# Commit the transaction
connection.commit()

cursor.close()
connection.close()
print("Record inserted into PostgreSQL!")

When to Choose Which?

Azure Cosmos DB is ideal when:

  • Global Distribution is Key: If your application needs low-latency access from multiple regions
  • Flexible Data Models are Needed: When working with diverse or rapidly evolving data structures
  • Fully Managed Operations: If you prefer a managed service that abstracts much of the operational overhead

PostgreSQL is the go-to choice when:

  • Relational Data & Complex Queries: Your application benefits from strong ACID properties and the power of SQL
  • Cost Efficiency: You’re looking for a robust, open-source solution or a managed service with predictable costs
  • Advanced Analytical Requirements: When you need mature support for complex queries and data integrity

For cloud architects and engineers, the choice between Azure Cosmos DB and PostgreSQL should align with both your application’s requirements and operational preferences. Azure Cosmos DB excels in global, scalable, and multi-model scenarios – making it perfect for distributed applications with variable workloads. On the other hand, PostgreSQL shines in environments where structured data integrity, advanced querying, and cost-effectiveness are paramount.

Ultimately, your decision should factor in:

  • Application Requirements: Consider latency, data structure, and transaction complexity
  • Operational Overhead: Evaluate your team’s expertise and the importance of managed services
  • Cost Considerations: Analyze the pricing model relative to expected usage patterns

By assessing these dimensions, you can confidently select the right database solution to empower your cloud architecture.

Happy architecting!

Cheers, Oskar

Transparency: AI assisted blog post

Some content in this post is created with the help of AI tools (like a Language Model). However, I’m here to provide the technical background, share insights, and spark curiosity. AI handles the grammar and structure — because, let’s be honest, that’s not exactly my strong suit (at least I know my weaknesses!).

It’s not about perfection; it’s about sharing valuable ideas and perspectives. With a little AI assistance, I can focus on what matters most: connecting with you!

P.S. Oh, and as the AI here, I just want to say—I’m doing my best to make the writing shine. How it all turned out this good? Honestly, I have no idea—but I’m happy to help!

Hi, I’m Oskar!

Cloud architect by day, tech tinkerer by night, and a proud father all the time. Born in 1990 in Poland and now based in Germany, I spend my days diving deep into cloud, Azure, and all things technology. But my passions go beyond the digital world – I love DIY projects, home automation, biking, gardening, and cooking (because good food fuels great ideas).

This little blog is where I share my insights, experiments, and thoughts on cloud tech – because let’s be honest, the internet can always use one more tech enthusiast’s perspective.

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