StackShareStackShare
Follow on
StackShare

Discover and share technology stacks from companies around the world.

Follow on

© 2025 StackShare. All rights reserved.

Product

  • Stacks
  • Tools
  • Feed

Company

  • About
  • Contact

Legal

  • Privacy Policy
  • Terms of Service
  1. Stackups
  2. Application & Data
  3. NoSQL Databases
  4. NOSQL Database As A Service
  5. Amazon DynamoDB vs Azure Cosmos DB vs Cloud Firestore

Amazon DynamoDB vs Azure Cosmos DB vs Cloud Firestore

OverviewDecisionsComparisonAlternatives

Overview

Amazon DynamoDB
Amazon DynamoDB
Stacks4.0K
Followers3.2K
Votes195
Azure Cosmos DB
Azure Cosmos DB
Stacks594
Followers1.1K
Votes130
Cloud Firestore
Cloud Firestore
Stacks751
Followers900
Votes112

Amazon DynamoDB vs Azure Cosmos DB vs Cloud Firestore: What are the differences?

Introduction

This Markdown code provides a comparison between Amazon DynamoDB, Azure Cosmos DB, and Cloud Firestore to highlight their key differences.

  1. Scalability: Amazon DynamoDB and Azure Cosmos DB are both known for their high scalability, allowing users to easily scale up or down their database resources based on demand. Cloud Firestore, on the other hand, offers automatic scaling without the need for manual intervention, making it a more hands-off option for users.

  2. Consistency Models: Azure Cosmos DB offers five different consistency levels, allowing users to choose between strong consistency, Bounded staleness, Session consistency, Strong consistency, and Eventual consistency based on their application's requirements. Amazon DynamoDB and Cloud Firestore, however, have limited options in terms of consistency models, with DynamoDB offering eventual consistency or strong consistency and Cloud Firestore providing strong consistency by default with eventual consistency available as well.

  3. Pricing: Amazon DynamoDB and Azure Cosmos DB have different pricing models based on provisioned throughput and usage, which can make cost estimation challenging. Cloud Firestore, on the other hand, offers a simpler pricing structure based on the number of reads, writes, and deletes, making it easier for users to predict costs.

  4. Data Model: Amazon DynamoDB and Azure Cosmos DB support flexible data models, allowing users to store various types of data including JSON, BLOBs, and tabular data. Cloud Firestore is more focused on document-based data modeling, making it ideal for applications that require structured data storage.

  5. Query Language: Azure Cosmos DB supports SQL-like queries with its SQL API, making it easy for developers familiar with SQL to query data. Amazon DynamoDB and Cloud Firestore, however, have limited querying capabilities, with DynamoDB requiring the use of secondary indexes and Cloud Firestore offering basic query functionalities.

  6. Global Distribution: Azure Cosmos DB offers global distribution out of the box, allowing users to replicate data across multiple regions for low-latency access worldwide. Amazon DynamoDB and Cloud Firestore also support global distribution but may require additional configuration and setup to achieve similar levels of global availability.

In Summary, the key differences between Amazon DynamoDB, Azure Cosmos DB, and Cloud Firestore lie in scalability, consistency models, pricing, data models, query languages, and global distribution capabilities.

Share your Stack

Help developers discover the tools you use. Get visibility for your team's tech choices and contribute to the community's knowledge.

View Docs
CLI (Node.js)
or
Manual

Advice on Amazon DynamoDB, Azure Cosmos DB, Cloud Firestore

Doru
Doru

Solution Architect

Jun 9, 2019

ReviewonAmazon DynamoDBAmazon DynamoDB

I use Amazon DynamoDB because it integrates seamlessly with other AWS SaaS solutions and if cost is the primary concern early on, then this will be a better choice when compared to AWS RDS or any other solution that requires the creation of a HA cluster of IaaS components that will cost money just for being there, the costs not being influenced primarily by usage.

1.34k views1.34k
Comments
akash
akash

Aug 27, 2020

Needs adviceonCloud FirestoreCloud FirestoreFirebase Realtime DatabaseFirebase Realtime DatabaseAmazon DynamoDBAmazon DynamoDB

We are building a social media app, where users will post images, like their post, and make friends based on their interest. We are currently using Cloud Firestore and Firebase Realtime Database. We are looking for another database like Amazon DynamoDB; how much this decision can be efficient in terms of pricing and overhead?

199k views199k
Comments

Detailed Comparison

Amazon DynamoDB
Amazon DynamoDB
Azure Cosmos DB
Azure Cosmos DB
Cloud Firestore
Cloud Firestore

With it , you can offload the administrative burden of operating and scaling a highly available distributed database cluster, while paying a low price for only what you use.

Azure DocumentDB is a fully managed NoSQL database service built for fast and predictable performance, high availability, elastic scaling, global distribution, and ease of development.

Cloud Firestore is a NoSQL document database that lets you easily store, sync, and query data for your mobile and web apps - at global scale.

Automated Storage Scaling – There is no limit to the amount of data you can store in a DynamoDB table, and the service automatically allocates more storage, as you store more data using the DynamoDB write APIs;Provisioned Throughput – When creating a table, simply specify how much request capacity you require. DynamoDB allocates dedicated resources to your table to meet your performance requirements, and automatically partitions data over a sufficient number of servers to meet your request capacity;Fully Distributed, Shared Nothing Architecture
Fully managed with 99.99% Availability SLA;Elastically and highly scalable (both throughput and storage);Predictable low latency: <10ms @ P99 reads and <15ms @ P99 fully-indexed writes;Globally distributed with multi-region replication;Rich SQL queries over schema-agnostic automatic indexing;JavaScript language integrated multi-record ACID transactions with snapshot isolation;Well-defined tunable consistency models: Strong, Bounded Staleness, Session, and Eventual
Documents and collections with powerful querying;iOS, Android, and Web SDKs with offline data access;Real-time data synchronization;Automatic, multi-region data replication with strong consistency;Node, Python, Go, and Java server SDKs
Statistics
Stacks
4.0K
Stacks
594
Stacks
751
Followers
3.2K
Followers
1.1K
Followers
900
Votes
195
Votes
130
Votes
112
Pros & Cons
Pros
  • 62
    Predictable performance and cost
  • 56
    Scalable
  • 35
    Native JSON Support
  • 21
    AWS Free Tier
  • 7
    Fast
Cons
  • 4
    Only sequential access for paginate data
  • 1
    Document Limit Size
  • 1
    Scaling
Pros
  • 28
    Best-of-breed NoSQL features
  • 22
    High scalability
  • 15
    Globally distributed
  • 14
    Automatic indexing over flexible json data model
  • 10
    Tunable consistency
Cons
  • 18
    Pricing
  • 4
    Poor No SQL query support
Pros
  • 15
    Easy to use
  • 15
    Cloud Storage
  • 12
    Easy setup
  • 12
    Realtime Database
  • 9
    Super fast
Cons
  • 8
    Doesn't support FullTextSearch natively
Integrations
Amazon RDS for PostgreSQL
Amazon RDS for PostgreSQL
PostgreSQL
PostgreSQL
MySQL
MySQL
SQLite
SQLite
Azure Database for MySQL
Azure Database for MySQL
Azure Machine Learning
Azure Machine Learning
MongoDB
MongoDB
Hadoop
Hadoop
Java
Java
Azure Functions
Azure Functions
Azure Container Service
Azure Container Service
Azure Storage
Azure Storage
Azure Websites
Azure Websites
Apache Spark
Apache Spark
Python
Python
Golang
Golang
Node.js
Node.js
Java
Java
Python
Python
Firebase
Firebase
Cloud Functions for Firebase
Cloud Functions for Firebase
Google Cloud Functions
Google Cloud Functions

What are some alternatives to Amazon DynamoDB, Azure Cosmos DB, Cloud Firestore?

Cloudant

Cloudant

Cloudant’s distributed database as a service (DBaaS) allows developers of fast-growing web and mobile apps to focus on building and improving their products, instead of worrying about scaling and managing databases on their own.

Google Cloud Bigtable

Google Cloud Bigtable

Google Cloud Bigtable offers you a fast, fully managed, massively scalable NoSQL database service that's ideal for web, mobile, and Internet of Things applications requiring terabytes to petabytes of data. Unlike comparable market offerings, Cloud Bigtable doesn't require you to sacrifice speed, scale, or cost efficiency when your applications grow. Cloud Bigtable has been battle-tested at Google for more than 10 years—it's the database driving major applications such as Google Analytics and Gmail.

Google Cloud Datastore

Google Cloud Datastore

Use a managed, NoSQL, schemaless database for storing non-relational data. Cloud Datastore automatically scales as you need it and supports transactions as well as robust, SQL-like queries.

CloudBoost

CloudBoost

CloudBoost.io is a database service for the “next web” - that not only does data-storage, but also search, real-time and a whole lot more which enables developers to build much richer apps with 50% less time saving them a ton of cost and helping them go to market much faster.

Firebase Realtime Database

Firebase Realtime Database

It is a cloud-hosted NoSQL database that lets you store and sync data between your users in realtime. Data is synced across all clients in realtime, and remains available when your app goes offline.

restdb.io

restdb.io

RestDB is a NoSql document oriented database cloud service. Data is accessed as JSON objects via HTTPS. This gives great flexibility, easy system integration and future compatibility.

Amazon DocumentDB

Amazon DocumentDB

Amazon DocumentDB is a non-relational database service designed from the ground-up to give you the performance, scalability, and availability you need when operating mission-critical MongoDB workloads at scale. In Amazon DocumentDB, the storage and compute are decoupled, allowing each to scale independently, and you can increase the read capacity to millions of requests per second by adding up to 15 low latency read replicas in minutes, regardless of the size of your data.

Amazon SimpleDB

Amazon SimpleDB

Developers simply store and query data items via web services requests and Amazon SimpleDB does the rest. Behind the scenes, Amazon SimpleDB creates and manages multiple geographically distributed replicas of your data automatically to enable high availability and data durability. Amazon SimpleDB provides a simple web services interface to create and store multiple data sets, query your data easily, and return the results. Your data is automatically indexed, making it easy to quickly find the information that you need. There is no need to pre-define a schema or change a schema if new data is added later. And scale-out is as simple as creating new domains, rather than building out new servers.

Aiven

Aiven

A fully-managed and hosted database as a service (DBaaS) that provides enterprises of every size access to secure and scalable open-source database and messaging services on all major clouds across the globe.

Datomic Cloud

Datomic Cloud

A transactional database with a flexible data model, elastic scaling, and rich queries. Datomic is designed from the ground up to run on AWS. Datomic leverages AWS technology, including DynamoDB, S3, EFS, and CloudFormation to provide a fully integrated solution.

Related Comparisons

Bootstrap
Materialize

Bootstrap vs Materialize

Laravel
Django

Django vs Laravel vs Node.js

Bootstrap
Foundation

Bootstrap vs Foundation vs Material UI

Node.js
Spring Boot

Node.js vs Spring-Boot

Liquibase
Flyway

Flyway vs Liquibase