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Amazon DocumentDB vs Microsoft Azure Cosmos DB comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Jan 25, 2026

Review summaries and opinions

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

Categories and Ranking

Amazon DocumentDB
Ranking in Managed NoSQL Databases
5th
Average Rating
8.2
Reviews Sentiment
4.0
Number of Reviews
6
Ranking in other categories
No ranking in other categories
Microsoft Azure Cosmos DB
Ranking in Managed NoSQL Databases
1st
Average Rating
8.2
Reviews Sentiment
6.9
Number of Reviews
109
Ranking in other categories
Database as a Service (DBaaS) (4th), NoSQL Databases (2nd), Vector Databases (1st)
 

Mindshare comparison

As of March 2026, in the Managed NoSQL Databases category, the mindshare of Amazon DocumentDB is 7.4%, down from 10.1% compared to the previous year. The mindshare of Microsoft Azure Cosmos DB is 16.0%, down from 16.3% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Managed NoSQL Databases Market Share Distribution
ProductMarket Share (%)
Microsoft Azure Cosmos DB16.0%
Amazon DocumentDB7.4%
Other76.6%
Managed NoSQL Databases
 

Featured Reviews

Hemanth Perepi - PeerSpot reviewer
Technical Lead at Trianz
Supports high-level data management and secure migration
Over the past few months, I’ve been working closely with a managed database service, and a few features stood out as game changers for me and my team: MongoDB Compatibility – The seamless migration experience was a huge win. No need to rewrite code or change drivers, which meant less friction and faster adoption for our developers. Fully Managed Service – Patching, backups, and monitoring are all automated. This freed up our team to focus on building applications instead of managing infrastructure. Separation of Compute & Storage – The flexibility to scale compute and storage independently gave us both cost savings and better performance optimization. Multi-AZ High Availability – Automatic failover and cross-AZ replication gave us peace of mind with improved uptime and disaster recovery. Performance at Scale – Even with large datasets, performance has remained consistent. Read replicas and efficient indexing have been especially valuable for read-heavy workloads. Security – End-to-end encryption, VPC isolation, and IAM integration made enterprise-level security feel straightforward and reliable. Backup & Recovery – Point-in-time recovery with automated backups made data protection effortless.
reviewer2724105 - PeerSpot reviewer
Senior Director of Product Management at a tech vendor with 1,001-5,000 employees
Provides super sharp latency, excellent availability, and the ability to effectively manage costs across different tenants
For integrating Microsoft Azure Cosmos DB with other Azure products or other products, there are a couple of challenges with the current system. Right now, the vectors are stored as floating-point numbers within the NoSQL document, which makes them inefficiently large. This leads to increased storage space requirements, and searching through a vast number of documents in the vector database becomes quite costly in terms of RUs. While the integration works well, the expense associated with it is relatively high. I would really like to see a reduction in costs for their vector search, as it is currently on the expensive side. The areas for improvement in Microsoft Azure Cosmos DB are vector pricing and vector indexing patterns, which are unintuitive and not well described. I would also like to see the parameters of Fleet Spaces made more powerful, as currently, it's somewhat lightweight. I believe they've made those changes intentionally to better understand the cost model. However, we would like to take a more aggressive approach in using it. One of the most frustrating aspects of Microsoft Azure Cosmos DB right now is that you can only store one vector per document. Additionally, you must specify the configuration of that vector when you create an instance of Microsoft Azure Cosmos DB. Once the database is set up, you can't change the vector configuration, which is incredibly limiting for experimentation. You want the ability to try different settings and see how they perform, as there are numerous use cases for storing more than one vector in a document. While interoperability within the vector database is acceptable—for example, I can search for vectors—I still desire a richer set of configuration options.

Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

Pros

"Efficient data retrieval with millisecond fetch times sets it apart from RDS."
"Amazon DocumentDB is a simple solution."
"There are many benefits to using Amazon DocumentDB, for example, regarding the price, you can start with a small database and when you need more performance, you can grow the database."
"Efficient data retrieval with millisecond fetch times sets it apart from RDS."
"Migrations are easy using this product."
"Its speed has had the most significant impact on our projects. For starters, we used it for its flexibility. With DocumentDB, you're not tied to a rigid structure like you are with Aurora or other relational databases. This makes it great for startups."
"The graphical representation of data is the most valuable feature of the solution."
"The efficiency of search capabilities is significant, particularly when it comes to the flexibility of conducting in-depth, almost recursive searches that are both efficient and cost-effective."
"Specifically, we are using the MongoDB API, so we leverage it in that way. I like the flexibility that it offers. My team does not have to spend time building out database tables. We can get going fairly quickly with being able to read and write data into a MongoDB collection that is hosted inside Azure Cosmos DB."
"Microsoft Azure Cosmos DB is a good solution for distributed application requirements. We can perform multi-modeling."
"Latency and availability are incredible."
"I would recommend Microsoft Azure Cosmos DB to other users without hesitation."
"The solution is used because we get faster response times with large data sets than with SQL."
"I like the way you can create and delete records. You pass a JSON, and then it creates a record."
 

Cons

"However, when you need more volume or more registers, it becomes complicated because the performance adjustments and tuning are challenging."
"Improvements for Amazon DocumentDB could focus on enhancing high availability, sharding methods, replication techniques, and automatic failover in case the primary goes down, as continuous backup is an excellent option for disaster recovery."
"There's a bit of a learning curve at the beginning."
"One possible improvement could be a hybrid database solution, where parts of the application leverage a relational database alongside DocumentDB. If a system were heavily relational in nature, a database like PostgreSQL might be a good fit."
"Improvements for Amazon DocumentDB could focus on enhancing high availability, sharding methods, replication techniques, and automatic failover in case the primary goes down, as continuous backup is an excellent option for disaster recovery."
"The technical support could be improved."
"Better documentation on how to integrate with other components would be helpful because I was struggling with this."
"There are no specific areas I believe need improvement as I am happy with what I am getting currently. However, I am open to new features in future versions, like possibly integrating AI features natively into Cosmos DB. Any improvement would be beneficial."
"Microsoft's support services are inadequate, especially during critical incidents."
"The UI needs enhancement. Unlike SQL, Cosmos DB's UI is not as straightforward, making it a bit challenging to use efficiently."
"Right now, the vectors are stored as floating-point numbers within the NoSQL document, which makes them inefficiently large. This leads to increased storage space requirements, and searching through a vast number of documents in the vector database becomes quite costly in terms of RUs. While the integration works well, the expense associated with it is relatively high. I would really like to see a reduction in costs for their vector search, as it is currently on the expensive side."
"The model with autoscaling for RU is complicated to optimize RU consumption."
"We should have more freedom to tweak it and make our own queries for non-traditional use-cases."
"The solution’s pricing could be improved."
 

Pricing and Cost Advice

Information not available
"The solution is a bit on the expensive side."
"Pricing, at times, is not super clear because they use the request unit (RU) model. To manage not just Azure Cosmos DB but what you are receiving for the dollars paid is not easy. It is very abstract. They could do a better job of connecting Azure Cosmos DB with the value or some variation of that."
"It is expensive. The moment you have high availability options and they are mixed with the type of multitenant architecture you use, the pricing is on the higher end."
"It is cost-efficient as long as you understand the right setup to optimize usage. Knowing the data needs of the organization and adjusting the Microsoft Azure Cosmos DB usage accordingly helps save costs, but if you don't know, you could end up spending more than necessary."
"Pricing is one of the solution's main features because it is based on usage, scales automatically, and is not too costly."
"Azure is a pay as you go subscription."
"Its pricing structure is quite flexible."
"Right now, I have opted for the student subscription plan, for which Microsoft charges me around 100 USD. The pricing of the solution depends on the solution's usage."
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Top Industries

By visitors reading reviews
Computer Software Company
17%
Financial Services Firm
12%
Manufacturing Company
10%
Educational Organization
7%
Legal Firm
12%
Financial Services Firm
10%
Comms Service Provider
9%
Manufacturing Company
8%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
No data available
By reviewers
Company SizeCount
Small Business33
Midsize Enterprise22
Large Enterprise58
 

Questions from the Community

What advice do you have for others considering Amazon DocumentDB?
Amazon DocumentDB offers us many useful features. It is definitely a solution that an organization in need of comprehensive and effective document management should invest its money into. We are im...
What do you like most about Amazon DocumentDB?
Its speed has had the most significant impact on our projects. For starters, we used it for its flexibility. With DocumentDB, you're not tied to a rigid structure like you are with Aurora or other ...
What is your experience regarding pricing and costs for Amazon DocumentDB?
The pricing and licensing of Amazon DocumentDB is managed directly by the client team with the vendor, so I am not involved in that aspect.
What do you like most about Microsoft Azure Cosmos DB?
The initial setup is simple and straightforward. You can set up a Cosmos DB in a day, even configuring things like availability zones around the world.
What is your experience regarding pricing and costs for Microsoft Azure Cosmos DB?
Microsoft Azure Cosmos DB's pricing model has aligned with my budget expectations because I can tune the RU as I need to, which helps a lot. Microsoft Azure Cosmos DB's dynamic auto-scale or server...
What needs improvement with Microsoft Azure Cosmos DB?
I have not utilized Microsoft Azure Cosmos DB multi-model support for handling diverse data types. I'm not in the position to decide if clients will use Microsoft Azure Cosmos DB or any other datab...
 

Also Known As

No data available
Microsoft Azure DocumentDB, MS Azure Cosmos DB
 

Overview

 

Sample Customers

Finra, The Washington Post, Freshop
TomTom, KPMG Australia, Bosch, ASOS, Mercedes Benz, NBA, Zero Friction, Nederlandse Spoorwegen, Kinectify
Find out what your peers are saying about Amazon DocumentDB vs. Microsoft Azure Cosmos DB and other solutions. Updated: February 2026.
883,089 professionals have used our research since 2012.