Google Cloud Bigtable vs Microsoft Azure Cosmos DB comparison

Google Cloud Bigtable vs. Microsoft Azure Cosmos DB

Download the complete report

Helped 884,797 peers since 2012

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

Google Cloud Bigtable

Ranking in Managed NoSQL Databases

10th

Average Rating

8.6

Reviews Sentiment

6.3

Number of Reviews

Ranking in other categories

Non-Relational Databases (5th)

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 Google Cloud Bigtable is 5.6%, up from 5.4% 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 Mindshare Distribution
Product	Mindshare (%)
Microsoft Azure Cosmos DB	16.0%
Google Cloud Bigtable	5.6%
Other	78.4%

Managed NoSQL Databases

Featured Reviews

Akash Shanker

Team Lead at a financial services firm with 5,001-10,000 employees

Consistent performance and seamless cloud integration enhance analytics capabilities while reducing management complexity

One point for improvement in Google Cloud Bigtable is that people have confusion in mapping. There are many similar products available, and Google has managed services for similar products as well. It would be easier if the journey of knowing when to use Google Cloud Bigtable versus other Cloud SQL and alternates such as Cloud Spanner is made clearer for users. Regarding additional functionality for Google Cloud Bigtable, I am uncertain if LLMs can be integrated or if Google Cloud Bigtable can act as a vector store for LLM-specific use cases where we are interacting or using generative AI capabilities.

Read full review

reviewer2724105

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.

Read full review

Quotes from Members

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

Pros

"I like the drive and the support of this program."

"Stability-wise, it is a simple solution. I rate the solution's stability a ten out of ten."

"The most valuable feature is the backup and replication service."

"Bigtable is faster than other competitors in the market. It helps us collate all the data, and the security features are great. The latency is low, and the computation speed is fantastic. Bigtable is also a managed service, so you don't have to worry about anything aside from analyzing the data ingested."

"Scalability-wise, I rate the solution a ten out of ten."

"It's very user-friendly where streaming data is required."

"The main benefit I receive from Google Cloud Bigtable is the managed service part."

"The solution is very convenient."

More Google Cloud Bigtable pros

"The best part of Microsoft Azure Cosmos DB is that with the default configuration and the Azure functional pipeline, if your go-to cloud provider is Microsoft Azure, the whole integration is seamless."

"It is one of the simpler databases to work with in terms of code management, tracking, and debugging due to its straightforward data storage and retrieval mechanisms."

"The most valuable features of Microsoft Azure Cosmos DB include the TTL, the ability to scale up and down as needed, and geo-replication, which comes out of the box."

"It works reasonably fast. You can retain the original format of the document as received by the third-party system."

"Microsoft Azure Cosmos DB has reduced our total cost of ownership by about half, allowing us to sell our product for about half of what we were selling it before, and Microsoft Azure Cosmos DB is probably 70% of the reason why that's true."

"What I appreciate most are the latency and the access, which are guaranteed by the tool, which is really impressive."

"We chose Azure Cosmos DB initially because of the type of data that we needed to store. We have a schema that is very nondeterministic and flexible. It is always changing based on whatever data we need to acquire from different devices, so we needed a document store with a flexible schema."

"Microsoft Azure Cosmos DB is very fast."

More Microsoft Azure Cosmos DB pros

Cons

"Improvement should be made as per customer recommended and requirements."

"I've used Bigtable for about three or four years."

"The pricing of the solution needs to be improved."

"This product needs better security and transparency, and the price should be reduced."

"The cost of this product is too expensive."

"The lagging problem of the product I face is an area of concern where improvements are required."

"When it comes to complex queries, a user can't get any help from a drop-down box and pick columns. It would be great if some improvements could be made in the aforementioned area concerning the solution."

"Our use case was a failure with Microsoft Azure Cosmos DB, and we do not have any other opportunity to use Microsoft Azure Cosmos DB."

"It's still new, and good training resources are harder to find. Even the most recent books on Cosmos DB are several years old, which is ancient in IT terms."

"Slight enhancements in integration interfaces, expanded dashboard functionalities, and broader use-case support would be beneficial."

"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."

"While Microsoft Azure Cosmos DB is generally easy to use, it has some limitations."

"The main area of improvement is the cost, as the expense is high. Also, when writing processes into Cosmos, sometimes the threshold is met, which can be a problem if developers have not written the code properly, limiting calls to five thousand. These aspects need addressing."

"The API compatibility has room for improvement, particularly integration with MongoDB. You have to connect to a specific flavor of MongoDB. We'd also like a richer query capability in line with the latest Mongo features. That is one thing on our wish list. The current version is good enough for our use case, but it could be improved."

"One of the most frustrating aspects of Microsoft Azure Cosmos DB right now is that you can only store one vector per document."

More Microsoft Azure Cosmos DB cons

Pricing and Cost Advice

"I would like to see better pricing. It is not too expensive, but it isn't cheap either."

"The solution is very expensive."

"With heavy use, like a large-scale IoT implementation, you could easily hit a quarter of a million dollars a month in Azure charges if Cosmos DB is a big part of it."

"Azure Cosmos DB is generally a costly resource compared to other Azure resources. It comes with a high cost. We have reserved one thousand RUs. Free usage is also limited."

"The pricing for Microsoft Azure Cosmos DB is good. Initially, it seemed like an expensive way to manage a NoSQL data store, but so many improvements that have been made to the platform have made it cost-effective."

"Everything could always be cheaper. I like that Cosmos DB allows us to auto-scale instead of pre-provisioning a certain capacity. It automatically scales to the demand, so we only pay for what we consume."

"Cosmos DB is expensive compared to any virtual machine based on conventional RDBMS like MySQL or PostgreSQL."

"The Cosmos DB pricing model, initially quite complicated, became clear after consulting with Azure Advisor, allowing us to proceed with confidence."

"Its price is very good for the basic stuff. When you go to a more complicated use case, especially when you need replication and availability zones, it gets a little costly."

More Microsoft Azure Cosmos DB pricing and cost advice

See which vendors are best for you

Use our free recommendation engine to learn which Managed NoSQL Databases solutions are best for your needs.

See recommendations

884,797 professionals have used our research since 2012.

Top Industries

By visitors reading reviews

Financial Services Firm

11%

Manufacturing Company

11%

Comms Service Provider

11%

Computer Software Company

Legal Firm

12%

Financial Services Firm

10%

Comms Service Provider

Manufacturing Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	6
Large Enterprise	4

By reviewers
Company Size	Count
Small Business	33
Midsize Enterprise	22
Large Enterprise	58

Questions from the Community

What needs improvement with Google Cloud Bigtable?

What is your primary use case for Google Cloud Bigtable?

My main use case for Google Cloud Bigtable is mainly for advertisement-related analytics-related use cases.

What advice do you have for others considering Google Cloud Bigtable?

Regarding integration with Google Cloud Bigtable and other Google Cloud services such as Dataflow, Dataproc, and BigQuery, we have not done that integration, but there are connectors available. Som...

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...

Amazon DynamoDB vs Google Cloud Bigtable

Comparisons

Compared 18% of the time

MongoDB Enterprise Advanced vs Google Cloud Bigtable

Compared 13% of the time

Amazon Keyspaces vs Google Cloud Bigtable

Compared 12% of the time

Timescale vs Google Cloud Bigtable

Compared 7% of the time

Oracle NoSQL Database Cloud vs Google Cloud Bigtable

Compared 6% of the time

More Google Cloud Bigtable Competitors

Pinecone vs Microsoft Azure Cosmos DB

Compared 6% of the time

Amazon DynamoDB vs Microsoft Azure Cosmos DB

Compared 6% of the time

MongoDB Enterprise Advanced vs Microsoft Azure Cosmos DB

Compared 5% of the time

Neo4j Graph Database vs Microsoft Azure Cosmos DB

Compared 5% of the time

Amazon DocumentDB vs Microsoft Azure Cosmos DB

Compared 3% of the time

More Microsoft Azure Cosmos DB Competitors

Product Reports

Google Cloud Bigtable

Download Google Cloud Bigtable product report

Microsoft Azure Cosmos DB

Download Microsoft Azure Cosmos DB product report

Also Known As

Google BigTable

Microsoft Azure DocumentDB, MS Azure Cosmos DB

Overview

Google Cloud Bigtable provides large data capacity, fast computation speed, and robust security for efficient data management. It supports seamless querying and integration, making it suitable for users transitioning to the cloud.

Google Cloud Bigtable is a managed service offering that facilitates efficient data handling through its high-performance capabilities and compatibility with other NoSQL databases. It is highly valued for its ability to manage and analyze large datasets, offering features like backup and replication, and is known for being faster than many competitors. Despite its strengths, users express concerns over its pricing, querying complexity, occasional performance lag, and difficulty in choosing between Bigtable and other services. There's also interest in its potential for integration with emerging technologies like LLMs for generative AI applications.

What are the key features of Google Cloud Bigtable?

Large Data Capacity: Supports immense volumes of data, essential for big data initiatives.
Seamless Querying: Queries large datasets with efficiency and speed.
Backup and Replication: Provides data reliability and easy recovery.
Low Latency: Ensures fast response times for queries and operations.
High Computation Speed: Offers rapid data processing capabilities.

What benefits should users look for in reviews?

Data Security: Provides robust security for sensitive data.
Ease of Analysis: Simplifies the process of collating and analyzing data.
Cloud Integration: Compatible with other NoSQL databases, aiding cloud transitions.
Fast Performance: Noted for being quicker than many alternatives.

Industries implement Google Cloud Bigtable for data management tasks such as managing large datasets, resolving production issues, and generating insights through dashboards. It is used in advertising analytics, client data evaluation in Power BI reports, and some automotive clients employ it for specialized needs, integrating business data into Google's ecosystem for efficient analysis.

Google

Microsoft Azure Cosmos DB offers scalable, geo-replicated, multi-model support with high performance and low latency. It provides seamless Microsoft service integration, benefiting those needing flexible NoSQL, real-time analytics, and automatic scaling for diverse data types and quick global access.

Azure Cosmos DB is designed to store, manage, and query large volumes of both unstructured and structured data. Its NoSQL capabilities and global distribution are leveraged by organizations to support activities like IoT data management, business intelligence, and backend databases for web and mobile applications. While its robust security measures and availability are strengths, there are areas for improvement such as query complexity, integration with services like Databricks and MongoDB, documentation clarity, and performance issues. Enhancements in real-time analytics, API compatibility, cross-container joins, and indexing capabilities are sought after. Cost management, optimization tools, and better support for local development also require attention, as do improvements in user interface and advanced AI integration.

What are the key features of Azure Cosmos DB?

Scalability: Effortlessly handles scaling for large workloads.
Geo-replication: Provides global data access with low latency.
Multi-model Support: Compatible with diverse data types.
NoSQL Flexibility: Offers a non-relational database experience.
Real-time Analytics: Facilitates data-driven decision-making.
Automation: Simplifies scaling and maintenance processes.

What benefits can users expect from Cosmos DB?

High Performance: Ensures rapid processing and low latency.
Ease of Use: Features a user-friendly interface enhancing deployment speed.
Seamless Integration: Connects effortlessly with Microsoft services.
Security: Offers robust measures to protect data integrity.
Consistent Availability: Ensures continuous access to data worldwide.

Industries use Azure Cosmos DB to support business intelligence and IoT data management, using its capabilities for backend databases in web and mobile applications. The platform's scalability and real-time analytics benefit sectors like finance, healthcare, and retail, where managing diverse datasets efficiently is critical.

Microsoft

Sample Customers

Cognite, Dow Jones, Loblaw Digital

TomTom, KPMG Australia, Bosch, ASOS, Mercedes Benz, NBA, Zero Friction, Nederlandse Spoorwegen, Kinectify

Google Cloud Bigtable vs. Microsoft Azure Cosmos DB