Google Cloud Bigtable vs Microsoft Azure Cosmos DB comparison

Google and Microsoft are both solutions in the Managed NoSQL Databases category. Google is ranked #10 with an average rating of 9.0, while Microsoft is ranked #1 with an average rating of 8.3. Google holds a 5.2% mindshare in MND, compared to Microsoft’s 16.4% mindshare. Additionally, 100% of Google users are willing to recommend the solution, compared to 95% of Microsoft users who would recommend it.

Google Cloud Bigtable

Read 7 Google Cloud Bigtable reviews

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576 Comparison Views

100% willing to recommend

Microsoft Azure Cosmos DB

Read 109 Microsoft Azure Cosmos DB reviews

9,934 Views
3,676 Comparison Views

95% willing to recommend

Google Cloud Bigtable

Microsoft Azure Cosmos DB

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Executive SummaryUpdated on Oct 19, 2025

Google Cloud Bigtable and Microsoft Azure Cosmos DB are competing cloud-based database services offering scalable, managed solutions for handling large amounts of data. Google Cloud Bigtable often has an edge for its seamless pricing structure and robust scalability, while Microsoft Azure Cosmos DB stands out due to its extensive feature set.

Features: Google Cloud Bigtable offers exceptional scalability and speed, supporting big data applications with low latency and high throughput. It is particularly efficient in handling large-scale data processing where speed is crucial. On the other hand, Microsoft Azure Cosmos DB provides a wide array of features, including multiple data models, global distribution options, and built-in indexing, which support versatile data management needs.

Room for Improvement: Google Cloud Bigtable could enhance its functionality by integrating more diverse data model support and improving global accessibility options. Cost predictability in smaller scale applications might also be an area of enhancement. For Microsoft Azure Cosmos DB, improving cost efficiency would be beneficial, as it is sometimes considered expensive. Enhancements in entry-level user support and simplification of complex configurations could further enrich its usability.

Ease of Deployment and Customer Service: Google Cloud Bigtable is known for its straightforward deployment, integrating seamlessly into the Google Cloud ecosystem, with customer support known for reliability. Microsoft Azure Cosmos DB offers a flexible deployment model adaptable across various regions and is supported by robust Microsoft customer service, making it appealing for enterprises with a global presence.

Pricing and ROI: Google Cloud Bigtable is praised for its predictable pricing model, providing a favorable ROI for high-volume data processing scenarios. While Microsoft Azure Cosmos DB may have a higher price tag, it justifies this with its rich feature set, providing enhanced outcomes for businesses leveraging its advanced capabilities, resulting in compelling ROI for those able to capitalize on its features.

To learn more, read our detailed Google Cloud Bigtable vs. Microsoft Azure Cosmos DB Report (Updated: December 2025).

Buyer's Guide

Google Cloud Bigtable vs. Microsoft Azure Cosmos DB

December 2025

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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

8.8

Reviews Sentiment

6.3

Number of Reviews

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 January 2026, in the Managed NoSQL Databases category, the mindshare of Google Cloud Bigtable is 5.2%, down from 6.9% compared to the previous year. The mindshare of Microsoft Azure Cosmos DB is 16.4%, down from 17.2% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Managed NoSQL Databases Market Share Distribution
Product	Market Share (%)
Microsoft Azure Cosmos DB	16.4%
Google Cloud Bigtable	5.2%
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.

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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

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

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

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

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

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

"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."

"The solution is very convenient."

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

"Cosmos DB is a document database that stores data in JSON format for faster retrieval of unstructured data. I personally appreciate the speed, which is significantly better for unstructured data, especially since Cosmos DB had JSON as a data type early on."

"Microsoft Azure Cosmos DB is easy to use and implement for application programmers."

"Cosmos DB has helped us by providing faster response times for everything, which significantly improved our search results quality."

"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 a NoSQL database."

"Their new feature, dynamic data masking, is very cool and useful for us."

"The solution is used because we get faster response times with large data sets than with SQL. It's essential for us because we have half a billion rows, and we need to return them quickly."

"It is easy to use because you don't need to know much about Cosmos DB or have prior experience."

More Microsoft Azure Cosmos DB pros

Cons

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

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

"The cost of this product is too expensive."

"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."

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

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

"If we have a lot of data, doing a real-time vector search is a performance challenge because the search happens over a large dataset. It consumes more time."

"The initial setup was difficult."

"Microsoft Azure Cosmos DB can be improved by providing more fine-grained control over certain aspects, such as connections and threads. There could be more control over how many connections are made."

"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."

"Firstly, having a local development emulator or simulator for Azure Cosmos DB would be beneficial. It would be very handy to have a Docker container that developers can use locally."

"What is missing in Microsoft Azure Cosmos DB is definitely cold storage. We know it's coming, but that's currently what is missing—the possibility to park older data in a cold tier."

"We'd like to avoid full DR replication if possible, as this would result in significant cost savings."

"Microsoft's support services are inadequate, especially during critical incidents."

More Microsoft Azure Cosmos DB cons

Pricing and Cost Advice

Information not available

"I would rate Cosmos DB's cost at seven out of ten, with ten being the highest."

"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."

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

"Cosmos should be cheaper. We actually intend to stop using it in the near future because the price is too high."

"This cost model is beneficial because it allows for cost control by limiting resource units (RUs), which is ideal. However, for our needs, we can't engage with their minimum pricing, which ranges from 100 to 1,000 RUs. At the bare minimum, we need to use 4,000 RUs for a customer. I would like to find a way to gain some advantages from the lowest tier, particularly the ability to scale down if necessary. It would be helpful to have more flexibility in cost management at the lower end."

"The tool is not expensive."

"Most customers like the flexibility of the pricing model, and it has not been an issue. They can start small, and the cost grows with adoption, allowing efficient management of the budget. Its pricing model has not been a concern at all for any of our customers. They understand it. It is simple enough to understand. Oftentimes, it is hard to forecast the RUs, but, in general, it has been fine."

"Its cost is transparent. Pricing depends on the transaction and data size, but overall, it is cheaper compared to hosting it on your corporate network due to other factors like power consumption."

More Microsoft Azure Cosmos DB pricing and cost advice

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Top Industries

By visitors reading reviews

University

16%

Financial Services Firm

Computer Software Company

Manufacturing Company

Legal Firm

12%

Financial Services Firm

11%

Comms Service Provider

Manufacturing Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

No data available

By reviewers
Company Size	Count
Small Business	33
Midsize Enterprise	21
Large Enterprise	58

Questions from the Community

What do you like most about Google Cloud Bigtable?

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

See all answers

What needs improvement with Google Cloud Bigtable?

See all answers

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.

See all answers

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.

See all answers

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...

See all answers

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...

See all answers

Comparisons

MongoDB Enterprise Advanced vs Google Cloud Bigtable

Compared 22% of the time

Amazon DynamoDB vs Google Cloud Bigtable

Compared 21% of the time

Amazon Keyspaces vs Google Cloud Bigtable

Compared 14% of the time

Amazon Timestream vs Google Cloud Bigtable

Compared 11% of the time

More Google Cloud Bigtable Competitors

Pinecone vs Microsoft Azure Cosmos DB

Compared 8% of the time

Chroma vs Microsoft Azure Cosmos DB

Compared 8% of the time

Neo4j Graph Database vs Microsoft Azure Cosmos DB

Compared 7% of the time

MongoDB Enterprise Advanced vs Microsoft Azure Cosmos DB

Compared 6% of the time

Milvus vs Microsoft Azure Cosmos DB

Compared 3% of the time

More Microsoft Azure Cosmos DB Competitors

Product Reports

Buyer's Guide

Managed NoSQL Databases

December 2025

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Buyer's Guide

Microsoft Azure Cosmos DB

December 2025

Download Microsoft Azure Cosmos DB product report

Also Known As

No data available

Microsoft Azure DocumentDB, MS Azure Cosmos DB

Overview

Cloud Bigtable is Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.

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

Buyer's Guide

Google Cloud Bigtable vs. Microsoft Azure Cosmos DB

December 2025

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