Faiss vs Microsoft Azure Cosmos DB comparison

Meta and Microsoft are both solutions in the Vector Databases category. Meta is ranked #5 with an average rating of 8.0, while Microsoft is ranked #1 with an average rating of 8.3. Meta holds a 5.1% mindshare in VD, compared to Microsoft’s 5.8% mindshare. Additionally, 100% of Meta users are willing to recommend the solution, compared to 95% of Microsoft users who would recommend it.

Faiss

Read 3 Faiss reviews

3,588 Views
3,265 Comparison Views

100% willing to recommend

Microsoft Azure Cosmos DB

Read 109 Microsoft Azure Cosmos DB reviews

9,934 Views
4,272 Comparison Views

95% willing to recommend

Faiss

Microsoft Azure Cosmos DB

Comparison Buyer's Guide

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Executive SummaryUpdated on Jan 25, 2026

Microsoft Azure Cosmos DB and Faiss compete in the database and search solution category. Microsoft Azure Cosmos DB appears to have an upper hand in terms of pricing transparency and support infrastructure.

Features: Microsoft Azure Cosmos DB offers global distribution, multi-model support, and comprehensive indexing capabilities which cater to scalable applications with low latency. Faiss focuses on efficient similarity search, clustering, and high-performance data operations that are ideal for resource-heavy tasks.

Ease of Deployment and Customer Service: Microsoft Azure Cosmos DB integrates seamlessly with the Azure ecosystem, offering detailed documentation and extensive customer support, simplifying deployment for businesses. Faiss, as an open-source library, requires more technical engagement for deployment and features a community-supported model, adding flexibility for technical teams.

Pricing and ROI: Microsoft Azure Cosmos DB uses a pay-as-you-go model enhancing transparency and cost predictability, which offers potential ROI benefits for large applications. Faiss has minimal initial setup costs due to its open-source nature but may incur higher operational expenses dependent on technical expertise.

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

Buyer's Guide

Faiss 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

Faiss

Ranking in Vector Databases

5th

Average Rating

8.0

Reviews Sentiment

3.3

Number of Reviews

Ranking in other categories

Open Source Databases (10th)

Microsoft Azure Cosmos DB

Ranking in Vector 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), Managed NoSQL Databases (1st)

Mindshare comparison

As of January 2026, in the Vector Databases category, the mindshare of Faiss is 5.1%, down from 12.3% compared to the previous year. The mindshare of Microsoft Azure Cosmos DB is 5.8%, up from 1.3% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Vector Databases Market Share Distribution
Product	Market Share (%)
Microsoft Azure Cosmos DB	5.8%
Faiss	5.1%
Other	89.1%

Vector Databases

Featured Reviews

Kalindu Sekarage

Senior Software Engineer

Integration improves accuracy and supports token-level embedding

The best features FAISS offers for my team include seamless integration with Colbert and the ability to use FAISS via the Ragatouille framework, which is tailor-made for using the Colbert model. Feature-wise, FAISS allows for more accurate result retrieval, and retrieval speed is also good when comparing the index size. Regarding features, I also emphasize that the usability of FAISS is very seamless, particularly its integration with Colbert and Ragatouille. FAISS has positively impacted my organization by helping us increase the accuracy of retrieval documents; when we store documents in token-level embedding, the accuracy will be high. Additionally, we do not need any external server to host FAISS, allowing us to integrate it with our backend framework, making it a very flexible framework.

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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

"I used Faiss as a basic database."

"The product has better performance and stability compared to one of its competitors."

"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."

"Our customer is very satisfied with it."

"The fact that scalability can be achieved by simply configuring availability zones is very attractive."

"It is a scalable product."

"The most valuable features for our organization with Azure Cosmos DB are multi-master capability for applications, automatic failover ensuring high availability, scalability, support for multiple data models, and low-latency access."

"The most valuable feature of Microsoft Azure Cosmos DB is its ability to handle concurrency and consistency."

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

"Our team has found the vCore index to be one of the most valuable features. We have tokenized and vectorized our entire database and stored this data in MongoDB collections with a vCore index, which works like magic for keyword selection."

More Microsoft Azure Cosmos DB pros

Cons

"One of the drawbacks of Faiss is that it works only in-memory. If it could provide separate persistent storage without relying on in-memory, it would reduce the overhead."

"It would be beneficial if I could set a parameter and see different query mechanisms being run."

"It could be more accessible for handling larger data sets."

"I have been a devoted Microsoft fan, but Redis DB's memory caching capabilities are really making progress. Even if Cosmos DB is continuously improving and is quite advanced in the field of internal memory optimization, I would still recommend Redis DB to a customer."

"There aren't any specific areas that need improvement, but if there were a way to achieve the right cosine similarity score without extensive testing, that would be very beneficial."

"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 cost is a concern. Microsoft Azure Cosmos DB did not decrease our total cost of ownership. From the standpoint of the old way of doing DBA operations, it did, but our cloud cost increased significantly."

"The UI needs enhancement. Unlike SQL, Cosmos DB's UI is not as straightforward, making it a bit challenging to use efficiently."

"We had some performance issues with a data segregation query. We worked closely with Microsoft to solve the problem of performance where, for example, one query had a delay of almost two or three minutes for this one use case. Microsoft tried to improve the product, but in the end, the solution was to change to MongoDB. MongoDB had better performance."

"The model with autoscaling for RU is complicated to optimize RU consumption."

"There is room for improvement in Microsoft's maintenance aspect. For example, we had a major incident at the end of December where the entire South Central region was down for our application, causing many problems due to a lack of access to the database."

More Microsoft Azure Cosmos DB cons

Pricing and Cost Advice

"Faiss is an open-source solution."

"It is an open-source tool."

"The price of Microsoft Azure Cosmos DB could be a bit lower."

"Its pricing structure is quite flexible."

"Our experience with the pricing and setup cost is that it aligns with what we expect based on the pricing we see. However, I would absolutely like it to be less if possible."

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

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

"The cost is the biggest limitation of this solution."

"It seems to have helped significantly. We were using a different database system previously, and one of the reasons for acquiring Microsoft Azure Cosmos DB was cost."

"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."

More Microsoft Azure Cosmos DB pricing and cost advice

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Top Industries

By visitors reading reviews

Financial Services Firm

15%

Computer Software Company

15%

Manufacturing Company

Comms Service Provider

Legal Firm

13%

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 Faiss?

I used Faiss as a basic database.

See all answers

What is your experience regarding pricing and costs for Faiss?

I did not purchase FAISS through the AWS Marketplace because FAISS is an open-source product. My experience with pricing, setup cost, and licensing is straightforward, as there is no cost for acqui...

See all answers

What needs improvement with Faiss?

I currently do not think there is anything to be improved based on our experience, as Faiss performs as we expected for our workflow. I would like to see improvement in the fact that FAISS currentl...

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

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Compared 10% of the time

Supabase Vector vs Faiss

Compared 8% of the time

Milvus vs Faiss

Compared 8% of the time

MongoDB Enterprise Advanced vs Faiss

Compared 7% of the time

MySQL vs Faiss

Compared 5% of the time

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Compared 6% of the time

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Product Reports

Buyer's Guide

Open Source Databases

January 2026

Download Faiss product report

Buyer's Guide

Microsoft Azure Cosmos DB

January 2026

Download Microsoft Azure Cosmos DB product report

Also Known As

No data available

Microsoft Azure DocumentDB, MS Azure Cosmos DB

Overview

Faiss is a powerful library for efficient similarity search and nearest neighbor retrieval in large-scale datasets. It is widely used in image and text processing, recommendation systems, and natural language processing.

Users appreciate its speed, scalability, and ability to handle high-dimensional data effectively. Faiss also offers easy integration and extensive support for different programming languages.

Its valuable features include efficient search capabilities, support for large-scale datasets, various similarity measures, easy integration, and comprehensive documentation and community support.

Sample Customers

1. Facebook 2. Airbnb 3. Pinterest 4. Twitter 5. Microsoft 6. Uber 7. LinkedIn 8. Netflix 9. Spotify 10. Adobe 11. eBay 12. Dropbox 13. Yelp 14. Salesforce 15. IBM 16. Intel 17. Nvidia 18. Qualcomm 19. Samsung 20. Sony 21. Tencent 22. Alibaba 23. Baidu 24. JD.com 25. Rakuten 26. Zillow 27. Booking.com 28. Expedia 29. TripAdvisor 30. Rakuten 31. Rakuten Viber 32. Rakuten Ichiba

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

Buyer's Guide

Faiss vs. Microsoft Azure Cosmos DB

December 2025

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