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Faiss 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

Faiss
Ranking in Vector Databases
12th
Average Rating
8.0
Reviews Sentiment
3.3
Number of Reviews
3
Ranking in other categories
Open Source Databases (12th)
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 April 2026, in the Vector Databases category, the mindshare of Faiss is 5.0%, down from 9.2% compared to the previous year. The mindshare of Microsoft Azure Cosmos DB is 5.9%, up from 2.5% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Vector Databases Mindshare Distribution
ProductMindshare (%)
Microsoft Azure Cosmos DB5.9%
Faiss5.0%
Other89.1%
Vector Databases
 

Featured Reviews

Kalindu Sekarage - PeerSpot reviewer
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.
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

"I used Faiss as a basic database."
"The product has better performance and stability compared to one of its competitors."
"Microsoft Azure Cosmos DB has helped decrease my company's total cost of ownership; it's easier because we have less to maintain, we're not trying to set up multiple SQL servers with replication and everything, and centralization helps a lot."
"Cosmos is preferred because of its speed, robustness, and utilization. We have all the merchandising information in Cosmos DB, which provides concrete and optimized data when searching for new products on the site. It is faster than other relational databases."
"Their new feature, dynamic data masking, is very cool and useful for us."
"I would recommend Microsoft Azure Cosmos DB to other users without hesitation."
"I like the scalability. There aren't any constraints for posting in the geolocation. I also like the SQL architecture."
"Cosmos DB performs exceptionally well and has not caused any issues that necessitate adjustments in nodes for improved performance."
"For modern applications, I would recommend Microsoft Azure Cosmos DB."
"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."
 

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 could be more accessible for handling larger data sets."
"It would be beneficial if I could set a parameter and see different query mechanisms being run."
"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."
"The only area Microsoft Azure Cosmos DB can improve on is its documentation; while it is solid and very useful, enhancements in the indexing documentation would help users save costs and make it more cost-effective."
"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."
"The model with autoscaling for RU is complicated to optimize RU consumption."
"The solution’s pricing could be improved."
"At this stage, we would like more enterprise support. We use MongoDB a lot, and we're trying to get rid of MongoDB. So, I would like to see more features in the Cosmos DB API for MongoDB space."
"It would be nice to have more options to ingest the data, for example, more file options or more search options. Currently, you can use JSON, but if there were other file types you can use for data ingestion, that would be nice."
"A minor improvement would be enabling batch operations through the UI. Currently, to delete all documents in a collection, we must use an API, which some of my team finds inconvenient for admin tasks."
 

Pricing and Cost Advice

"Faiss is an open-source solution."
"It is an open-source tool."
"The RU's use case determines our license fees."
"Because of the lack of understanding about RUs, the costs become unpredictable. It sometimes goes over the budget."
"Cosmos DB's pricing structure has significantly improved in recent months, both in terms of its pricing model and how charges are calculated."
"Azure is a pay as you go subscription."
"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."
"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 Cosmos DB pricing model, initially quite complicated, became clear after consulting with Azure Advisor, allowing us to proceed with confidence."
"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."
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Top Industries

By visitors reading reviews
Financial Services Firm
15%
Computer Software Company
11%
Comms Service Provider
9%
Manufacturing Company
9%
Legal Firm
11%
Financial Services Firm
11%
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 do you like most about Faiss?
I used Faiss as a basic database.
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...
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...
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...
What is your primary use case for Microsoft Azure Cosmos DB?
We have a very large team of developers who develop a solution for our customers. In the part where they need some infrastructure on Microsoft Azure, we deploy entire environments of different type...
 

Also Known As

No data available
Microsoft Azure DocumentDB, MS Azure Cosmos DB
 

Overview

 

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
Find out what your peers are saying about Faiss vs. Microsoft Azure Cosmos DB and other solutions. Updated: April 2026.
891,869 professionals have used our research since 2012.