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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
13th
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 May 2026, in the Vector Databases category, the mindshare of Faiss is 4.7%, down from 7.6% compared to the previous year. The mindshare of Microsoft Azure Cosmos DB is 6.2%, up from 2.9% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Vector Databases Mindshare Distribution
ProductMindshare (%)
Microsoft Azure Cosmos DB6.2%
Faiss4.7%
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

"The product has better performance and stability compared to one of its competitors."
"I used Faiss as a basic database."
"We love the ability to land data with Cosmos DB easily. Cosmos is native to Azure, so everything works seamlessly with it. You need good data to have good AI, and Cosmos makes it easy to land the data."
"It performs very well, especially under high load where it automatically scales up the RUs, and the main advantage of Microsoft Azure Cosmos DB is its low latency, with response times in milliseconds, making it great for chatbots."
"It's not a specific feature that I value, but the scalability of this system is the most impressive aspect."
"Change feed is a pretty amazing feature. Once you make the changes, they are quickly read for you, and then you also have geo-replication. You can do a lot of things in your region, and the same regions can be replicated all over the world."
"Cosmos DB is stable and easy to use."
"Azure Cosmos DB's graph queries are its most valuable feature. Although I have not yet explored vector search, it's coming to Cosmos DB, and I plan to look into it. Having data in a flat file format in a document database speeds up processes, which is the primary purpose. Additionally, Cosmos DB's use of the Mongo platform makes it intuitive and cost-effective."
"The most valuable features of Microsoft Azure Cosmos DB were the general infrastructure, ease to use, and interface."
"Azure Cosmos DB helped improve the quality of our search results."
 

Cons

"It would be beneficial if I could set a parameter and see different query mechanisms being run."
"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."
"One of the most frustrating aspects of Microsoft Azure Cosmos DB right now is that you can only store one vector per document."
"It would be ideal if we could integrate Cosmos DB with our Databricks. At this point, that's not possible."
"To show this in real time, we need a live connection that automatically updates in response to new records being inserted. This automated updating feature is lacking in Microsoft Azure Cosmos DB compared to Databricks."
"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."
"There should be a simpler way for data migration."
"The main downside I have faced was with hierarchical partitioning in Microsoft Azure Cosmos DB."
"The UI should be improved since if you provide the option to query directly when signing into the Azure portal, it makes no sense if you have such a poor UI for querying that you can't even feed the reports correctly."
"One area of improvement for Cosmos database is the auto-scaling of RUs during high loads. It would be beneficial if the database could automatically scale resources rather than requiring manual adjustments."
 

Pricing and Cost Advice

"Faiss is an open-source solution."
"It is an open-source tool."
"Cosmos DB is cost-effective when starting but requires careful management."
"Cosmos should be cheaper. We actually intend to stop using it in the near future because the price is too high."
"Cosmos DB is a highly cost-optimized solution when used correctly."
"Azure Cosmos DB's pricing is competitive, though there is a need for more personalized pricing models to accommodate small applications without incurring high charges. A suggestion is to implement dynamically adjustable pricing that accounts for various user needs."
"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."
"For the cloud, we don't pay for the license, but for the on-prem versions, we do pay."
"Azure is a pay as you go subscription."
"Cosmos DB gave us three accounts for $400. We pay according to the usage."
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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%
Financial Services Firm
12%
Legal 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.
893,915 professionals have used our research since 2012.