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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

"I used Faiss as a basic database."
"The product has better performance and stability compared to one of its competitors."
"The best feature of Microsoft Azure Cosmos DB is API access, which makes it very easy to interact with the database without needing to write queries."
"What I appreciate most are the latency and the access, which are guaranteed by the tool, which is really impressive."
"I like the scalability. There aren't any constraints for posting in the geolocation. I also like the SQL architecture."
"The most valuable features of Microsoft Azure Cosmos DB were the general infrastructure, ease to use, and interface."
"What I appreciate most are the latency and the access, which are guaranteed by the tool, which is really impressive."
"The value that it has added to my AI or search workloads is that I think it's optimized that process and made it easier; we have a lot of unstructured data coming from different dissimilar systems and different data sources, so correlating those things together and making sense of it has been very beneficial."
"The dynamic autoscale or serverless model of Microsoft Azure Cosmos DB has indeed helped reduce our costs and operational effort by allowing us to scale horizontally in a straightforward manner according to our needs."
"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."
 

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."
"The tool's pricing is expensive."
"It would be nice to have more options to ingest the data, for example, more file options or more search options."
"The challenge for us is always scale."
"The challenge for us is always scale."
"A way Microsoft Azure Cosmos DB could be improved is through the introduction of an access control list on a row and on a specific field within the document, rather than relying on application-level coding to manage different access control lists."
"I hope they improve the service. Before last year, improvements on Cosmos DB were very slow."
"There are no particular factors that need improvement. There is a little bit of a learning curve with scaling workloads, but it works smoothly."
"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."
 

Pricing and Cost Advice

"Faiss is an open-source solution."
"It is an open-source tool."
"You need to understand exactly the details of how the pricing works technically to stay within reasonable pricing."
"Its pricing is higher compared to solutions like Aerospike. However, it is justified because of the out-of-the-box features that are provided. The availability and resiliency that we have make it worth the price."
"The pricing for Cosmos DB has improved, particularly with the new pricing for Autoscale."
"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."
"Azure is a pay as you go subscription."
"Pricing, at times, is not super clear because they use the request unit (RU) model. To manage not just Azure Cosmos DB but what you are receiving for the dollars paid is not easy. It is very abstract. They could do a better job of connecting Azure Cosmos DB with the value or some variation of that."
"If you are a small organization or startup building from scratch without the Microsoft Startup Founder Club support, it could be expensive."
"Cosmos DB is expensive, and the RU-based pricing model is confusing. Although they have a serverless layer, there are deficiencies in what I can define and assign to a database. Estimating infrastructure needs is not straightforward, making it challenging to manage costs."
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Top Industries

By visitors reading reviews
Financial Services Firm
14%
Computer Software Company
11%
Comms Service Provider
9%
Manufacturing Company
9%
Financial Services Firm
12%
Legal Firm
12%
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.
893,244 professionals have used our research since 2012.