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Faiss vs Microsoft Azure Cosmos DB comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Sep 29, 2024
 

Categories and Ranking

Faiss
Ranking in Vector Databases
3rd
Average Rating
8.0
Number of Reviews
2
Ranking in other categories
Open Source Databases (14th)
Microsoft Azure Cosmos DB
Ranking in Vector Databases
7th
Average Rating
8.2
Reviews Sentiment
7.2
Number of Reviews
46
Ranking in other categories
Database as a Service (DBaaS) (6th), NoSQL Databases (4th), Managed NoSQL Databases (1st)
 

Featured Reviews

Vasu Bansal - PeerSpot reviewer
May 7, 2024
Provides quick query search and has a big database
I made some vectors for my data set. I added them and got the embeddings from Hugging Face. I made a query key-value pair for Faiss. When you want to do a query search on it, you call Faiss and send your query within it I used Faiss as a basic database. I didn't know what algorithm was being…
Michael Calvin - PeerSpot reviewer
Sep 9, 2024
Easy to integrate, has a shallow learning curve, and scales dynamically
Azure Cosmos DB is quick to adopt with a shallow learning curve. The average user can be operational within hours or days, handling small to medium data volumes. However, optimizing for ultra-high throughput scenarios involves a steeper learning curve, requiring substantial knowledge to master Azure Cosmos DB. Nonetheless, most users can leverage it as their operational data store with minimal effort. Our platform boasts several extensive language model features, particularly around summarization capabilities. We use vector searching in Azure Cosmos DB to facilitate the retrieval of an augmented generation model with our LLM implementation. It's a standard RAG implementation using Azure Cosmos DB. Compared to other options, a key advantage of vector indexing in Azure Cosmos DB is the ability to query documents alongside vectors. This pinpoints the precise information required for RAG in our LLM solution, granting us greater flexibility than vector searching in other Azure services. We integrated the vector database with the Azure OpenAI service for our LLM solution. The Azure AI services were simple to integrate with the vector database. There was a slight learning curve, especially as we were on the private preview of vector searching. This led to some hiccups with our existing database configurations, specifically regarding continuous backup. We couldn't enable continuous backup and vector searching simultaneously. However, this was solely due to our participation in the preview, and I'm confident this issue won't persist in the general availability release. Azure Cosmos DB is fantastic for searching large amounts of data when the data is within a single partition. Over the last two weekends, we ingested over 400 gigabytes of data into our Azure Cosmos DB database and saw no change in querying performance compared to when our database was only 20 gigabytes in size. This is impressive and powerful, but the scope is limited to those partition queries. The first benefit we've seen is increased developer productivity. Azure Cosmos DB is an easy database to work with. Its schema-less nature allows us to iterate quickly on our platform, develop new features, and store the associated data in Azure. Developers find it easy to use, eliminating the need for object-relational mapping tools and other overhead. Geographic replication and the ability to scale geographically is another advantage. This is challenging with other databases, even other NoSQL databases, but Azure Cosmos DB makes it easy. Cost optimization is a major benefit as well. We've been able to run our platform at a fraction of the infrastructure cost our customers incur when integrating with us. This allows us to focus resources on feature development and platform building rather than infrastructure maintenance. Azure Cosmos DB helped reduce the total cost of ownership. We don't need DBAs, system administrators, or typical IT staff to run the infrastructure because we can use Azure Cosmos DB as a platform or a software-as-a-service data storage solution. This makes the total cost of ownership significantly lower than any comparable solution using relational databases or other NoSQL solutions like MongoDB. We enable auto-scaling on all of our Azure Cosmos DB resources, which helps us achieve cost optimizations.

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."
"One of the nice features is the ability to auto-scale"
"It's not a specific feature that I value, but the scalability of this system is the most impressive aspect."
"The graphical representation of data is the most valuable feature of the solution."
"Microsoft Azure Cosmos DB's most valuable feature is latency."
"The best feature is the velocity to make a query."
"The biggest benefit it offers is scalability. It's easier to work with concurrency and updating data."
"The solution is stable."
"With Azure you can start small and grow as you need."
 

Cons

"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."
"There is room for improvement in terms of stability."
"The integration of the on-premise solution with the cloud can be difficult sometimes."
"It would be ideal if we could integrate Cosmos DB with our Databricks. At this point, that's not possible."
"The support tickets are not cheap."
"It doesn't support all databases."
"Azure Cosmos DB could be better for business intelligence and analytical queries."
"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."
"The one thing that I have been working on with Microsoft with regard to this is the ability to easily split partitions and have it do high-performance cross-partition queries. That is the only place where either our data size or our throughput has grown beyond one partition, so being able to do cross-partition queries efficiently would be my number one request."
 

Pricing and Cost Advice

"It is an open-source tool."
"Faiss is an open-source solution."
"The solution is very expensive."
"Its pricing structure is quite flexible."
"Azure is a pay as you go subscription."
"The price of Microsoft Azure Cosmos DB could be a bit lower."
"Cosmos DB is cost-effective when starting but requires careful management."
"The cost is the biggest limitation of this solution."
"Cosmos should be cheaper. We actually intend to stop using it in the near future because the price is too high."
"When we've budgeted for our resources, it's one of the more expensive ones, but it's still not very expensive per month."
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Top Industries

By visitors reading reviews
Computer Software Company
20%
Financial Services Firm
13%
Manufacturing Company
8%
Educational Organization
8%
Computer Software Company
14%
Financial Services Firm
12%
Comms Service Provider
8%
Retailer
7%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
No data available
 

Questions from the Community

What do you like most about Faiss?
I used Faiss as a basic database.
What needs improvement with Faiss?
I didn't know what algorithm was being learned to fetch my query. It would be beneficial if I could set a parameter and see different query mechanisms being run. I can then compare the results to s...
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.
What is your experience regarding pricing and costs for Microsoft Azure Cosmos DB?
The pricing and licensing model was initially difficult to understand, but as soon as I learned what was going on and how it was priced, it was pretty easy. What is more difficult is to understand ...
What needs improvement with Microsoft Azure Cosmos DB?
The one thing that I have been working on with Microsoft about this is the ability to easily split partitions and have it do high-performance cross-partition queries. That is the only place where e...
 

Also Known As

No data available
Microsoft Azure DocumentDB, MS Azure Cosmos DB
 

Learn More

Video not available
 

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: September 2024.
813,266 professionals have used our research since 2012.