Microsoft Azure Cosmos DB Reviews

We use Cosmos DB as a database for the cache mechanism. We have a product integrating e-commerce platforms with backend ERPs, pulling merchandising data. We maintain millions of products in the ERP and store them in Cosmos DB in document format. When a query comes from the e-commerce platform, it goes directly to Cosmos.  

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.

It can query large amounts of data efficiently, depending on how you write the queries. This is a Document Database, and the system needs to read the whole document. If that is correctly clustered, then it will be faster, but if the developer makes some mistakes, it won't be optimized. 

The most valuable feature is the data writing process, where we write data into batch segments. The built-in vector database is helpful. There's one vector for the product and another for the price. I don't have much experience with vectors because we use Cosmos as a cache DB. You won't see any major challenges when you use it as a more significant enterprise application. I would rate the vector database's interoperability with other solutions an eight out of 10. 

The main area of improvement is the cost, as the expense is high. Also, when writing processes into Cosmos, sometimes the threshold is met, which can be a problem if developers have not written the code properly, limiting calls to five thousand. These aspects need addressing.

I have been using Cosmos DB for three years.

I would rate the interoperability of the vector database with other solutions as eight out of ten. It's good, but the performance depends on how well queries are written.

We compared MongoDB and Cosmos DB. Cosmos DB is easier to configure, and our team is already familiar with managing it, providing an advantage.

The initial setup was straightforward, with no major challenges. We onboarded the team in no more than three days. 

The cost of using Cosmos DB is high, which sometimes raises concerns from clients regarding the increased solution cost. While it has helped decrease the overall cost of ownership, the specific figures are not readily available.

I would rate Azure Cosmos DB eight out of 10. The solution is variously challenging but manageable once the team is familiar.

Our primary use case for Azure Cosmos DB is mainly as a Document DB and vector DB.

Azure Cosmos DB is very easy to use. We do not have to spend a lot of time on its optimization.

There is a lot of reference code we can use. It is very easy. We could grab some code to interact with the database.

We have integrated the vector database with some of the IoT applications and recently, some AI-related topics because it is a cloud-native service. Our company offers professional services to help customers bring their own applications to the cloud. The cost and performance are some of the main benefits of the vector database. 

The integration of the vector database with Azure AI services is great. In most applications right now, we use the logic of vector search and the traditional way of using full-text search. It is easier for the applications to get those search results.

I am more on the presales side. Most of the time, we do a quick demo for our customers. We only spend about fifteen minutes building a simple application with the RAG functionality with the customer's own data. That is very impressive.

It provides good SLAs and requires less effort in maintenance.

The global synchronization feature of Azure Cosmos DB stands out as the most valuable for me. It is a reliable and consistent storage solution, suitable for various data types. It is always available. Additionally, it is cost-effective.

I do not have any specific suggestions for improvements at the moment. However, having more AI capabilities in the future would be beneficial.

I have been using Azure Cosmos DB for three or four years.

The stability of Azure Cosmos DB is very nice, with features like cross-region synchronization that allows fast and reliable performance.

The latency and availability of Azure Cosmos DB are very nice. There are cross-region synchronization features. The speed is very fast.

Azure Cosmos DB scales well, both in terms of capacity and performance. You can adjust the Request Units (RUs) as needed, and the cross-region synchronization allows easy scaling across different locations.

As compared to a traditional RDBMS, Azure Cosmos DB’s dynamic scaling decreases an organization’s overhead costs by half.

We previously used Redis and Postgres for vector databases before they were supported in Azure Cosmos DB. In the beginning, the vector database was not supported with Azure Cosmos DB, so we had to use the Redis or Postgres database, which was expensive. Azure Cosmos DB is cheaper.

Our company offers consulting services for Microsoft-related products. This is one of the reasons for recommending Azure Cosmos DB, but sometimes our customers use MongoDB and other solutions.

The initial setup of Azure Cosmos DB was easy. During the migration or implementation of Azure Cosmos DB, there are sometimes some incompatibility issues, but they are minor issues.

It was easy for our team to use. It took them one week to know the system and work with it. It takes our team members about four weeks to earn their certification for Azure Cosmos DB. There is a special certification for Azure Cosmos DB.

It is cost-effective. They offer two pricing models. One is the serverless model and the other one is the vCore model that allows provisioning the resources as necessary. For our pilot projects, we can utilize the serverless model, monitor the usage, and adjust resources as needed.

I would rate Azure Cosmos DB an eight out of ten. There is room for growth, but Microsoft is constantly releasing new features and moving very fast.

The main use cases involve creating some kind of dashboards in near real-time. Our use cases focus on manufacturing, where we used Microsoft Azure Cosmos DB to maintain data for the very intensive manufacturing processes. In the end, we performed data analysis on the operational processes in manufacturing.

Big data, along with data analysis, is one of the valuable features. We are able to have insights into how to make improvements in the processes for operational people.

Microsoft Azure Cosmos DB is a Microsoft solution specifically, but we can develop with different developer kits for different databases.

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. We reached the performance required using MongoDB instead of Microsoft Azure Cosmos DB.

I used it for one year or less than one year.

Microsoft Azure Cosmos DB has good performance and latency. We only faced performance issues with the data segregation query.

I would rate Microsoft Azure Cosmos DB a nine out of ten for the capability to scale workloads. 

On a scale of one to ten, I would rate customer service a seven. For example, when I created a ticket with them, they gave us feedback very often, even each week. This went on for four or five months, but they did not solve the problem. They only gave feedback, and in the end, it did not resolve the problem.

Neutral

We changed from using Microsoft Azure Cosmos DB to MongoDB because Microsoft Azure Cosmos DB did not give us the correct performance for certain data segregation, so we replaced it with MongoDB.

People who helped us implement MongoDB were more specialized or had more expertise than Microsoft people.

The setup of Microsoft Azure Cosmos DB was very easy. It took us a few weeks.

We received help from Microsoft directly. They helped us to get started with it. 

Our use case was a failure with Microsoft Azure Cosmos DB, and we do not have any other opportunity to use Microsoft Azure Cosmos DB.

Its pricing is not bad. It is good. 

We have a contract with Microsoft to use their technology. In my opinion, Microsoft Azure Cosmos DB is a good option for the total cost of ownership.

I would rate Microsoft Azure Cosmos DB as seven out of ten.

Our primary use case is mirroring the data for reporting.

Cosmos DB has helped us by providing faster response times for everything, which significantly improved our search results quality.

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.

I use Azure AI services, including cognitive services and OCR. I recently built a chatbot using the model. Cosmos DB integrates well with other apps. 

There are no specific areas I believe need improvement as I am happy with what I am getting currently. However, I am open to new features in future versions, like possibly integrating AI features natively into Cosmos DB. Any improvement would be beneficial.

I have been using Azure Cosmos DB for more than five years.

I have not encountered any issues related to the stability of Cosmos DB. The challenge does not lie in the technical aspect of Cosmos DB but in the non-technical aspects.

Cosmos DB has impressive scalability. We have been able to scale workloads as needed during peak hours without any issues, effectively meeting our expectations.

The initial setup was quite quick, taking only a few days for the team to be onboarded with Cosmos DB. The primary challenge was non-technical.

The implementation involved just the normal onboarding process for any human resource.

Our organization's total cost of ownership has been reduced by 20 percent due to the backend data mirroring for setting up the repository for reporting purposes. Dynamic scaling has also helped decrease our organization's overhead cost by automating the scaling process and reducing the need for human intervention.

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 rate Azure Cosmos DB eight out of 10. There is always room for improvement, and the company could develop new features that could make it even better, but I am very satisfied with the current performance.

Our primary use case for Azure Cosmos DB is storing information for our large accounting application, which integrates several sites on SharePoint Online. We use event programming to store all calls in Cosmos DB, so we can redo them and have them persist in the database.

Cosmos DB has helped our organization handle large amounts of data. For example, we had a customer who collected data from 100,000 sites, and we increased that to a million without significantly increasing search query time. We can now search in nearly real-time, which has been crucial, especially with AI workloads.

Azure Cosmos DB's resiliency is valuable. It is available in every Azure region, allowing quick information storage and retrieval. We can partition it to improve indexing, enabling us to retrieve information and recreate website content quickly. 

It's easy to use for our use case because we use it to store and retrieve information, but it will be more complex if you are configuring a Redis cache or something similar. 

Cosmos DB also integrates well with Azure app services and functions, allowing us to scale by efficiently storing calls. Its ability to scale workloads is impressive, and features like partitioning and Azure replication enhance its scalability. Its interoperability with solutions is better than that of other NoSQL databases we assessed. It's native to Azure and integrates with the networks and security.

Cosmos DB should continue evolving in AI features. We expect Cosmos DB to lead on that. There is potential for improved security features, which is important for data storage, especially for Dell Technologies. We must ensure data security remains the top priority.

I have been using Cosmos DB for over eight years, starting from its preview release.

There have been no notable issues with the stability of Cosmos DB. Any problems encountered were not directly related to Cosmos DB but perhaps coding errors or usage methods.

Cosmos DB scales workloads impressively through features such as partitioning and Azure replication. Its design as a NoSQL database has helped us transition from traditional SQL, impacting costs positively.

We previously used MongoDB, but Cosmos DB's integration within Azure provided better network and security options, making it a preferred choice. I've worked on Microsoft technologies since the beginning, and I love how Microsoft solutions are integrated. Everything works together securely, and moving from one technology to another is simple.

The initial setup was easy. The transition from MongoDB was seamless as Cosmos DB has improved upon existing NoSQL structures without reinventing them.

Cosmos DB has decreased our organization's total cost of ownership, particularly with decreasing overhead costs due to its scalable features.

We prioritized fine-tuning operations to optimize costs, and Cosmos DB’s pricing model allows room for improvement. We are assessing its use in other areas to potentially eliminate third-party solutions.

I rate Microsoft Azure Cosmos DB nine out of 10. To avoid migration challenges, data storage methods in Cosmos DB should be carefully considered.

We primarily use Microsoft Azure Cosmos DB as a transactional data store and for some event-driven applications. We utilize the change feed, and the function app triggers quite a bit. MPerks, our customer loyalty application, uses it. It has become our go-to database, and we hardly touch SQL Server for new stuff.

Our developers find Microsoft Azure Cosmos DB easy to use and more scalable. The whole cloud model of only paying for what you use fits our organization well. 

The features most valuable to us in Microsoft Azure Cosmos DB are the auto scale and change feed. These features allow us to do some operations that are not possible with SQL Server. It is super configurable, allowing us to pick and choose the different Cosmos databases we need, whether or not dynamic scaling is the right thing for that workload.

Latency and availability are incredible. Given that our data is partitioned and indexed correctly, we can run queries and get results in less than five milliseconds. This has resulted in happier customers.

Cosmos is super-easy to use. It adopts a whole document database strategy with no relational data, so what you see is what you get. It's straightforward to understand, and you no longer need to worry about entity diagrams.

One of our biggest pain points is the backup and restore functionality needs improvement. They've gotten a little better in this area. SQL Server's long-term retention is amazing, and you can restore data from years ago. You need to open a support Microsoft ticket to restore your Cosmos DB backup, and it comes in on a different Cosmos account. It's just kind of a headache to restore data.

CosmosDB's ability to search through large amounts of data isn't great. It kills the RUs if you're using the transactional store. We use Synapse Analytics for our more analytical workloads. We love Synapse for that purpose.

I have been using Microsoft Azure Cosmos DB for about six years.

There are no critical scalability issues with Microsoft Azure Cosmos DB. It scales well with RUs, and it is never an issue for us. Our issues usually lie more on the application side.

The support experience has been pretty good, and I don't have a lot of complaints.

Positive

Previously, we used SQL Server. Microsoft Azure Cosmos DB was chosen because it is the go-to document data store, and our developers are familiar with SQL syntax.

New developers are able to get jumpstarted on Microsoft Azure Cosmos DB quickly. Although we learned some lessons on how to structure and partition data, the initial setup was not problematic.

I can't specify the exact ROI, but Microsoft Azure Cosmos DB has decreased our total cost of ownership.

We pay for what we use, with the flexibility to reserve our use. Autoscaling is a premium option, but it helps when our database isn't in high demand. It provides flexibility in configuring our RUs, whether we want to do it at the database or container level. We have lots of options to configure and pay for the solution. 

We evaluated AWS solutions, but ultimately chose Microsoft Azure Cosmos DB.

I would rate Microsoft Azure Cosmos DB a nine out of 10. Both Microsoft Azure Cosmos DB and Cosmos SQL DB are familiar to our developers who come from a SQL Server background.

We use Microsoft Azure Cosmos DB to store document-type data, graph data, and key-value type data. It is a globally distributed database, which we mainly utilize to store document-type JSON data. 

In my project, I work with core SQL-type queries. Using the API, we are storing JSON data in Microsoft Azure Cosmos DB with a database and container-level architecture. This involves storing items using a partition key for optimized query performance.

We get data from BLOB storage. After some processing, we are storing it in the JSON format in Microsoft Azure Cosmos DB.

Microsoft Azure Cosmos DB automatically indexes documents. By indexing every field in the document, it is easy to get fast performance to retrieve the records. While fetching, it fetches only specific fields required for further processing, which makes it efficient. Fetching all the fields from a document takes more time.

Storing data with a partition key makes data fetching easier and faster.

Microsoft Azure Cosmos DB helps in fetching data faster. There is a single-digit millisecond response to fetch those records.

Microsoft Azure Cosmos DB supports scalability. At a peak time, it will automatically scale the RUs. When there is less data, it will decrease them.

In Microsoft Azure Cosmos DB, one valuable feature is its ability to store data in multiple regions. If one region fails, it automatically switches to a healthy region, ensuring minimal latency and disaster recovery without impacting data latency in applications. It scales automatically based on query performance and peak traffic.

Microsoft Azure Cosmos DB is very easy to use.

Currently, I have no suggestions for enhancement or new implementations in Microsoft Azure Cosmos DB. However, the cost can sometimes be high, especially during cross-partition queries with large data amounts.

I have been using Microsoft Azure Cosmos DB for the last two years.

Microsoft Azure Cosmos DB provides high availability with 99.9% reliability. When we store documents in Microsoft Azure Cosmos DB, it stores them in multiple regions, not only at specific regions. If one region fails, it automatically switches to a healthy region. 

There is low latency. The partition key helps achieve low latency by ensuring data is stored and accessed efficiently.

Microsoft Azure Cosmos DB offers both automatic and manual scaling. The automatic scaling feature adjusts RUs based on peak demands, which helps manage workloads efficiently. The dynamic scaling feature has helped reduce overhead costs by automatically managing resource utilization.

Our application is being used globally, and we have ten members in our team.

When I joined the organization, Microsoft Azure Cosmos DB was already in use. I have not worked with other NoSQL databases before.

It is a Platform as a Service. It was already implemented before I joined.

I started working with it in the first month. I had the support of the senior developers of the time.

It does not require maintenance from our end.

We monitor the cost daily through Azure Monitor to evaluate how much it is costing for documents, thereby keeping track of the return on investment.

Microsoft Azure Cosmos DB pricing is based on RUs. Reading 1 KB document costs one RU, whereas writing one document costs five RUs. Pricing for querying depends on the complexity of the query. If you increase the document size, it will automatically increase the RU cost.

I would recommend this solution. For e-commerce applications, it is more beneficial because it can store semi-structured data. It is the best option if you want to get data quickly because it organizes the data in a good way. When a region fails, it automatically switches to a healthy region. It has backup storage, and it scales automatically based on the peak time or low time.

I would rate Microsoft Azure Cosmos DB an eight out of ten. It is a good solution, but the cost can increase with cross-partition queries due to data distribution.

Our corporate mission is to help companies achieve more with their data, which often means unifying your data. We have a SaaS solution and have built a Copilot with Copilot Studio on top, as well as some of the Azure AI services, which is now Foundry. We are starting to use it to allow people to use natural language to ask questions of their data. We are early in our journey, but I suspect it will work well for us.

By incorporating Cosmos DB into our Azure ecosystem, we have streamlined costs and improved efficiency. The integration has allowed us to manage Cosmos alongside our other services, providing a comprehensive view of resources. The inclusion of advanced capabilities has been beneficial, positively impacting our internal operations and the services we offer to clients.

We're early in our journey, but I believe it will improve the quality of our search results. We're having a lot of success with Copilot and are excited to see how it'll work in a traditional sense as well. We're in analytics, so we work with a lot of massive data and look at tens of millions of rows. We haven't had any capacity challenges or thresholds. We started with small data, so having a tool that will grow with you is great. 

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.

The recently added ability to mirror to Fabric has been beneficial. Cosmos DB enables us to handle transactional and analytical workloads in the same database. 

Cosmos DB is easy to use. You can set up a database with a couple of clicks, and it's simple to scale it up and down based on your needs. Within Azure, the Explorer UX has been great for us, too. You don't have to install another tool to run a quick query or explore some data. Additionally, the ability to estimate your Cosmos costs through the portal and manage features has been useful.

Like most database tools, it takes some time to understand. If you come from SQL or even from the Mongo world, many concepts will be familiar to you. While it takes some learning and expertise, it's not a large hill to climb. You must learn the advanced capabilities, but they make your solutions more powerful.

The vector database requires an additional engineering step to move the data from a transactional database to a vector store so that you can query it and use it in AI. However, because the vector capabilities are built in, it saved us engineering time and allowed us to get our solution out faster. 

We would like to see advancements in AI with the ability to benchmark vector search capabilities, ensuring it answers questions accurately. During our initial implementation, we faced challenges with indexing and sorting, which are natively available in other offerings but required specific configurations in Cosmos.

We have used Cosmos SQL for more than five years.

There have been no challenges with Cosmos DB's stability. We have both our SaaS app and the analytical side running without throttling issues.

The scalability is great, both horizontally and vertically.

In the past, we've worked with traditional SQL Server and MongoDB. However, Cosmos being native to Azure and the seamless integration prompted our switch.

The onboarding process was relatively quick for us. We were up and running within two weeks, including a pilot test.

The dynamic scaling during peak times has been crucial in cost management.

The integration of Cosmos with our other Azure services allows us to manage costs proactively. The built-in capabilities help control costs in line with our growth expectations through the portal. 

I rate Cosmos DB eight out of 10.