Microsoft Azure Cosmos DB Reviews

We use Microsoft Azure Cosmos DB in our loyalty platform, which is based on our proprietary technology, Synapse LX. In loyalty, we need to enroll, score, and deliver rewards communications in near real-time. There are significant volume spikes in those activities, so our use case is to support the writing of information into our database. Cosmos DB is a no-SQL database that allows us to scale quickly and handle large volume spikes. It allows us to auto or manually scale in many different ways. It gives us much flexibility to handle that requirement and ensure we deliver the right customer experiences.

Cosmos DB is not difficult to use, but like anything, it requires careful planning and consideration of use cases. This is especially important when planning to implement it. From an optimization perspective, Microsoft has made significant efforts in the past 12 to 18 months to facilitate changes after initial implementation and optimize cost.

Cosmos DB provides excellent search result quality. Since implementing it, we have not encountered any issues with our searches.

After deploying Cosmos DB, we initially experienced some performance gains, followed by additional benefits that required a learning curve regarding tuning and configuration. As our understanding deepened, we were able to optimize it further.

In the last three months, Cosmos DB has helped reduce our total cost of ownership. Microsoft recently implemented a feature that allows us to achieve savings of up to 50 percent.

The most valuable aspect of Cosmos DB is its performance. It serves as the foundation for OpenAI's infrastructure, providing us with similar functionality. This not only prepares us for AI use cases but also efficiently supports our loyalty use cases. We can share information with our customers and deliver experiences without concern about performance.

For our Disaster Recovery plan, we currently use geo-replication. We'd like to avoid full DR replication if possible, as this would result in significant cost savings.

I have been using Microsoft Azure Cosmos DB for five years.

We have not had any stability issues with Cosmos DB.

Cosmos DB's scalability is excellent, which is the whole reason to use it for scalability and performance.

The dynamic scaling helps decrease our overhead costs.

The initial deployment was straightforward and consisted of two to three people.

Cosmos DB's pricing structure has significantly improved in recent months, both in terms of its pricing model and how charges are calculated. This has led to substantial cost savings for both us and our customers.

I rate Microsoft Azure Cosmos DB seven out of ten because of the disaster recovery requirements.

Cosmos DB presents a steep learning curve. I would rate it a five out of ten. The challenge lies not so much in understanding its concepts as in utilizing them effectively and efficiently.

It took us 12 to 18 months of focused attention to fully onboard our team. At that point, we began to understand. However, it wasn't until we went live and observed actual user activity that we truly grasped the whole picture. Testing is one thing, but experiencing real-world interactions provides invaluable insights and a deeper understanding.

Cosmos DB requires minimal maintenance, but monitoring its performance and optimizing it as needed is crucial.

Potential users should plan accordingly, as Cosmos DB is a NoSQL database that uses similar design principles. Consider the design and apply those principles beforehand to optimize performance from the start. Understanding your read-and-write ratio is crucial due to cost implications, so ensure you understand the balance between reading and writing to the database. All these factors matter as they can impact your costs, so consider them carefully. 

I was using Microsoft Azure Cosmos DB to store unstructured data.

It is easy to learn how to use Azure Cosmos DB.

Azure Cosmos DB enhances search result quality by enabling rapid data retrieval within its collections. It can handle large amounts of data efficiently.

Azure Cosmos DB offers numerous advantages, including flexibility, cost-effectiveness relative to performance, and a pre-existing infrastructure on Azure. Its support for multiple data models, extensive document database capabilities, and fully managed maintenance provided by Azure make it a compelling choice with immediately apparent benefits.

Microsoft Azure Cosmos DB simplifies the process of saving and retrieving data. The only requirement is to create the collection and streamline data management.

There should be a simpler way for data migration. Currently, we need to write scripts to update data in bulk and ensure proper connectivity for migration with .NET, which seems hectic and risky.

I have used Cosmos DB in my previous project for around one year.

Azure Cosmos DB offers high availability, at approximately 99.9 percent, with good latency.

Azure Cosmos DB handles scalability well. It is easy to scale up the workloads.

Dynamic scaling enhances cost-efficiency and usability by automatically adjusting resources to meet demand. This means the system scales up capacity when needed and scales down when not in use, preventing unnecessary expenses.

I have used MongoDB, but not extensively. If working with Node, I would recommend Mongo, but in a Microsoft environment, I recommend Cosmos.

The initial deployment was manageable. It took around five to seven hours to deploy Cosmos to a working condition fully.

At that time, we had a team of four people managing the infrastructure and deployments.

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

New users should be familiar with DocumentDB since most people are only aware of relational databases, but Cosmos is different.

I develop applications. I developed an application where I had to search the Azure Cosmos DB database for values related to suspicious entities. It involved retrieving, sorting, and manually searching data through queries.

Azure Cosmos DB helped improve the quality of our search results. We could see its benefits immediately after the deployment.

The availability and latency of Azure Cosmos DB are excellent. It handles large data insertions efficiently without any problems related to scalability. It scales workloads very well.

The library of Azure Cosmos DB is like JPA, but it is not exactly JPA. We could not integrate that.

The size of the continuation token in Azure Cosmos DB should be static rather than increasing with more data, as it can lead to application crashes. They should use a static key size.

If we want to update some data, we cannot use the SQL command line. It is not like SQL Server or any other relational database. We have to send the JSON file or send the text to the Azure portal.  These are the only two options. We cannot use the normal SQL statement.

I have been using it since December 2021.

The scalability is very good. We performed performance tests, inserting objects with more than 10,000 records without any issues, although, on the application side, we started to see high memory consumption. That is because, with larger JSON files, you will have more objects in the Java application. These things consume memory, but there are no issues regarding scalability.

I have not contacted their support.

Neutral

I have experience with MongoDB but only for personal studies. I only learned the basic things.

It was easy because we have Terraform embedded in the Jenkins pipeline. Once I deploy the application, it connects with Azure Cosmos DB. It is already configured, so I do not have to worry about this part.

It took us about one month to get onboarded and understand the basic functionalities. 

It does not require any maintenance at our end.

We usually have a team of three to four people.

I am not aware of the price, but a challenge that I have faced occasionally is that running longer queries requires more RUs, so I have to ask someone with permissions to execute the queries.

I would advise learning more about queries and select statements. You can use that on the Java side and Cosmos SDK.

It is easier to learn if you already know relational databases. You can use some of that knowledge to work with Azure Cosmos DB. Also, if you know JPA, it would not be so difficult to work with the Cosmos SDK for Java application development. Inserting data is also simple.

It is at a medium level in terms of ease of use. There is documentation for gathering the information. Azure Cosmos DB does not have any constraints for the column names. If you want to create a specific query, you can find information related to that in Microsoft documentation. You can find queries to solve specific problems.

I would rate Azure Cosmos DB an eight out of ten.

We use it as a data storage platform for several proprietary applications that we have designed and built and now support. We generally use it to be able to scale so that our customers can search a sizable amount of data. We have millions of records that include an extensive amount of text.

We implemented Azure Cosmos DB for our tokenization process. We had originally built a data dictionary to be able to tokenize different words within our MongoDB collection, but over a period of three years or so, we found that there were some limitations to doing that. The data dictionary needed to be updated, so we turned to the vector search feature because it essentially allows us to measure the similarity between words. Those sorts of comparisons could be done very easily. There is the ability to tokenize words, which we then use in the search functionality provided for the users of our applications. It has helped us improve the search functionality of our applications.

I landed on it as an architectural component of one of our first solutions. I went in expecting its benefits. It delivered the benefits of being able to quickly scale and being able to support semi-structured, unstructured, and structured data sets or data properties. All of these aspects are supported. We were able to realize its benefits early on.

We have used the vector database with Azure AI services. It works fine. They are embedded vectors. We are running some text through Azure AI. It then returns these embedded vectors, and we store those. We are able to use those vector values or vectors to determine the similarity between various words that are being searched in our applications.

The Azure Cosmos DB's ability to search through large amounts of data is excellent. It is fantastic. We have benefited from it. It is great.

There are a number of different APIs or data storage supported in Azure Cosmos DB. Specifically, we are using the MongoDB API, so we leverage it in that way. I like the flexibility that it offers. My team does not have to spend time building out database tables. We can get going fairly quickly with being able to read and write data into a MongoDB collection that is hosted inside Azure Cosmos DB.

I found it very easy to use. We have been using it for five years, so it is quite flexible for us. The ease of use is quite high for us.

It is easy to use, but optimization has been a mixed experience. It has been more of trying to figure out how to do so. We have not found much support there, so we have to come up with our own way of optimizing it in different ways. That is one area of improvement. We would like to have more tools that support the optimization of Azure Cosmos DB. There are not many tools out there. We have had to develop our own tools internally, such as a clean plan or query plan, and look at the index usage, throughput, and those sorts of things. The portal experience for optimizing and monitoring the service needs a few enhancements.

I am looking at it through the lens of MongoDB API. There are four or five other APIs that are supported in Azure Cosmos DB. The MongoDB API experience could be improved substantially by having a more user-friendly set of administrative tools so that I can go out there and query the documents that are part of the collection. Currently, I am working around that by using third-party tools like 3T. I also use MongoDB's Compass client tool. They can make this part of managing the database or the collection a lot easier by providing some built-in toolsets, similar to what is offered by Azure with Azure Data Studio. That is a big area for improvement. 

We should also be able to better manage the cost. There have been some improvements there, but there is still room for improvement in terms of how costs are managed through the Azure portal relative to Azure Cosmos DB. To me, it is one of the more expensive services out there depending on how it is being leveraged.

One of the key limitations is that only so many vectors can be supported. It does not work very well with the large amount of text that has to be embedded in the vectors. That is one limitation we have run into with the feature set.

It has been five years. 

The SLA is pretty good. We have been able to at least get past 99.9%. We are probably closer to 99.99%. So, overall, it has done well over the five years. Just like with most things, there were a few instances where it crashed or was not available. Those instances are memorable, but they are few and very far apart.

Its latency is good. The availability is also good.

Its scalability is good. However, redundancy does not work very well. Redundancy is having a set of backups. It is also a part of high availability. We have used some of the redundancy features in Azure Cosmos DB, and it created problems for us consistently. We recently had to move away from having a redundant copy of the data and just having a single copy. Of course, we have adequate backups.

Through the lens of MongoDB API, the scalability can be better. However, the limitations are core to the actual platform. MongoDB is not designed to scale horizontally, so that is how Azure offers it. It scales vertically which means that I can go and request more compute and more memory RUs for the instance that I am using. If I was supporting multiple workloads that had different read/ write patterns, it would work, but it is not designed to do that well at its core, as I understand it. It is less of a function of Azure Cosmos DB and more of a function of MongoDB itself.

The dynamic scaling has helped decrease our organization’s overhead costs. We are able to scale up during business hours, or when there is demand, we scale automatically. There are some tools that we have built and some processes that do that. We can also scale down during non-business hours or when the demand drops for the database or the data store. It has helped to manage scaling costs.

I contacted their support this year when we had some issues. When it comes to customer service, it always comes down to the person you get on the phone or who picks up your ticket. 

Regarding Azure Cosmos DB, we felt frustrated when we needed that support from Microsoft. It has not been there because the things that we are dealing with are generally more complex than most customers would have to deal with. At times, the representatives or engineers we got or who picked up the tickets we submitted did not have the breadth of experience needed to support us or resolve the issues. So, we resolve the issues ourselves the best we can.

Negative

We use some of the alternatives because it does not solve everything. There is no such thing as one perfect data store. We use Azure SQL instances. We use SQL and VM in Azure. We have started doing a lot more Postgres, which is the flavor of the time. Everybody seems to be moving to Postgres all of a sudden.

Synapse is another tool. It is another Azure service that we use. It just depends on what type of data we are using and what makes sense in terms of the implementation of the application. Those are some of the alternatives that we have used.

Its deployment is easy. Setting up the service is easy. You make a decision around where you want to deploy and those sorts of things. There is a lot of pointing and clicking. That was very easy.

Taking it to production was a lot harder. It was a lift to get the data loaded into Azure Cosmos DB. At the time, there were about 750,000 resumes that we uploaded for a customer, so it took a lot of time. We had to build a custom app to load those documents on data records into Azure Cosmos DB. We had two people working on it for two weeks. We probably spent somewhere around 60 hours around the lift to get that all loaded up and going in Azure Cosmos DB.

It took us about 18 months to feel fully confident in working with this and reach a level where we can go and teach others. We feel that we have got a firm grasp of the service after about 18 months of production support.

Its maintenance has been taken care of by Microsoft. However, at the end of last year going into this year, there were a few disruptions with the service that hampered our customers or users. There have been times when the service went down, or the service was upgraded but the SDK or NuGet packages used to support or connect to Azure Cosmos DB were not in sync. Overall, Microsoft takes care of the maintenance.

It was all done in-house. We had two people involved in it, myself and my lead developer. It was mainly about loading data into Azure Cosmos DB. That was a big lift.

Azure Cosmos DB helped decrease our organization’s total cost of ownership. It is hard to provide the numbers, but managing the data store is easier for us with Azure Cosmos DB with the MongoDB API because there is no need for a DBA. We do not have a DBA on the team who is just taking care of the indexes and making sure that the database is healthy. It pretty much just runs. If we had a DBA in the team specifically for MongoDB, we would be paying about 150,000 dollars a year. We have to somewhere in the neighborhood of 50,000 dollars for the service in Azure. In terms of the total cost of ownership, it saves us about 100,000 dollars.

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. 

Overall, I would rate Azure Cosmos DB an eight out of ten.

Our primary use case for Cosmos DB is unstructured data. We utilize it to spin up databases quickly.

We previously used on-premises databases. Switching to the cloud significantly improved scalability, flexibility, and uptime. It also addressed our uptime issues and greatly benefited our organization. We've never had issues searching through any amount of data; it's more than capable of searching large amounts of data. 

The connectors, such as the MongoDB connector and the integration with SQL, are incredibly valuable.

There's a little bit of a learning curve because I was new to Azure. But once you learn the tool, it's pretty straightforward.

I have been using the solution for about three years now.

We haven't noticed any significant issues with latency, but we don't have many applications. The availability is excellent, and we have multiple availability zones, so nothing goes down. 

The solution can scale very well both up and down, although we predominantly work with smaller databases. We haven't needed extensive scalability yet, but it seems very capable.

We previously used MongoDB. The shift to Cosmos made sense as we wanted to move to the cloud and benefit from its MongoDB API connection.

The setup was straightforward. Team members could start using the tool within a few hours, although not at an expert level.

As far as I know, it's cheaper compared to running on-prem, although comparing costs exactly can be challenging.

I personally don't deal much with budgets, but our financial analyst hasn't raised any complaints. The pricing aligns well with budget expectations.

I would rate the product an eight or nine out of ten. We are very happy with it as it runs smoothly right out of the box.

I am building an extension app for DocuSign. One of the ways for me to demonstrate this is by using a third-party database. I read and write data from Cosmos DB using DocuSign tools.

We wanted to use Azure function apps and Cosmos DB because Cosmos is serverless and non-relational, so it's easy to set up and simple to scale up and down. Overall, it was a good fit. 

I like the way you can create and delete records. You pass a JSON, and then it creates a record. It is easy to use because you don't need to know much about Cosmos DB or have prior experience. 

Cosmos DB does a pretty good job of searching. I've never had trouble as long as I search for a unique key or value I'm looking for. If my query is right, it returns the value.

Once you create a database, it calls the container, and then items show up. A better description and more guidance would help because the first time I created it, I didn't understand that a container is similar to a table in SQL. 

I have been using it for six months.

My use case is like a proof of concept, so the data set is not extremely large, but I know from reading about it that it can scale up well. It should do a good job on large amounts of data. 

The initial setup was simple the first time I used Cosmos DB. It took just a few hours for my small technical team to get used to how Cosmos DB works.

The pricing model has aligned with our expectations. In Azure, setting it as consumption-based or serverless keeps the cost low, but we had instances where automation increased the cost significantly. It was more of a configuration problem, where options to keep it minimal are still present.

We wanted to go with Azure function apps and Cosmos DB to keep it serverless and non-relational, making it easy to set up, scale up, and down.

I rate the product as eight out of ten.

We use Cosmos DB as our entire storage database solution for our application. We don't use any other relational database. We have a file that we use for configuration, but we use Cosmos for user data.

We have about 100,000 users a week who visit our website. We have plans to increase usage to four times what we're using now.

The biggest benefit it offers is scalability. It's easier to work with concurrency and updating data. We don't have to worry about locking cables or the speed of reads or query searches because we've structured our data around a key value. Everything is super fast, and it basically removes any bottleneck related to databases in our application, and we just use reference keys. One document will reference the key of another document that we need, so we don't have to rely on searching.

Partitioning is helpful because we use it heavily. Partitions are really nice because they help with the collection of data. Not only is it fast to recall the data, but when you partition it, you can pull the partition and then query the exact document from that partition. It helps with data recall.

There's another feature that we just started implementing, which is partial updates of documents. It doesn't require the entire object to update, but updating documents across applications becomes difficult because you have to pull the entire document, which means you have to support the entire model to update it. So, that application has to know about every single parameter that may or may not have been added because if it reads and writes the document again, you'll lose data elements.

An improvement would be a more robust functionality around updating elements on a document, or some type of procedural updates that don't require pulling the entire document. Otherwise, you have to keep all of your apps up to date with the models, and that can be cumbersome and lead to errors. Usually, you don't always remember, and then it leads to some type of bug, but you won't realize why. You'll lose some value because you don't realize that you have some application that doesn't run often. You forget that it writes to that same document and you didn't update the model.

It would be nice to have some type of functionality for less common updating applications and to not always have to worry about keeping that model up to date.

There's some integration with Entity Framework and it's nice, but it's not robust and it would be good to have something like that when it comes to pulling data.

Occasionally, you have to query the database for values because we save our appointments and we don't have an index on appointments. We don't have a manual lookup for appointments, so we don't save it in another file. We have to run a query to get appointments that occur on a specific day and the downside of that is you have to use strings just to hardcode the string values. It would be nice to more easily integrate with a tool like Entity Framework, and I know that they do, but it's not an easy process. It would be nice to have an easier way without relying on text to query the database.

I have used Cosmos DB for a year and a half.

There have been some configuration issues, but we haven't hit any thresholds or roadblocks when it comes to throughput. That was one of the reasons that we leaned toward it and not a relational database, especially at scale. We haven't run into any issues when it comes to that.

We look at community answers because we can usually get answers faster than messaging support directly. We don't usually resort to a customer service type of support unless it's a fundamental issue. 

When we had an outage in the middle of the night, the turnaround time was within a few hours.

Previously, I used Couchbase. I've also used Neptune, which is a different type of database. I've also used SQL.

We chose Cosmo DB because it's more tightly integrated. One of the reasons we chose this version of a non-relational database was because of the speed of development. We also chose Cosmos DB over other types of NoSQL databases because it's so tightly integrated with Azure, it's easily managed through deployment templates, and it's very easy to scale. If you're using Azure already, it's a very easy tool to pick up and integrate into your applications.

Cosmos is pretty straightforward. There is more complexity, so you just have to be mindful. We had a small issue with making sure that the disaster recovery settings were set up correctly. We found out that there was some type of outage in the middle of the night, but we noticed that the failover didn't run properly. It was because of some configuration that should have been caught earlier, and it wasn't obvious that we got it wrong.

There are some infrastructure teams that manage some underlying resources that are related to Cosmos and some of the configurations, but for our specific implementation, we have three developers at most. We usually only need two people for maintaining and managing the solution.

We deployed the solution in the cloud, but we configured everything in-house.

We have seen ROI. There's no active management when it comes to that. When I've worked on relational databases, there's a lot that goes on, like indexing, upgrades, and store procedures. I've managed relational databases for years while working on an application and worked with people who managed them. Cosmos is nearly maintenance-free and very easy to use.

The pricing is really good. I would rate the cost as 9 out of 10. There may be some more complicated use cases that are more expensive. When we've budgeted for our resources, it's one of the more expensive ones, but it's still not very expensive per month.

I would rate this solution as 8 out of 10. 

When it comes to ease of use, spinning up and working at scale, our specific use case, and the scalability that it offers, the solution is definitely very good.

My advice is to use containers as single objects and create manual indexing to improve efficiency.

In my role, I use Microsoft Azure Cosmos DB fairly extensively across various platforms. At ASOS, we utilize it for order processing to record incoming orders and for commercial integration platforms. Overall, we have numerous use cases.

Overall, Microsoft Azure Cosmos DB is easy to use.

Microsoft Azure Cosmos DB has provided benefits compared to SQL databases, particularly in terms of availability.

Microsoft Azure Cosmos DB has helped to improve our total cost of ownership.

Microsoft Azure Cosmos DB offers a relatively easy learning curve due to its limited programming service area compared to SQL Server. This streamlined functionality allows users to quickly grasp the SQL query language.

The autoscale feature is the most useful for us.

There should be parity between the various APIs. I often work with the Mongo API, and features for it sometimes lag substantially behind the core API, such as the Analytical Store feature. Additionally, I am waiting for the full fidelity change feed that would surface all changes, including deletes to documents.

While Microsoft Azure Cosmos DB is generally easy to use, it has some limitations. Certain areas are more restrictive, and we are awaiting features that will simplify development. For example, currently under development, the full fidelity change feed will expose all document changes, enabling tasks like synchronizing collections while accounting for deletions. This is challenging because the existing change feed doesn't provide information about deleted documents.

I have been using Microsoft Azure Cosmos DB for around seven years.

Cosmos DB demonstrates good stability and great reliability, with technical issues arising approximately once per year.

While Cosmos DB offers good latency and availability, careful consideration must be given to selecting appropriate consistency levels.

The scalability is excellent, and as long as the data can be partitioned, the scalability is nearly infinite.

Cosmos DB's ability to scale workloads is a significant advantage, as evidenced by our successful management of multiple terabytes of data without encountering any issues.

The quality of customer service and support varies. We always get an answer eventually, but the speed of resolution depends on the reason for the support ticket. If there is a bug in the product, we have to wait for it to be fixed.

Positive

The initial setup of Microsoft Azure Cosmos DB is straightforward. Even someone with no experience can easily deploy the solution.

The deployment can be completed by one person on the same day.

The deployment can be done entirely in-house. Whether we are doing it manually in the portal or deploying it through Terraform, it is straightforward. We do not require help from an integrator or consultant, although there are considerations about partitioning collections when creating resources.

The pricing for Cosmos DB has improved, particularly with the new pricing for Autoscale. Previously, we were charged according to the busiest partition across all regions, but now, each partition is only charged for what it uses. This change has substantially reduced our costs.

When evaluating new projects, we determine whether data storage is best suited for a relational database, such as Azure SQL Database, or a non-relational database like Cosmos DB.

I would rate Microsoft Azure Cosmos DB eight out of ten.

Understanding some of the subtleties of Microsoft Azure Cosmos DB can take time, and some individuals at ASOS still find concepts like partitioning unclear. However, getting started with Cosmos DB and developing functional applications is quick and can be achieved in a short timeframe.

We require minimal maintenance to validate that we've configured, for example, the correct indexing policies as required by our queries.

New users should make sure they understand partitioning because once it's selected, it is difficult to change it. Otherwise, you would need to migrate everything over to another collection.