Microsoft Azure Cosmos DB Reviews

I have been working with Microsoft Azure Cosmos DB for approximately two years across seven projects. I started by creating basic containers at the free tier level that Azure provides, and when requirements grew, I moved them to the paid version. Azure typically provides two containers for free, after which you need to upgrade to the paid version. Of the seven projects, five were small projects and two were mid-sized projects. We created approximately 20-30 containers in total.

It works similarly to MongoDB when using NoSQL. When deploying on Azure, the communication is rather easy without many steps and complications, though this is more a benefit of Azure rather than Microsoft Azure Cosmos DB specifically. The queries take a similar amount of time compared to other databases, but the database management system provides a better-looking UI for viewing data compared to other solutions. 

The queries in Microsoft Azure Cosmos DB are faster when trying to fetch specific fields from JSON or run particular queries on a container.

The main downside I have faced was with hierarchical partitioning in Microsoft Azure Cosmos DB. When using the second partition within hierarchical partitioning, I encountered issues while fetching queries. Though it retrieves values, the performance is not optimal when using partitioning. For example, when dealing with users and different categories of users in hierarchical partitioning, the query results were not providing all the desired results. The documentation regarding partitioning keys was limited, and despite contacting support, the problem remained unresolved. Additional documentation on this feature would be beneficial.

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

Lag only occurs when different resources are set up at different locations. When all resources are at the same point, there is no lag. In production, we have experienced minimal issues. The project has been live for two years without any database problems. We decreased the timeout for connections and queries to two seconds, and it works efficiently. Our project remains at mid-scale without requiring a load balancer at the Microsoft Azure Cosmos DB level.

Given the RU size, it can scale up significantly. If the user base increases, increasing RUs to handle more database calls is not problematic. Azure has done an excellent job making everything more scalable, including Microsoft Azure Cosmos DB. The plans allow for easy upgrades based on user growth, supporting parallel queries and increased call volumes. You can move to a better plan if you are going to have more users.

The response time you experience mostly depends on the specific service you are using. For instance, For Azure AD-related issues, their support was quite fast and effective. However, with Azure AI speech service, I faced some challenges. They didn't provide a precise answer to my issue, but they did share some documents for reference. It took about 48 hours for them to respond, and even then, the solution they offered wasn't exactly what I needed.

On the other hand, my experience with Azure Cosmos DB was more positive. I had a specific inquiry regarding partitioning, and their support team was helpful. One representative reached out to me, and we discussed the issue. They were able to guide me in implementing different hierarchical IDs to structure the data better. Our goal was to optimize our queries and reduce API calls. Overall, their response took around seven to eight hours, and I felt they effectively resolved my concerns.

Overall, I would rate their support an eight out of ten. Specifically, if I focus on .NET, I find their support to be excellent. However, for other services, such as Azure reports, there are still issues that prevent me from giving a higher score, so in those cases, I would rate it a seven.

Positive

I previously used MongoDB. The choice depends on requirements. Microsoft Azure Cosmos DB is optimal for simpler structures with fewer containers and less complex data partitioning, offering faster queries. For more complex database structures, MongoDB might be preferable. However, when using other Azure services and hosting on Azure, Microsoft Azure Cosmos DB works best. The choice ultimately depends on the entire application architecture and hosting environment.

The onboarding process and learning to run queries is straightforward. It requires only a connection string for the database and container name to run queries. Migration from SQL to NoSQL is relatively simple due to easy syntax and connection process. The initial setup took approximately 5 minutes using the emulator on the local machine, and the Azure subscription setup required only about a minute. An API was used to create all containers efficiently.

The pricing is calculated per query with specific calculations, though I cannot provide detailed information about this aspect.

I have worked with Azure AI services including translation, transcription, and speech synthesis. We used Microsoft Azure Cosmos DB for storing links to storage accounts for AI-generated data, utilizing NoSQL queries for data retrieval. Azure AI services can be somewhat challenging to integrate, in my opinion. I find that integrating Microsoft APIs is generally harder compared to others. In this case, we weren't using the Vector DB explicitly; instead, we utilized Microsoft Azure Cosmos DB. We relied on standard containers, and I believe we had about seven or eight containers in total.

We generated our data using AI services and then stored it in these containers. The links to specific storage accounts for each request were saved in Cosmos DB. When we needed to retrieve that information, we used queries to fetch the data.

In a banking project, we used the Vector DB capabilities of Microsoft Azure Cosmos DB, though limitations were due to our API standards rather than database limitations. The team later discovered and implemented the inbuilt vector DB features when the database grew.

Overall, I would rate it a nine out of ten with the only significant issue being the partitioning key functionality. It's a good alternative to other NoSQL databases.

The main use case for Microsoft Azure Cosmos DB is as a key-value store where we store all the user data that we have and perform lookups. We use it at a significant scale, with storage of unique data reaching 12 terabytes and handling up to 3 million requests per second.

The scalability of Microsoft Azure Cosmos DB has significantly aided us in managing workloads efficiently.

We were able to realize the benefits of Microsoft Azure Cosmos DB immediately after deployment, making it quite easy to get started.

The most valuable features of Microsoft Azure Cosmos DB include the TTL, the ability to scale up and down as needed, and geo-replication, which comes out of the box. We do not have to do anything for geo-replication. We just have to enable it.

The indexing policy is also very good, and the overall metrics and monitoring system are also quite good.

Microsoft Azure Cosmos DB is fairly easy to use.

Microsoft Azure Cosmos DB can be improved by providing more fine-grained control over certain aspects, such as connections and threads. There could be more control over how many connections are made. I am not sure if it is a knowledge gap issue. A regular connection with the Azure Cosmos DB team might help in addressing knowledge gaps. Being able to fine-tune these features would be helpful for us.

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

Over the last two years, Microsoft Azure Cosmos DB has been very stable. It has very good latency and availability. Latency is good on the server side and the client side. We have had only one significant issue that affected our production system. Overall, stability has been excellent.

The scalability of Microsoft Azure Cosmos DB is one of its best attributes. We can scale very efficiently and adjust workloads as needed, which is more challenging with other systems.

We have contacted their support many times. The quality of customer and technical support has improved over the years. Initially, it used to take quite a while for issues to be resolved, but now the support is seamless and very efficient. We have not needed much support in the last couple of years due to the system's stability. It is pretty stable now. I would rate their support a nine out of ten.

Positive

I have used Redis briefly and Aerospike extensively before switching to Microsoft Azure Cosmos DB.

Both Microsoft Azure Cosmos DB and Aerospike have their own advantages. The biggest advantage of Microsoft Azure Cosmos DB is that it is very easy to get started with and it does not require too much effort. It takes just one click to deploy Microsoft Azure Cosmos DB and put it into multiple regions. It does not require too much maintenance, whereas Aerospike requires a lot of maintenance effort. It requires a dedicated team. In this aspect, Microsoft Azure Cosmos DB is very good. However, Aerospike provides control over a few things, which we do not have in Microsoft Azure Cosmos DB. If we want to run or use the maximum amount of resources, Aerospike helps a lot. Both have their advantages and disadvantages.

The initial setup was easy. It was not difficult.

It took us a quarter to be able to use it efficiently. It is fairly easy and straightforward.

We had set up our own autoscaler. There was a pipeline that ran on top of Azure Cosmos DB to see how many RUs were provisioned. It did require a little bit of maintenance because we built custom software on top of that, but that was it. Our autoscaler performed better than Azure Autoscaler. However, because of some billing benefits, we have started using Azure Autoscaler. The Microsoft team said that if we used Azure Autoscaler, they would give us a discount, so we started using that, but our autoscaler performed better.

Initially, the deployment required an entire team, but now, it can be managed by a smaller team of two to three engineers.

It has decreased our total cost of ownership by approximately 20% compared to other alternatives such as Redis.

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.

To new users, I would advise first knowing their data. They should know whether it fits their solution, which Azure Cosmos API to use, and what scale they intend to run it.

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

I worked on an entity data client project that handled multiple users from digital products, like a Stream player similar to Netflix. It is called Play Plus, with a user base of seven million and a website that attracts eighty-two million unique users per month. We store the data from these users in Cosmos DB to ensure a fast connection between the user profile and the advertising system.

I believe the best features of Cosmos DB are its global distribution capabilities. It supports multi-region replication with low latency in read and write operations and automatically adjusts throughput dynamically based on workloads. It ensures low latencies, offers consistency levels, and provides a serverless mode. Also, for this scenario, the solution was fast due to its serverless nature, allowing it to handle eighty-two million user profiles with low latency and swift connectivity with the advertising system.

I think Cosmos DB enhanced the benefits of NoSQL databases, offering query flexibility, partition management, and backup and restore options. These aspects are crucial, and perhaps improving the complexity of partition management would be a beneficial area for Microsoft to focus on.

I have been using Cosmos DB since 2023 when I executed a project for clients.

I do not experience problems with Cosmos DB, nor does the team have any issues with it.

I have used both Cosmos and AWS DynamoDB. Compared to Cassandra and Cosmos DB, I think Cosmos is a superior solution because it is serverless. There is no need to worry about installation, updates, or security, as Microsoft handles all this. Our focus is solely on development.

The setup was easy, very easy.

By using Cosmos DB, I estimate at least a twenty percent saving because it allows us to develop rapidly.

I truly recommend Cosmos DB because it is a serverless product. Our responsibility is just to develop and adhere to Microsoft's instructions to achieve optimal performance from Cosmos DB. It is an excellent tool. The product rating is nine out of ten.

This is an event-driven solution. Most oil and gas companies have folder source systems, where they cannot scale, but they still want to provide real-time data to their end consumers for various analytical use cases and AI/ML processing; this is where we input raw data into the Azure environment of this solution. Then, eventually, we built the API on top of Microsoft Azure Cosmos DB because it's highly scalable. The solution is a little bit expensive, but the businesses are ready to accept it. 

In terms of performance versus scalability of this solution, you don't need to worry as long as you have your initial numbers in place. This product works by using performance currency, which is the number of request units per second. Once the data is ingested, based on that, we can know how many users are going to access across the world in every day, hour, or minute. Once you have the ingestion versus consumer pattern identified, you can use this product to input all those numbers, like the volume of data for migration. 

The built-in integration of the solution is tight. It can be used in conjunction with Synapse, Microsoft has also created a Synapse link. In this solution, the OLTP workload will never affect the OLAP workload. Therefore, the solution does data replication asynchronously without affecting the OLTP source system. No specific pipeline is thus required, which is not easily found in other services. 

In the server, there are two ways in which you can provision a call, one is serverless, which has a pay-as-you-go model, and another is a dedicated provision throughout. So, irrespective of what you allocate and whether you use it or not, in this solution, the charges are accounted for the request unit per second. This is a big drawback of the solution.

This is an expensive solution and if you get the initial calculation wrong involving how much you are going to ingest, how many people are going to query and more, then you are going to receive a very large bill at the end of every month.

Additionally, on the serverless option, there is a limitation regarding the amount of data you can ingest; this doesn't allow you to upgrade beyond a point, and the limit cannot be utilized for many use cases. On the execution side, whatever you create as a container, that container cannot be used as a destination when using serverless mode. This is another key limitation of the solution. 

I have been using the solution for one and a half years. 

I had minor issues while using the solution, but they were actively solved, and eventually, a justification was also given. Ninety-five percent of the time I used this solution, there were no issues. Microsoft's service in the cloud market is still growing and so there are some feature limitations. 

The solution is highly scalable. We use the solution in our enterprise both internally and externally, including integration for clients. We created our solution end-to-end by considering different audiences, people who can directly onboard Azure but might not need Cosmos DB.

There are vendors and individuals who cannot directly consume data on the Azure environment and will have a dependency on data. However, we cannot expose the source data for its performance issues and limited scalability, so we deliver this data by using Microsoft Azure Cosmos DB. The parent company Reliance has multiple subsidiaries like Ajio, manufacturing supply chain, Oil and Gas, and more; we used to use the same API for all subsidiaries, which was built on Cosmos. 

Technical support was good. I would rate the customer support an eight out of ten. They were fast and responsive, but the support team runs from different locations within or outside India, so whoever is working during the particular shift will take care of the case initially and then some other individual will take over.

So, my team had to re-explain the same thing over a call or meeting. But it was only a few times, they were able to get all the information based on the previous conversations most of the time. 

Positive

Deployment of the solution was very easy. Once the initial numbers are in place based on request units, only the instance creation was a time-consuming process. The time was consumed due to the dependency on other teams like DevOps, who are responsible for provisioning. So, it was a one-time process, but migrating and running the same workload between different environments was not much of a hassle. 

It took less than a week to configure and install this solution. To complete the setup, it took five to six professionals from our team. One key solution architect, two people from the DevOps team, and two solution architects from Microsoft were needed for the deployment of this product.

Maintenance of the solution is very easy because the solution follows a Platform-as-a-Service type of model. There is actually no need for any downtime or a patch upgrade because it is taken care of by Microsoft. I never have to worry about downtime for this solution. They perfectly deliver on the key characteristics of the product. 

Our business need was to deliver or provide the source data without any latency issues, in less than five or ten minutes latency, to be precise. We had to provide the data to the end consumer without overwhelming our source. We got the business confidence in the initial three months of using Microsoft Azure Cosmos DB. 

The solution is a bit on the expensive side. 

We tried to compare this solution with MongoDB, which is open-source. But we choose this solution because Microsoft is the first implementation partner for us. 

I would rate the solution an eight out of ten. My advice to other people will be first to identify the purpose of availing the solution. There is also a product called Azure Data Explorer, which is a more extensive service used for similar use cases.

Also, in terms of Cosmos, the user should be clear about whether they will be able to use the serverless deployment model or whether they need the dedicated provisioned throughput Model. They should also first use the price calculator by inputting the numbers to decide if they need it. I would also advise you to get in touch with a Microsoft Specialist and walk through all the doubts. 

The solution has a very good service, but the user should be clear about how to start using the product. For the initial three months, we did a lot of trials to get the components and RUs right and check how the calculation is happening. However, after the trials, we were very clear about how we wanted to move forward with the solution to get the maximum ROI. 

I like to describe it as a programmer's database. .NET developers, in particular, can design and work with the data easily because it's schema-free. Unlike traditional databases, which are considered rigid with their rules, developers really love Cosmos DB because of its schema-free nature and the freedom it offers.

Cosmos is widely used for web applications. You can also use it for inventory management and IoT solutions... there are a ton of different applications.

It's very easy to integrate Azure Cosmos DB with other Azure services. For example, generating a Power BI report from data in Cosmos is just a few clicks. It's also simple to stream IoT or sensor data into Cosmos.

When it comes to supporting IoT or real-time analytics, the main advantage is speed. Cosmos DB doesn't rely on traditional disks and database structures like a relational database. It uses JSON, which is similar to XML, and that makes it incredibly fast.

The way it was designed is most valuable for global distribution. Unlike old-school SQL Server that was intended for a single data center, Cosmos was built from the ground up for global availability. 

Features like geo-clustering and mirroring were not afterthoughts. If you have a database in Chicago, you can right-click and easily create a failover group in Japan. That works well for global companies with offices across continents; it minimizes latency.

Cosmos's multi-model support made databases more highly available. 

The downside is that Cosmos is new and fairly complex. There's a limited pool of talent who are really good at working with it.

Because of that, I've been approached by recruiters quite a bit; they see my Cosmos DB certification on LinkedIn. It's hard to find people to work on Cosmos projects. 

Sometimes, a really smart developer will design and build a Cosmos implementation and then move on, leaving the company struggling to find someone to work with it and maintain it.

Interestingly, if you need to restore a Cosmos DB database, you have to put in a ticket with Microsoft – they're the only ones who can do that.

I've worked with Cosmos on and off for about three years.

For Cosmos DB, their technical support is very good. They are the experts in that product.

Overall, the customer service and support are excellent. 

Positive

I did a couple days of training on DynamoDB, which is Amazon's comparable product to Cosmos DB.

They're actually quite similar, both being multi-model databases. Relational databases are good for structured data, but once you get into semi-structured and unstructured data, they just don't perform well. 

That's where DynamoDB and Cosmos DB excel – storing, indexing, and quickly working with that less-structured data.

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. 

The harder part is on the developer side – designing the collections (similar to tables) and how the data will flow in.

I've set up a few Cosmos DB instances, and it's about a half-hour tops.

One person can handle the deployment. I'd typically set it up alongside other Azure components like a VM. You choose your settings, networking details, etc., basically walk through a wizard, hit deploy, and it's up within half an hour.

There are some configuration options for database administration on the customer's side. You'll need to go in and enable things like automatic indexing with checkboxes.

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.

I would recommend using it, but with a caveat – it's a good fit for companies with deep pockets.

It's powerful and amazing, but the costs can add up.

I'd give it an eight out of ten. It's super powerful and solves real problems with global distribution. I hesitate to give it a perfect ten because it's still new, and good training resources are harder to find. Even the most recent books on Cosmos DB are several years old, which is ancient in IT terms.

I had to work hard to get a certification in it. 

In our setup, we rely on Azure Cosmos DB primarily for cloud-native applications that demand global scalability. We use it for connecting web apps and implementing search functionalities.

Cosmos DB's low-latency data access has greatly improved our application performance. It is a game-changer, allowing us to move workloads from on-premises to the cloud, thanks to the reduced latency, and freeing us from the constraints of on-premises environments.

The most valuable features for our organization with Azure Cosmos DB are multi-master capability for applications, automatic failover ensuring high availability, scalability, support for multiple data models, and low-latency access. Additionally, the seamless integration with microservices running in containers adds another layer of efficiency to our operations.

In terms of improvement, slight enhancements in integration interfaces, expanded dashboard functionalities, and broader use-case support would be beneficial.

I would rate the scalability of Cosmos DB as a nine out of ten.

The technical support is quite good.

It is a relatively affordable solution.

For those considering Cosmos DB, my advice is to embrace its versatility. Cosmos DB can handle various data models like documents, wide columns, and graphs seamlessly. You can consolidate your needs into one database, Cosmos, eliminating the need for multiple databases. It simplifies management and offers a comprehensive solution for a wide range of use cases.

Overall, I would rate Microsoft Azure Cosmos DB as an 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.

I used to work for a bank in Turkey and used Microsoft Azure Cosmos DB in the bank for reporting. We used the solution for customer relationship management (CRM) and cost management.

Microsoft Azure Cosmos DB is easy to use and implement for application programmers.

The integration of the on-premise solution with the cloud can be difficult sometimes.

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

Microsoft Azure Cosmos DB is a stable solution if you use it on the Azure cloud.

I rate Microsoft Azure Cosmos DB a nine out of ten for stability.

Microsoft Azure Cosmos DB is a scalable solution. Currently, 10,000 users are using the solution. They use the dashboard application, but the dashboard application calls the data from Microsoft Azure Cosmos DB.

The solution's initial setup is straightforward if you use it on the Azure cloud.

We use a Microsoft subject matter expert (SME) to integrate Microsoft Azure Cosmos DB with the cloud or banking application. Microsoft Azure Cosmos DB can be deployed in one day. The solution's implementation is very easy in the Azure portal, but the most time-consuming step is to define the old data model in Cosmos DB. The security and the integration between Azure and on-prem are also time-consuming.

Microsoft Azure Cosmos DB is moderately priced, where it is neither expensive nor cheap.

The solution's licensing is usage-based. You will have an enterprise agreement if you use the solution in a cloud environment. The enterprise agreement is complex, where it is usage-based in addition to a base price. They decrease the solution's cost for an enterprise agreement, calculate the usage, and charge monthly bills.

Microsoft Azure Cosmos DB was deployed on the cloud in our organization. Only two or three people are enough to deploy and maintain the solution. Microsoft Azure Cosmos DB is the best solution for customers needing high-quality technical support.

Overall, I rate Microsoft Azure Cosmos DB a nine out of ten.