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 main use cases involve syncing client accounting data and containers, and we use it as a read database. We do not put much into it; we just sync from their on-premises data or from other APIs, and we collect things.

We have not used enough features of Microsoft Azure Cosmos DB yet, which is why I'm here to try to use more. We're trying to figure out how to do more by linking data from things like documents and our SQL structured databases into Microsoft Azure Cosmos DB. Our goal is aggregating our clients' data to run searches or reporting, and we're trying to learn how to use it more.

I evaluate the enterprise-grade security features of Microsoft Azure Cosmos DB in terms of data encryption and access control as excellent.

The scaling of Microsoft Azure Cosmos DB's automatic elastic scaling of throughput and storage works fine in our current projects, and we use shared throughput successfully.

The scalability and ease of use with the APIs of Microsoft Azure Cosmos DB have allowed us to meet our customers' expectations pretty easily with little barrier to entry.

The features have allowed us to become SOC 2 and NIST compliant relatively easily, so I would say that's been a good success for us.

I have not utilized Microsoft Azure Cosmos DB's multi-model support for handling diverse data types.

We haven't really used the global features; we don't make it multi-regional and only have a backup, so there hasn't been a reason to utilize globalization.

There is nothing right now; that's something that we'd be interested in regarding Microsoft Azure Cosmos DB's consistency models and their role in fine-tuning the performance of our applications.

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

I faced nothing that we couldn't overcome pretty easily; there were no significant issues. It's always a learning curve, but it wasn't hard to get past.

I wouldn't say we have benefited from the workload management by using it; we just sync data to it and make it available for people to retrieve.

I evaluate my customer service and technical support experience as great; anytime I've needed technical support, it's been excellent.

On a scale from one being the worst and ten being the best, I give my customer service and technical support a ten.

Positive

Prior to adopting Microsoft Azure, I wasn't using a different solution to address similar needs.

My experience with deploying Microsoft Azure has been relatively painless; it has been easy, and we haven't had any problems yet.

I have seen a return on investment.

We use it to sync data that is not easily accessible; the scalability and ease of integration into our system have been where our return on investment is.

We considered all Azure solutions before selecting Microsoft Azure Cosmos DB, including table storage, but Microsoft Azure Cosmos DB was a better fit, and we haven't looked at any other solutions.

I wouldn't know how Microsoft Azure Cosmos DB can be improved because I don't think we use enough of it; I need to learn more about what to use in Microsoft Azure Cosmos DB.

I find the pricing transparency of Microsoft Azure Cosmos DB to be a little confusing, but we're figuring it out.

I would recommend Microsoft Azure Cosmos DB to another organization that's considering using it. I gave this review a rating of nine.

Some of the use cases for Microsoft Azure Cosmos DB include storing log files and generating keys for our clients inside Microsoft Azure Cosmos DB. It helps us solve the problem of generating unique identifiers for our clients in Brazil, as we have many clients in our company. The system serves to generate unique keys for client attendance.

We are at the beginning of use, about 2 months in production, but it has been working well so far. We have not faced any problems.

Microsoft Azure Cosmos DB has improved our organization because there are various plans we can choose for different situations. We can scale and improve when needed, and the solution can be provided very fast when we want. The solution we use operates without problems.

We could see its benefits quickly because we can provide Microsoft Azure Cosmos DB very fast and when we want.

With Azure being our main cloud, the valuable features of Microsoft Azure Cosmos DB include integration with other Azure products that we're using and governance inside Azure. For integration with other products inside the Azure cloud, it was a better choice. 

It was easy to use and optimize Microsoft Azure Cosmos DB, as it was not difficult to configure. 

We are at the beginning of production, and everything is working very well. The price can always be lower, but currently, it's not a problem.

My experience with Microsoft Azure Cosmos DB is less than one year.

My impressions of the latency and availability of Microsoft Azure Cosmos DB are good, as we haven't faced any problems until now.

We have not scaled workloads with Microsoft Azure Cosmos DB yet, as we don't need it.

It's a large enterprise.

I would rate the support for Microsoft Azure Cosmos DB as excellent because the support team was very nice and helpful. We just send an email or call on Teams, and they quickly answer our questions.

Positive

I would rate the ease of setup for Microsoft Azure Cosmos DB as a seven out of ten. The setup was not very difficult because of the SaaS deployment, as we just needed to configure some things, such as the network and type of billing.

We did a detailed research on the solution we needed and decided to go with Microsoft Azure Cosmos DB. It took us one hour to set up the environment, tables, and connections.

In terms of the learning curve, another team is using it more extensively. I don't know if they have had any challenges. The learning curve seems to be pretty good.

For the deployment of Microsoft Azure Cosmos DB, we required two people. The roles involved in the deployment included one person from the network team and one person from the infrastructure on the cloud team.

We have seen a return on investment with Microsoft Azure Cosmos DB because we can have more control over our NoSQL solution. More control over our NoSQL solution helps us manage fraud, which can save money. We can better understand our data using this solution since we can integrate with other data and create views to understand the information.

We are not consuming so much yet since we are at the beginning of using this solution. I would rate the pricing of Microsoft Azure Cosmos DB a six out of ten.

We needed this kind of product. We are using NoSQL for the first time. We previously looked at MongoDB, but we switched to Microsoft Azure Cosmos DB because we preferred to use a native solution from Microsoft. The main difference is that Azure Cosmos DB is a Microsoft-native solution, and we prefer it because we have the support.

My advice to people considering using Microsoft Azure Cosmos DB would be that if they are using Azure and need a native solution, it is a nice choice. If they use MongoDB, they would need some APIs to integrate. 

As it is our first time using a NoSQL solution inside the company, we will probably continue using Microsoft Azure Cosmos DB. 

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

I used it in my last organization. We were creating a full-stack web application and used Microsoft Azure Cosmos DB to store user credentials and most of the transactional data, as well as user chats. We did many PoCs for the vector embedding of files for critical things.

We used the built-in vector database capabilities in Microsoft Azure Cosmos DB; we conducted different PoCs around that and tested many beta features. We tried them, and there were obviously hiccups because they were in the beta phase. The additional support provided was sufficient to help us with our PoCs.

RAG was something we wanted to deep dive into. We were trying to get a few machine learning models to run from the Kubernetes side. We wanted to take the data from our own database and then vectorize it and RAG over it so that we could have Q&A directly for what we wanted to do. 

We built an application internally for taking official documentation present on any publicly accessible website, chunking it, and vectorizing the data into vector embeddings. We used it to have Q&A so that we didn't need to go over much official documentation. That was the internal use of it, which helped significantly. We followed the guides present in the Azure official documentation and their YouTube channels. Operationally, it helped with efficiency. We doubled our productivity with this small application. When building something, if we didn't know about the technology, we typically searched the internet or ChatGPT, but with the application, we didn't have to follow the older practices of going to the official documentation, reading, understanding, and getting snippets there. With vector embeddings and RAG built over it, we could also optimize feedback from customers that guided our future enhancement, whether to build new features, enhance existing ones, or remove features that weren't beneficial.

Using Microsoft Azure Cosmos DB improved our organization's search result quality significantly. While running queries during the test phase, we were able to configure which particular dataset required fewer RUs and which required higher RUs. This way, when handing off the end product to customers, we ensured that only databases needing higher throughput would get more RUs. It positively impacted the costs. It helped us lower the overall cost of the database, dropping from 33% to 22%, reflecting an 11% decrease in the latest quarter.

The best part of Microsoft Azure Cosmos DB is that with the default configuration and the Azure functional pipeline, if your go-to cloud provider is Microsoft Azure, the whole integration is seamless. Doing it by SDK or any other way, through a POST request or HTTP request, is easy, and that is documented, so that is a plus point. 

Apart from that, the NoSQL database with SQL query support is a significant advantage. You can have both semi-structured and structured data stored in JSON and then have SQL queries run over it, which can be more advantageous compared to other providers.

The topic of RU consumption needs better documentation. 

Now that Microsoft has partnered with different LLM organizations, such as OpenAI, a bot could guide us through different metrics present in Microsoft Azure Cosmos DB. For enhanced productivity, it would be better to add information about the new features to the Microsoft Azure Cosmos DB admin dashboard itself. We usually have to rely on YouTube tutorials or the official documentation. 

Furthermore, while it is supported regionally, I did experience a rare case during our working time where it went down on their end and showed faulty previous data. Better error handling would be beneficial. We had to go to forums to check if it was failing for everyone else. It was surprising that a large organization like Microsoft doesn't provide an official statement about the maintenance or issues that could impact our overall usage.

I would rate the customer support of Microsoft Azure Cosmos DB a seven out of ten. The reason for deducting three points is that when you raise a support request, you don't know who will respond. Sometimes, the assistance is very helpful and effective, while other times, it might not meet expectations.

Neutral

It didn't take much time. We had a meeting for deploying certain elements, along with two environments for development and production, and completed cost estimations in one to two days. It took us about one to two weeks to spin up everything. We didn't only create Microsoft Azure Cosmos DB; we also migrated our data from the existing dataset to the new one. It took about a week. We were a small company starting up, so we didn't have that much data. If this involved a larger company, it would have taken one to two months of effort.

Initially, using Microsoft Azure Cosmos DB was uphill because we were just beginners, but it then got easy, and I was enjoying my ride. It was seamless; there was support for different language stacks. From that perspective, it was easy. We didn't need many tutorials or helper guides for it. We just read the official documentation, which made it easy to get hold of it.

The learning curve for Microsoft Azure Cosmos DB is straight; it's not steep. I didn't have extensive prior knowledge, but I followed the official documentation and a Kubernetes course recommended by a senior. After a few days of completing that course and reviewing a few documents, I was up and running.

Initially, our environment size had about three developers, which scaled up to four or five. Eventually, it included non-developers and an ML team. We were a small organization, so it never scaled over 10 developers, and including clients, it never went over 30.

Microsoft Azure Cosmos DB helped decrease the total cost of ownership. When I joined the organization, we were shifting from AWS to Azure. We were part of the Microsoft for Startup Founders Hub and had credits from their end. While trying to establish multiple PoCs based on our investors' suggestions and our client's recommendations, we aimed to have a data warehouse for clients' data for better future project developments and for enhancing current offerings or eradicating features from the current stack. 

That helped with cost estimation for the overall project and different features we gave, such as the image generation feature, which was one of the main client demands. We spun up an image generation model in Azure Machine Learning Studio, connected its data to Microsoft Azure Cosmos DB via a pipeline. The costs spiked for us, so we added a register cache on the frontend, and in the backend, we created a workaround to directly store the most searched or most recently created images into BLOB storage linked to Microsoft Azure Cosmos DB. This allowed faster access compared to re-generating through the entire pipeline, which also contributed to reducing our costs.

If you are a small organization or startup building from scratch without the Microsoft Startup Founder Club support, it could be expensive. However, if you have the budget and your use case leans more towards AI, Microsoft Azure is leading in AI integration compared to other cloud service providers, giving you an edge. If it's about the latest AI, especially LLM RAG, which often involves vector embeddings, Microsoft Azure Cosmos DB can handle that.

For mid-tier organizations that have thoroughly analyzed the data migration costs and potential new charges, Microsoft Azure Cosmos DB could be a viable option. For top-tier organizations, it's a better route to go through Azure itself.

It handles semi-structured data and unstructured data efficiently, which worked for us because we dealt with images, videos, and other multimedia formats that couldn't be structured properly. However, there was some uncertainty with increasing the RUs and other elements, which complicated things because when you increase the RU and limit it to say 800 or 1,000, even though you are not reaching that limit, you're still paying for it, which is a disadvantage for a startup. You're burning money for that.

We didn't have huge amounts of data to assess in Microsoft Azure Cosmos DB, but it was efficient. Its efficiency also depends on how you've configured it.

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

Our use case for Microsoft Azure Cosmos DB is storing track and trace data, mainly for regulated markets.

The recent introduction of autoscale V2 has been a big benefit for us, as well as the compression has helped us reduce our costs without much impact.

It's a platform as a service; it definitely helps us operate better. We are not a big company. We have 200 people. It would be impossible for us to run the systems without a platform as a service.

It is pretty fast to learn the basics. However, when it comes to optimization and understanding all the details, it takes a little bit longer. Its learning curve is pretty short. It's pretty intuitive.

I would assess Microsoft Azure Cosmos DB's ability to search through large amounts of data as an eight out of ten.

What I appreciate most are the latency and the access, which are guaranteed by the tool, which is really impressive. I appreciate that it's a platform as a service that allows me not to think about capacity or operation, which makes a big difference for us.

What is missing in Microsoft Azure Cosmos DB is definitely cold storage. We know it's coming, but that's currently what is missing—the possibility to park older data in a cold tier. Aside from the storage, we are pretty happy with the rest.

I have been using this solution for six years.

I would rate the stability of Microsoft Azure Cosmos DB a nine out of ten. It is super stable.

Microsoft Azure Cosmos DB is highly scalable. I would rate it a nine as well, although we sometimes encounter data center capacity issues because we are in the top three biggest instances of Microsoft Azure Cosmos DB.

Our clients are enterprises. We have 20 people working with this solution.

We have regular contact with the product group, who listen to us to optimize our consumption and help us improve our solution to get more benefit from it. We had one incident, and they were very supportive during the incident, resolving it within the SLA, so it has been a good experience.

I would rate the technical support for Microsoft Azure Cosmos DB an eight out of ten.

Positive

We did not test or use another solution. We went with Microsoft Azure Cosmos DB from the beginning, so I cannot really judge any improvements compared to what we were doing before. My experience is only with Microsoft Azure Cosmos DB.

I would rate its initial setup a nine out of ten. Implementing the solution takes weeks, but the deployment of a new instance takes less than a day. 

I believe Microsoft Azure Cosmos DB has decreased our total cost of ownership by clearly decreasing operational costs; the solution is highly reliable. On the other hand, the cost of the tool is still pretty high, which is a common complaint among customers. Looking at the spread of Microsoft Azure Cosmos DB on our total Azure landscape, it is by far the biggest cost point, so it is still expensive, but it is highly reliable and high-performance.

It is pretty easy to use, but it is tricky to optimize because of the way the pricing works. You need to understand exactly the details of how the pricing works technically to stay within reasonable pricing.

We do not utilize the built-in vector database capability yet, but we have plans to.

I would recommend Microsoft Azure Cosmos DB to other users. I would highly recommend digging into the details of how it works behind the scenes and discussing with the technical team prior to implementation to avoid mistakes that could lead to a gigantic invoice at the end of the month for nothing. Ensuring a good understanding of how it all works.

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

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.

In my place of work, we use Microsoft Azure Cosmos DB to store data for our services and mobile and web applications.

The problems we were trying to solve with Microsoft Azure Cosmos DB primarily involve storing data that is not relational, such as hierarchical documents and data. It helps to store data and scale up when we are pushing large amounts of data.

We need to scale data. With Microsoft Azure Cosmos DB, we are able to scale up when processing large amounts of data or scale down as needed. It's also lightweight.

There is ease of use, especially for managing lots of non-relational data, which is easier to use than relational data. Azure is our enterprise solution, so we utilize Microsoft Azure Cosmos DB as it offers resources to store and manage non-relational data. Given that we run an e-commerce site, we have a lot of non-relational data, especially for items and all related aspects. It's very helpful because alternative solutions using relational data would not fulfill our needs. It effectively stores hierarchical data and data without defined relationships, making it quite useful for our organization.

Using Microsoft Azure Cosmos DB is fairly easy, but most importantly, the availability of documentation and community makes it easy to research information. For instance, if there are certain queries I want to run, I could easily go online. There's also the Copilot feature that helps generate the query I need to retrieve the data I want. It's fairly easy to use, and there are many tools to assist in utilizing the resource.

The best features of Microsoft Azure Cosmos DB include the speed to query data; as long as you index properly, retrieving data is fast and lightweight. 

Additionally, the scalability to scale your RUs when pushing large amounts of data to Microsoft Azure Cosmos DB and managing the speed or latency for read and write is crucial, depending on how you set it up.

Areas of improvement for Microsoft Azure Cosmos DB include indexing. While it makes data retrieval easier, it also increases costs. If there's a better way to improve indexing at a lower cost, that would be really helpful, but that's the major point for now.

I have been using Microsoft Azure Cosmos DB for almost three years.

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

For scalability, I would rate Microsoft Azure Cosmos DB a ten out of ten. The technology department that utilizes Microsoft Azure Cosmos DB spans approximately 500-1000 users. We have multiple locations and departments using Microsoft Azure Cosmos DB.

It offers us scalability. It saves cost because the system scales up when we have an enormous amount of data going in to allow a large flow of data. Once we are back to low traffic, it scales down, saving costs.

I would rate the technical support for Microsoft Azure Cosmos DB a seven out of ten.

Neutral

I have not used other vendors; I have only worked with Microsoft. Therefore, I don't have a comparison to make.

It took a couple of hours.

The learning curve for onboarding with Microsoft Azure Cosmos DB is quite short; as long as you get the portal, it is fairly self-explanatory on how to navigate around it.

The maintenance required for Microsoft Azure Cosmos DB is minimal. Maintenance is needed when Microsoft rolls out an update or when ensuring that our code packages are using the correct version of the Cosmos NuGet packages. It just means we need to stay up to date with the documentation.

I cannot quantify how much time or resources Microsoft Azure Cosmos DB saves me because I do not primarily work with cost management. I mainly focus on research and setup, but I think it saves costs—perhaps a few hundred pounds if we don't scale up. Putting it into perspective, it saves costs between 10% to 20%.

It is cost-efficient as long as you understand the right setup to optimize usage. Knowing the data needs of the organization and adjusting the Microsoft Azure Cosmos DB usage accordingly helps save costs, but if you don't know, you could end up spending more than necessary.

We do not use the built-in vector database capability of Microsoft Azure Cosmos DB; the nature of my department requires us to use the regular storage capability.

I would recommend Microsoft Azure Cosmos DB to other users without hesitation. I would rate Microsoft Azure Cosmos DB an eight out of ten.

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.