Microsoft Azure Cosmos DB Reviews

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

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

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

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

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

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

It provides good SLAs and requires less effort in maintenance.

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

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

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

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

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

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

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

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

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

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

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

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

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

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.

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

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

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

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

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

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

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

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

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

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

Positive

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

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

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

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

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

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

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.

Our primary use case is mirroring the data for reporting.

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

Azure Cosmos DB's graph queries are its most valuable feature. Although I have not yet explored vector search, it's coming to Cosmos DB, and I plan to look into it. Having data in a flat-file format in a document database speeds up processes, which is the primary purpose. Additionally, Cosmos DB's use of the Mongo platform makes it intuitive and cost-effective.

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

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

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

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

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

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

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

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

Our experience with the pricing and setup cost is that it aligns with what we expect based on the pricing we see. However, I would absolutely like it to be less if possible.

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