Microsoft Azure Cosmos DB Reviews

As the technical lead of the Microsoft Azure Cosmos DB team in my previous company, I helped our customers. We had a team of around 20 people. We addressed any issues our customers faced when using Microsoft Azure Cosmos DB or related services. Once resolved, I worked directly with our operation manager to engage with customers, checked their user experience, gathered feedback, and made improvements. This work was primarily managed by a manager who collects feedback and monitors KPIs to improve our service.

Microsoft Azure Cosmos DB is very easy to use once you understand the process, and we have a very good team. Because it is more costly compared to other services, the Microsoft product team takes customers very seriously. If any issue arises, they immediately join calls with customers to troubleshoot problems.

Microsoft Azure Cosmos DB has significantly improved the quality of search results, making searching easier compared to other services such as ADF, data factory, or SQL databases. Compared to AWS, Microsoft Azure Cosmos DB is user-friendly and offers robust features.

The Microsoft product team is proactive and engages with customers, helping to update features and resolve issues promptly, demonstrating a commitment to customer satisfaction. The learning curve for Microsoft Azure Cosmos DB is manageable, as it didn't take much time for me to grasp the basics. With the right information, even new users can learn the fundamentals in about two to three months.

For areas of improvement in Microsoft Azure Cosmos DB, the cost from the RU perspective needs attention. The cost structure differs for internal and external customers, causing frustration among some internal customers. Additionally, outside of SQL and Mongo APIs, there is limited support for the APIs. Developing new features compatible for customers beyond SQL and MongoDB would be beneficial, and reducing the overall cost would make it more accessible for startups.

I have been using Microsoft Azure Cosmos DB for more than 2.5 years.

The stability of Microsoft Azure Cosmos DB is generally good, though there are instances of outages. I would rate the stability at seven because there is room for improvement. 

The scalability of Microsoft Azure Cosmos DB rates at six. We have documented guidelines to help customers scale, but there are still some issues where customers struggle with scaling down after scaling up. It is straightforward, but some customers might need more guidance on using the Cosmos capacity calculator before scaling up. Customers should be able to scale down easily without needing detailed formulas.

In our organization, about 100 users specifically worked with Microsoft Azure Cosmos DB. This technology is utilized across almost every organization today, and Microsoft provides robust support that is taken very seriously. 

Our clients ranged from small to enterprise businesses, and we managed support requests from various types of customers, including premier customers who required extensive assistance.

The technical support of Microsoft Azure Cosmos DB deserves a rating of eight because I have experience with other services where assistance takes longer. In other services, there are multiple layers to check, but with Microsoft Azure Cosmos DB, we can directly reach out to the Microsoft product team members who are developers, and within a day or two, we can get on a call with the customer to help them with their issues and suggest best practices. This quick support is not seen in other services, where it can take five to ten days.

Positive

I observed many customers migrating their data from native MongoDB to Microsoft Azure Cosmos DB, indicating significant improvement.

Microsoft Azure Cosmos DB stands out in comparison to AWS, specifically with DynamoDB. Microsoft Azure Cosmos DB offers unique and cost-effective features that AWS does not. Additionally, it supports various configurations beyond just SQL or Mongo, such as the Table and Gremlin APIs, which many customers prefer.

The deployment of Microsoft Azure Cosmos DB is very easy. With the right approach, migration can be done smoothly and quickly.

I was using the built-in vector database when I was with the previous organization. There are vector search capabilities and other related features.

I recommend Microsoft Azure Cosmos DB to other users because it has significantly improved, especially concerning visible outage scenarios. The portal now provides clear workload choices for production and testing accounts, making it easier for customers to decide what they need. 

I would rate this solution a seven out of ten.

We are basically a system integrator, so we use Microsoft Azure Cosmos DB in multiple projects for different things, often when migrating from other hyperscalers. We do many AWS to Azure migrations. It's our go-to solution, given its flexibility on the SQL driver and the MongoDB driver. When running a NoSQL database, it's our preferred choice. Recently, with the AI wave, we've been using it as our backing store for many things, from vectors to structured or somewhat structured data. 

One of the scenarios in which we have used the MongoDB driver on Microsoft Azure Cosmos DB was an AI project with the NFL. It was called the NFL Combined Copilot, and we needed to ground data and provide real-time insights to scouts and coaches on the sidelines. It had to be fast and precise, with significant stakes involved. The experience was fantastic in terms of performance. One of the most critical aspects was that there was no room for error - it's five days in February with 350 athletes and 32 NFL teams present. It needs to work, scale, be precise, and bring the required results, or you must wait a year. This has been one of the places where we have pushed it to the limit regarding availability, scalability, and the whole concept of search and grounding for AI applications.

Using Microsoft Azure Cosmos DB and getting started with it is super straightforward. As you scale and adapt along the way, it remains fairly easy to work with. However, as the complexity increases, one challenge is that you need to be mindful of properly structuring your data for world-scale applications. Fortunately, there is plenty of guidance, documentation, and examples available to assist with this.

As a developer or development firm, one of the aspects we appreciate most is the ability to prototype effectively. We can take a project from the initial prototype stage to production-ready status without the need to redeploy the database or switch products. This approach allows us to use the same tools for both prototyping and scaling. It's important to note that you don't have to face a super complex scenario to benefit from this product. It is well-suited for prototyping and remains capable when transitioning to world-scale applications.

With the current AI wave, the built-in vector database capability of Microsoft Azure Cosmos DB for model grounding or the RAG pattern is crucial. Previously, we had to consider alternatives such as Pinecone and other third-party software, dealing with all the problems of designing, scaling, and maintaining the database. Microsoft Azure Cosmos DB enabling this feature allows us to get it out of the box with familiar tools and context, along with the benefits of its scalability and elasticity, providing excellent support for the highly relevant RAG pattern for AI search.

We have developed several AI scenarios, one of which was recently highlighted in Gartner research. This scenario involves discovering multimodal media within the context of sports, showcasing how organizations like the NBA and NFL use Azure to locate specific pieces of content through interaction with an agent. This was built using the vector database functionality we have integrated.

The peace of mind that Microsoft Azure Cosmos DB provides regarding global distribution is invaluable. In traditional databases, you need to consider how to scale, whether horizontal scaling is possible, and handle multi-regions, multi-masters, redundancy, and other concerns when building a world-scale solution. We get most of these features with Microsoft Azure Cosmos DB essentially included.

I would discuss two separate streams. The first concerns the local developer experience. Microsoft Azure Cosmos DB is a complex cloud platform service, and when developing applications, the most legitimate way to test it is by using the actual product. The ability to run an emulator locally would reduce development costs and improve accessibility, eliminating the need to provision it for each developer. When developing an application, developers typically run everything on their own machine. With Microsoft Azure Cosmos DB, to get the exact same experience and features, we end up using it in the cloud on Azure and paying for it during development. As we add or remove developers from the project, we need to provision new databases or instances. Having the ability to run an emulator or replica in the local development environment would be fantastic for cost savings and developer onboarding.

The second area involves tooling around projected costs for queries. Microsoft Azure Cosmos DB has a unique way of using units to charge for CPU or compute while running queries. Having a calculator to determine query efficiency and expense based on current data structure and projected volume would be really interesting. However, if I had to choose one improvement, it would be the local development experience.

We have been using Microsoft Azure Cosmos DB since its release, approximately eight years ago, and we have witnessed its entire journey.

The resiliency aspect makes Microsoft Azure Cosmos DB our go-to solution for databases. It has the ability to run in multiple data centers. If there happens to be an outage, which is unlikely, you still have spare nodes and replicas available. The SLA ends up being extremely high from an overall service perspective. Having the flexibility to continue operations even if one Azure region goes down is significant, as you can still write to it and restore functionality when the region returns. With traditional database engines, you would need to implement complex workarounds, such as restoring backups in another location and attempting to sync back to the original location. The stability is excellent, and its resiliency in globally distributed deployments is outstanding.

The scalability is excellent, though it comes with associated costs. When you need more replicas, regions, or additional resources, you will need to pay for them, but you maintain the ability to scale. This contrasts with deploying your own database, where you would need to handle maintenance, and scaling to required volumes might not even be possible due to engine design limitations. Microsoft Azure Cosmos DB has been built with scalability in mind, which is evident throughout the product deployment. The ability to configure regions and replicas is crucial, and it feels unlimited in potential. As long as you can accommodate the costs, you have the opportunity to expand and improve the SLA without re-architecting the entire solution.

I have used MongoDB and AWS Aurora in different combinations, such as self-hosted MongoDB, MongoDB Atlas, Aurora, and Postgres. Compared to others, what stands out about Microsoft Azure Cosmos DB is its scalability. When working with MongoDB or traditional SQL databases, horizontal scaling and multi-region/multi-master scenarios are complicated topics that require significant work and planning. With Microsoft Azure Cosmos DB, it's simply a matter of flipping a switch. Though there is a cost involved, it removes many complexities and saves our team considerable time.

It's really straightforward and easy to get started with Microsoft Azure Cosmos DB. One of the main advantages is its compatibility with various drivers. For example, if you are migrating an application from MongoDB, you can use the same MongoDB driver to interact with it. The same applies if you're using SQL or DocumentDB; you can leverage the existing code with minimal changes. This is a significant benefit, especially in scenarios where you might be considering a switch in database engines. Often, developers worry about having to revise their entire application when changing databases, but with Microsoft Azure Cosmos DB, that's usually not necessary. For developers familiar with DocumentDB or MongoDB, the ability to use the same libraries and code brings a sense of familiarity, which is a major time-saver. Additionally, provisioning through the Azure portal is a breeze—it's as simple as clicking a button to get started.

The initial setup took less than an hour to do properly, approximately half an hour.

It does not require any maintenance, but as software systems are living and breathing things, you might need to adjust usage patterns and queries for efficiency. Compared to running your own database, there is no maintenance - you don't need to worry about indexes, drives getting full, or CPU scaling.

The implementation was completed by one person.

The pricing for Microsoft Azure Cosmos DB is good, but there is a developer factor to consider. It could be economical or expensive depending on usage. Guidance about query consumption of Request Units (RUs) would be beneficial, especially since costs can escalate if not used properly. When building solutions on Microsoft Azure Cosmos DB as intended, following guidance and documentation, it works well. Compared to traditional databases, it has a different pricing structure that factors in multi-region capabilities, number of requests, and multi-master functionality. While traditional managed databases simply consider CPUs, memory, and bandwidth, Microsoft Azure Cosmos DB's pricing involves more variables. When used properly, it can be more cost-effective, offering better value due to the included multi-region capabilities, which are quite expensive to implement in traditional database settings.

My advice is to start with the drivers you are most familiar with. If you have experience working with MongoDB, begin using Azure Cosmos DB with the MongoDB driver and the code you already know. From there, you can gradually learn about specifics such as request units (RUs), indexing, and partitioning—elements that contribute to what makes Microsoft Azure Cosmos DB powerful and scalable. By leveraging SDKs and libraries you are already accustomed to, you'll have one less thing to worry about: how to use the platform effectively.

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

Our primary use case for Microsoft Azure Cosmos DB is as a solution for customers who are not necessarily migrating an existing application but are looking to build something more cloud-ready and scalable. The objective is to provide a scalable and flexible database solution that does not require the compatibility requirements of Azure SQL, allowing for fast data access across regions without latency concerns. They are not looking for all the compatibility requirements for Azure SQL, but they are looking for something that they can scale quickly without latency.

I found Cosmos DB to be rather intuitive and straightforward. The documentation is pretty clear because it is a managed service. I could give the custom developers their endpoint and even set up managed identity in a way where we do not have to worry about having secret keys and all of those pieces. We are using private endpoints for everything and found it to be working just as advertised.

The most valuable features include the global write capability, which allows customers to read and write across different regions simultaneously, enhancing performance and availability. 

A lot of my customers like the ability to choose a different API with which they are familiar. The flexibility to choose different APIs, such as MongoDB or Cassandra, allows customers to leverage their existing knowledge while using Microsoft Azure Cosmos DB. 

We are using the vector database a little bit. We often use Azure AI search for that capability. We have an application that is taking in legal documents and needs to do a semantic search against those. It is a combination of using the embedding models and vectorization to get closer to the right chunks of the documents that they are looking for. We, in turn, send that over to Azure OpenAI services to fine-tune and get the best result from our initial results.

We integrate the vector database with another application. It is a custom-built homegrown application that provides a UI for their end-users to be able to use AI search and vector search to be able to get highlighted results of their PDFs.

The vector database absolutely improved the search result quality of our customer's organization. They are partially using Microsoft Azure Cosmos DB in that, but, in general, the two combined absolutely did help by not defaulting only to keyword search and being able to do a hybrid between the two.

For this project, there has been significant improvement in the time to process these documents. There has been a 5x time reduction for the end users in finding the data they are looking for, inputting it into their model, and performing their workflow.

In terms of Microsoft Azure Cosmos DB’s ability to search through large amounts of data, for this specific use case, we are probably on the low end of what Microsoft Azure Cosmos DB can accomplish. We have a decent dataset, but definitely not a gigantic one. So far, our experience has been great, but we are not necessarily testing it to its limits. The one that we are working on is still under a terabyte. We only have several hundred gigabytes for this specific customer. It is a lot of data, but in the grand scheme of things, it is not very much.

Continuing to educate customers on how they can take better advantage of Microsoft Azure Cosmos DB without having to completely rewrite their entire application paradigm would be beneficial. They can help them understand that there are multiple options to interact with it. They do not necessarily have to start from scratch. They can refactor their existing application to be able to use it better.

They can continue to find better use cases for it. It helps to be able to show our customers example documents or example applications. It definitely helps us to be able to show customers how they could be using this.

I have been using Microsoft Azure Cosmos DB for around a year to a year and a half.

The latency and availability of Microsoft Azure Cosmos DB are fantastic. It provides resiliency and business continuity without having to do much. Having it already built in is a big selling point.

I am not working with any customers who are going to have any problems with scalability. We are not going to push the limits of what it can do. My customer base does not have to worry about scaling because none of their applications are ever going to struggle with something as global and as resilient as Microsoft Azure Cosmos DB.

Microsoft Azure Cosmos DB’s dynamic scaling helped decrease the overhead costs for our customers. They have spikes, but most of the time, they have a pretty low baseline. Rather than overprovisioning to handle those spikes, they are able to settle in and ride the waves of their utilization throughout the days and weeks. They have seen a decrease in costs and expenditures. It is still early for a lot of it because a lot of new functionality was added. They did not necessarily have a true baseline to compare against, but they like the idea that it is so elastic.

The onboarding process was relatively quick, taking about six to eight weeks, as the team adjusted to using Microsoft Azure Cosmos DB.

We have not run into many challenges during the migration or implementation of Microsoft Azure Cosmos DB other than being novices and unfamiliar with it. We need to understand all the different components of it, but we have not necessarily run into any technical problems or issues with timelines or things like that.

It takes only a couple of months to onboard customers with Microsoft Azure Cosmos DB. We are able to go pretty quickly. Our onboarding path is about six or eight weeks.

There is a bit of a learning curve for the customers who have only worked with traditional Azure SQL VMs and are not familiar with having a fully managed or PaaS instance. There is some learning curve for them to understand that they do not just have x number of cores or memory available, and it just grows as they use it.

In a couple of use cases, Microsoft Azure Cosmos DB helped decrease an organization’s total cost of ownership. Oftentimes, when we are implementing some of these features, we do not have a baseline to compare against. In my own experience, there definitely is an opportunity if we are able to use the model to reduce cost instead of provisioning a VM or something like that, as we would historically do. It is hard to provide metrics, but when I have done comparisons or cost calculations, I have sometimes personally seen as much as 25% to 30% savings.

Most customers like the flexibility of the pricing model, and it has not been an issue. They can start small, and the cost grows with adoption, allowing efficient management of the budget. Its pricing model has not been a concern at all for any of our customers. They understand it. It is simple enough to understand. Oftentimes, it is hard to forecast the RUs, but, in general, it has been fine.

I would rate Microsoft Azure Cosmos DB a nine out of ten. There is always room to grow, but it is a highly capable solution. I am looking for more opportunities to use it as we help customers move toward more cloud-native technologies, rather than always defaulting back to what they are familiar with, which is sticking with Microsoft SQL Server or Azure SQL.

We use Microsoft Azure Cosmos DB emulator to display database contents and occasionally perform manual data edits when necessary. We utilize it for general database emulation tasks.

It has been very efficient so far. The team has been using it for quite a while. I am new to the team, but they always talk about how efficient it is. We are using the NoSQL version. It is easy to use for development. It is reliable and quick. 

It has been pretty efficient when it comes to search. I have no complaints about that. It is easy to use and very compatible with Java.

I had a challenging experience implementing the emulator with a Mac. I had to install the emulator in a Docker container because it is not natively compatible. A significant amount of time was spent researching how to enable HTTPS communication when connecting the container and the emulator. I encountered TLS and SSL errors but resolved most of them by setting an environment variable in the container and using HTTPS protocol communication. I also had to use gateway mode with the Cosmos client in my Java app. I am disappointed with the lack of compatibility of the Microsoft Azure Cosmos DB emulator with Mac. I also found a scarcity of online resources regarding this issue.

It would be great to include compatibility with various databases like graph databases, adding to the existing NoSQL and MongoDB compatibility. I have used that for various projects on other platforms, and such additions would be beneficial.

I have been using it for about a week now.

I do not see any stability issues. I would rate it a ten out of ten for stability.

It is scalable. I would rate it a ten out of ten for scalability. We have had no issues with its ability to search through large amounts of data.

We have thousands of users. We are a big organization, and it is being used at various locations.

I love the community forums. They provide a wealth of useful information, which gives me an advantage when it comes to support. The only disappointment was not being able to find any information about setting it up on a Mac.

Neutral

I have used the cloud-based Firestore database and MongoDB before. They largely perform similar tasks, and I have no problems using either one. They work and get the job done.

For me, the setup was not complex because my team had everything ready.

I watched a couple of videos on YouTube. The onboarding was seamless, especially the database part. It took me no more than two days to learn the basics and necessary setup.

In terms of maintenance, it does not complain if you do not update it, but there are always updates that you can add. For example, for the emulator that I am using, there are a lot of versions I can install, but it works with most of them.

I have no complaints. It does its job efficiently and is easy to set up. Our organization has been using it for quite some time. They must see a value in it. Otherwise, they would go for a better technology in terms of performance or pricing.

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

We use it primarily to log all events for a particular user and product. A particular users are logged in to see if a product has been modified. If someone modifies the data, we log that information along with the email. This helps when we need to compare modifications to a product.

Our admin section benefits greatly as Cosmos DB makes it easier to track down the history of product modifications, including the initial price, the current price, who modified it, and how much it was modified.

The search is slower than SQL but faster than MongoDB and other document databases.

Cosmos DB is a document database that stores data in JSON format for faster retrieval of unstructured data. I personally appreciate the speed, which is significantly better for unstructured data, especially since Cosmos DB had JSON as a data type early on.

It's pretty easy to use and optimize since it's unstructured data. It sometimes takes time since it's in JSON format, but it's useful in the admin section. The learning curve isn't long if you have some SQL knowledge because the queries are similar. It's straightforward for anyone with database exposure. 

We don't use the vector database, but we're aware of it and we know that it will allow faster retrieval with Azure AI integrated.

The UI needs enhancement. Unlike SQL, Cosmos DB's UI is not as straightforward, making it a bit challenging to use efficiently.

I have been using Cosmos DB for the last seven years.

We have not experienced any downtime. The retrieval is significantly faster compared to using SQL for storing JSON data.

We have had no issues with scalability. It works well for us, fitting seamlessly into our workflows.

We evaluated DocumentDB and other document databases, but since Cosmos DB is a Microsoft product and integrates well with Azure, it was the preferred choice. 

Like any technology, it took a little time to learn Cosmos DB. It was relatively straightforward. We had to watch a few videos on how to set up particular databases, indexes, and keys. 

Cosmos DB has definitely improved our organization's cost structure, but I would need to check the specifics to provide exact numbers.

The Cosmos DB pricing model is reasonable, especially since we use it for backup operations rather than front-end processes. We have been using it for several years and continue to do so.

We compared Cosmos DB with DocumentDB and other document databases.

I would rate Azure Cosmos DB an eight out of 10. There is potential for improvement, especially in the UI, which can be cumbersome to navigate.

My main use cases in my company for Microsoft Azure Cosmos DB are to store data for semi-structured and unstructured data and to retrieve data for data agents.

The feature I have found most valuable in Microsoft Azure Cosmos DB is its scalability and speed.

Microsoft Azure Cosmos DB can scale quite fast and easily, and you can store a lot of data, so I believe that is the biggest advantage of it.

I evaluate the enterprise-grade security features of Microsoft Azure Cosmos DB in terms of data encryption and access control as a positive implementation because data security is important today, so it is very beneficial.

These features have helped improve my company's data security strategy because every client, as I work in a consultancy, wants their data to be secured, and nobody wants it to get leaked. The features already implemented into Microsoft Azure Cosmos DB help to make our job easier.

I think it could be better if it included more in regards to AI or if it were more exposed to AI. I find it straightforward as you store whatever you want and then train the models and fine-tune the models.

I have been using Microsoft Azure Cosmos DB for around six months, as they introduced it relatively recently.

In my experience, the global distribution and multi-region replication of Microsoft Azure Cosmos DB have not significantly influenced the performance and availability of my applications because we work primarily in West Europe. I did not experience much multi-regional functionality as we are based in one region and work in one region.

Microsoft Azure Cosmos DB can scale quite fast and easily, and you can store a lot of data, so I believe that is the biggest advantage of it.

I have utilized Microsoft Azure Cosmos DB's multi-model support for handling diverse data types to some extent, but not extensively.

I would assess Microsoft Azure Cosmos DB's automatic and elastic scaling of throughput and storage for my current projects as quite good, as it is fast, easily scalable, you can store a lot of data, and you cannot see significant latency.

I have minimal interaction with customer service and technical support because we have salespeople and more tech-related sales representatives who handle all the talking and requirements gathering. I am more of a tech-savvy technical specialist who implements everything.

Neutral

Before choosing Microsoft Azure Cosmos DB, the company I work for did not use another solution. I have had some exposure to AWS, but now I am in the Microsoft stack.

For me and my colleague, the deployment process for Microsoft Azure Cosmos DB is quite easy and not complicated.

The biggest return on investment for me when using Microsoft Azure Cosmos DB is that you can store everything—not only structured data or unstructured data, but everything. You can also integrate it with AI, which I believe is the best investment.

I do not believe my company is considering other products instead of Microsoft Azure Cosmos DB because we are currently very happy with the product and what Microsoft is doing by integrating Microsoft Azure Cosmos DB and AI Foundry. We also received news that it is a DocumentDB as well, so we will stay within the Microsoft tech stack.

I would say the main difference between AWS and Microsoft is that I prefer Microsoft since, in my opinion, it is more user-intuitive and everything is on one platform. If you want to do Fabric, everything is in one place, and if you want to do Azure, everything is still in one ecosystem, so you do not need many third-party applications to do your job.

From what I have used, I believe the tool is quite good.

Microsoft Azure Cosmos DB is currently quite good, and I do not have any enhancements I would recommend since I am not a heavy user, having used it for about six months.

My advice to other companies considering Microsoft Azure Cosmos DB is to simply try it, and you will love it. I would rate this product a 9.5 out of 10.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

I have been using Microsoft Azure Cosmos DB for the last five years for IoT-based data saving and other purposes. We use non-structural data for various reasons. For instance, we are using artificial intelligence to save multiple data sets coming from different sources.

It is a managed service, so we do not want to worry about other aspects.

One valuable feature of Microsoft Azure Cosmos DB is partitioning. Its performance is very nice. I use it mostly on the Microsoft backend, particularly .NET and .NET Core technology. From deployment and accessibility aspects, there is significant performance improvement.

Additionally, consistency is noteworthy. For example, we have people spread across multiple locations. If they update data in Australia, we can access it in another location within a fraction of a second. That is an impressive feature of Microsoft Azure Cosmos DB.

It is very good from the optimization and usage point of view. It is very user-friendly. Microsoft also provides support from the performance aspect. They support us from the optimization and scalability aspects.

The query searching functionality has some complexities and could be more user-friendly. Improvements in this area would be very helpful.

We have multiple applications. Our applications are running in different environments such as AWS and Azure. We are able to give flexibility to AWS to access this data from Microsoft Azure Cosmos DB. We have created an interface between them through APIs. Through the APIs, the AWS applications can consume the data from Microsoft Azure Cosmos DB, but we have seen some slowness or latency, whereas with Azure, we see better performance. Our AWS is in the Eastern zone, and people in the Western zone have some latency.

I have used the solution for five years.

We are seeing some latency issues with AWS. It offers good availability. 

Being serverless, the scalability is very good.

We pay for the support. We are happy with their support. If we face any challenges initially, they provide us with a resource to answer all our questions.

Positive

Previously, we used MongoDB and DynamoDB, though not extensively. Because of client preferences and their use of Azure, we chose Microsoft Azure Cosmos DB. DynamoDB uses clusters, which affect costs.

The initial setup can be somewhat tedious. We have to set up things, run them, see the results, and fine-tune them.

The initial setup took more than one month. After that, everything became automated. Now, if we want to deploy it in another location, the operational team typically takes one week. They verify whether everything is working properly or not. By using the automated scripts, we can deploy it at other locations.

We have a separate team for configuration. We also get support from Microsoft.

Its price is in the middle, neither too low nor too high.

We are happy with the usage of Microsoft Azure Cosmos DB for our use case. In terms of learning, it is of medium complexity. It is neither very tough nor very easy.

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