Microsoft Azure DocumentDB Reviews

The biggest use case for my team, as we are a BI team with six data engineering teams, was to connect to Microsoft Azure DocumentDB, get the data in the format that my team's application WebFOCUS will utilize, and read that data while adding coding and reformatting the data according to WebFOCUS standards.

This allows us to publish reports so that all stakeholders of our organization, such as Bank of America, Bank of Scotland, and all global banks in the case of Mastercard, can access these reports. The primary source is the data coming through this database, and then WebFOCUS will connect to it, get the data into it, reformat it, and add coding to publish reports in different formats, whether graphical, analog, digital, bar chart, histogram, or pie chart.

The language used to fetch data from Microsoft Azure DocumentDB is very simple and user-friendly.

If anything in the technical world is simply built, developed, and put to use, that is the unique selling proposition. In comparison, Influx has its own language that requires learning, and Power BI query is sometimes very complex.

For Microsoft Azure DocumentDB, it is very simple, and you can read the data very quickly. Any first-time user will also adapt to it in a day or two.

The ACID framework is utilized not just in Microsoft Azure DocumentDB but in other formats as well. Cost optimization through the pay-as-you-go model is very critical and already available. Support for data lakes and data warehousing is very critical and already provided. Seamless connectivity to Power BI for dashboard and reporting is the biggest advantage, as nobody in the IT industry would not have heard of or seen a Power BI report. Microsoft Azure DocumentDB integrates very seamlessly with Power BI.

All Microsoft applications, whether database or analytics, are a little costlier compared to Google products, and this is a barrier that needs to be crossed so that all its applications become more affordable.

For example, startups typically go for free tools. Microsoft should offer these products for free to startups or perhaps free for the first year or two years, so that startups can use these applications, and once they grow bigger and have larger revenue, they can purchase it at a reasonable cost. All Microsoft tools need to cut down on their price and have a better strategy around pricing for the masses. If you sell only to ten customers, your revenue is lesser, but if you give something for free to some customers, two years down the line they will purchase it.

Microsoft Azure DocumentDB still needs the ability to connect to other analytics easily without any additional integrator. For example, if I can connect to Google Analytics or other analytics that are not Microsoft tools using this database, that would be a good source of revenue. If I have already purchased Google Analytics in my company and I need a database, and Microsoft Azure DocumentDB can very easily connect or integrate to my Google Analytics, I would not have to think twice. Cross-company application connectivity is something that needs to be worked on.

I have used this solution for approximately seven years, on and off. In organizations where I have worked, such as SLB and currently at Mastercard, we have all Microsoft products including Power BI and database. At Mastercard, we had a BI team, and we were connecting to Microsoft Azure DocumentDB and publishing reports using WebFOCUS coding while taking data through this database.

The stability rating is 99.99 percent.

Microsoft Azure DocumentDB is never down because of its scalability feature. Since the database is never down, the application is always having the data it needs to read and publish when the customer tries to access the application. Without Microsoft Azure DocumentDB, the application is nothing, and without these two, the customer has nothing to see. The scalability factor and scaling capability are very important. You can always increase or decrease the computing resources based on the workload demand.

ACID transactions and elastic scaling are very important because at the end of the day, it is all about money. When the traffic is not very heavy for the application, Microsoft Azure DocumentDB will automatically come down on the database utilization. The number of computing resources are less, the charge to the company is less, and my company is happy.

Microsoft support is very good. Microsoft is a very professional organization, and as far as tech support is concerned, there is no problem with that.

The setup is very simple, as easy as any other primitive database.

Microsoft is the leader because Microsoft SQL server, which is the original version of the database, is also a Microsoft product. At this point in time, they are the leader. Of course, there are different databases in the market, including MongoDB and Influx databases, but Microsoft is still the king.

Influx is a time series database, and MongoDB is another database. There are also no SQL databases available. These are different products, but they have not become very big. It has been seven or eight years they have been in the market, but they have not captured the market share. Microsoft, because of its heritage, rich history, and larger network, is still capturing the market significantly.

My application connects and talks to the database as well. Because we have a Power BI team that needs data, all of it is interconnected. We have an entire Microsoft toolset, including Jira, Azure DevOps, Confluence, Microsoft Teams, and Microsoft Outlook. Everything is in Azure. I would rate this product a ten out of ten.

I usually recommend Microsoft Azure DocumentDB for medium-sized companies with a PC scale of less than 1,000 PCs, so it works pretty well with them.

It might be a little bit expensive for smaller companies; the tradeoff is between performance and cost, as a higher performance system can deliver what they want in a few seconds rather than a few minutes, which makes sense.

The impact of ACID transactions on my operations is that we have seen about 20 to 30% improvement in transactions from moving from PostgreSQL to Microsoft Azure DocumentDB.

The automated indexing of JSON data in Microsoft Azure DocumentDB has improved our query performance by almost 50%.

In Microsoft Azure DocumentDB, the features I find most valuable are its speed and performance.

I use its ACID transactions for data consistency.

One of the most appreciated aspects of Microsoft Azure DocumentDB is the elastic scaling feature. It has significantly helped manage business growth for two customers who have employed it, experiencing a dramatic rise in improvement.

Additionally, the automated indexing of JSON data improves query performance. Despite the cost issue associated with this feature, the advantages in speed and performance cannot be ignored.

To make Microsoft Azure DocumentDB closer to 10, I would like to see a columnar index as an additional feature.

I would also like to improve the analytics aspect of Microsoft Azure DocumentDB because it does not provide a lot of analytics; if that were available, it would be useful.

From a marketing perspective, one thing competitors could learn is the importance of incorporating a columnar index, as that would really help us.

I have been dealing with the tool for about three years now.

So far, I have not seen any issues with Microsoft Azure DocumentDB, although there are some issues with the SDK as REST APIs sometimes return an error, but I think that is more because of Azure than Microsoft Azure DocumentDB.

The elastic scaling feature in Microsoft Azure DocumentDB has helped manage business growth for two customers who have employed it, and they found a dramatic rise in improvement, but the cost is a big issue.

It is a bit expensive, especially concerning the elastic scale.

I used to work with Azure Database for PostgreSQL previously.

I switched purely because of performance.

The initial setup for Microsoft Azure DocumentDB is pretty straightforward.

It usually takes a few minutes.

If Microsoft Azure DocumentDB does not fit the budget, I would recommend PostgreSQL as the ultimate solution, but it does not really work as much as Microsoft Azure DocumentDB does; Microsoft Azure DocumentDB is very performant and does the work we want at a much lower cost, though elastic scale is the challenge.

We switched a few years ago because one of our customers recommended it, and we tried it out; we found it is working far better from a performance perspective.

About 80% of our customers have shifted to Microsoft Azure DocumentDB, while some are still using Azure Database for PostgreSQL.

I would not say that Microsoft Azure DocumentDB is the best option on the market now, but it is a choice for medium enterprises.

We have not integrated Microsoft Azure DocumentDB with Azure Analytics services, but that is a plan.

I have not done an assessment of the multi-region replication feature for maintaining high availability.

I would rate this review as an 8.

The use cases that we were using for Microsoft Azure DocumentDB were driven by our client, who had a lot of artifacts that they were using because they were a financial institution. They wanted to make sure that they had a good repository where they would be able to track, check in their documents, check out in a more secure manner and put this as a part of their contracting. We had to use this as part of that project as a use case.

The most beneficial features of Microsoft Azure DocumentDB that I noticed is that it appeared to be straightforward. It was ease of use; it was not complicated compared to some of the other tools that are out there. The teams were familiar with it, and it integrated well with other Microsoft products.

The automated indexing of JSON data in Microsoft Azure DocumentDB was helpful for query performance in my case. It helped ensure that we were able to retrieve the document, especially when searching. There was faster retrieval, especially when searching for a document and linking with other documents. That is how I found it quite useful.

Some of these things are seamless with Microsoft Azure DocumentDB's multi-region replication, so you might not really see the impact. It depends on how it is configured; if it is configured well, you might not recognize that the data is replicated at certain intervals for continuity. I have not been able to see the back-end, but I think we could see insights from our dataset. It is seamless, and sometimes you might not recognize what has happened or is happening in terms of availability, which is the beauty of the solution.

Regarding Microsoft Azure DocumentDB's elastic scaling feature, when you do projections of where you sit as a business, especially in financial services, it really helps us to plan for customer growth and transaction growth as well. I found that feature to be quite useful because at least you are not constrained around scaling your business.

For me using Microsoft Azure DocumentDB, there are no immediate areas for improvement right now, but perhaps it would be beneficial to have more integration, especially with Copilot and other AI models, just to make sure that it becomes easy to set up because sometimes you almost have to have technical knowledge in terms of setup and support. I would want to see more AI immersion in setup, monitoring, and support.

Regarding the price of Microsoft Azure DocumentDB, what I have seen is reasonable, given the value. However, what we also want to see is bundling of value because sometimes Microsoft tends to put tiers, and some of those tiers might not necessarily be aligned with current needs. Perhaps that is something they might want to look at in comparison to what we are seeing elsewhere. The value points are there, and we just want to optimize the pricing versus what we are getting.

I have about two decades of experience in this domain, not only with Microsoft tools but with other tools as well.

The solution is still okay; it is still fine, indicating that it is stable and reliable.

Regarding Microsoft Azure DocumentDB's elastic scaling feature, when you do projections of where you sit as a business, especially in financial services, it really helps us to plan for customer growth and transaction growth as well. I found that feature to be quite useful because at least you are not constrained around scaling your business.

The support team was helpful from a response and support point of view. I have not had issues regarding the SLAs that we would have agreed on and the response time, especially when escalating things; I have not had issues with the response times.

I used to work with operating systems like CentOS, Ubuntu, and Linux, but not anymore.

I am currently using ChatGPT cloud and the Codex model. What I am actually doing now is using it to talk to my other solutions like Microsoft in terms of automating my workflows. It is easier because I do not have to think about sitting down and creating something. I just tell it what I want to do, and it does it. I am experimenting and moving forward as we go.

I would estimate about two years of experience with Microsoft Azure DocumentDB setup, though it was on and off.

Microsoft Azure DocumentDB actually feels more familiar in the sense that it was part of the ecosystem that was already being used by the client in terms of Microsoft. At the same time, it was ease of deployment and I think it was quite intuitive for the teams to be able to adopt it and also have this flexibility to scale it and build on top of that.

For me, because in my own business, the return on investment is clear with Microsoft Azure DocumentDB because it supports my daily usage. It is seamless; it embeds within what I want to do. However, with the proliferation of the other tools that are coming up, I think we will probably start to question the value more and more. Microsoft needs to provide more for the value of the dollar you are getting. That is the pressure that is coming everywhere.

Microsoft gives you the base functionality which you can grow from, but some tools perhaps go a bit further because that is their only focus or niche. Microsoft has a bigger portfolio, which is where it becomes a challenge. However, what I have seen with Microsoft is that most of the base functionalities are there, so it is probably just the twenty or thirty percent that might not be there. There is always a trade-off because being part of the Microsoft ecosystem comes with some limitations.

Microsoft tools, especially in terms of databases like Azure SQL, Postgres, or Cosmos DB, are still important databases as part of our ecosystem. The only key issue is perhaps the use cases that we are employing them for that matter now. All of them are useful, but to me, the question is what are you building with either Microsoft or some other database.

I had experience with Microsoft Azure DocumentDB in the past and at the beginning of this year, but right now I am experimenting because of AI. For automation, what is happening now is that I have moved into AI in terms of automating my tasks. This allows me to streamline processes more efficiently. I am exploring Microsoft Power Automate's features and potential uses.

I worked on the side of a partner, where we were supporting deployment for a wider program that was for a client. With Microsoft Azure Analytics, especially from the cloud perspective, you are able to bring insights because most of what we have been looking at is the deployment of cloud solutions. I think it helped us bring insights from huge datasets that we would not ordinarily have been able to access. That is the power I saw, especially concerning data lakes that you would be looking at. That is something I found very useful, particularly in terms of the agile business models that we have; that is where I found the biggest value in terms of the analytics and insights that come out of it.

Regarding support, it is mostly about improving the user experience and just being more proactive and agile. My overall rating of the solution is an eight out of ten, where ten is the best.

My main use case for Microsoft Azure DocumentDB is for existing applications that use MongoDB APIs, and my team mostly wants Azure-managed services but prefers an open-source orientation and foundations. JSON document storage is the primary data model for us in modern application development, and it is a strategic priority for Microsoft Azure DocumentDB. Those are the main points that I keep focused on.

I worked for one of my customers where we were using Microsoft Azure DocumentDB, which mostly required throughputs for retrievals. Most of the data we stored in JSON documents were mostly IoT data, so the retrievals and storing are pretty easy. Regarding my main use case with Microsoft Azure DocumentDB, it is mostly very easy for retrieval and putting them in structured ways inside Microsoft Azure DocumentDB. It would be quite formatted and as easy as it is if we compare it to PostgreSQL.

Microsoft Azure DocumentDB has positively impacted my organization by having the same flavor as PostgreSQL.

The best features Microsoft Azure DocumentDB offers are similar flavors of MongoDB compatibles, built on top of SQL DB, and it is a stronger fit for modern applications using JSON documents, microservices, and AI applications. Additionally, it supports vector workloads, so for RAG and other applications, we can use it.

The MongoDB compatibility or architecture of Microsoft Azure DocumentDB has helped my projects as it does not exactly replace DocumentDB or Cosmos DB or MongoDB, but I see it as a compelling option for PostgreSQL-centric organizations that want DocumentDB capabilities without introducing any new database platforms. That is what I see because it is in an initial phase and still has a lot of offerings we need to replace to use in our product.

When considering how Microsoft Azure DocumentDB can be improved, I see that there are definitely a couple of things we find out and feel could actually improve Microsoft Azure DocumentDB a little more. Some of the limitations I see that it does not have yet, when compared to MongoDB's ecosystem, is that it is still in an evolving state. Its identity is focused around MongoDB compatibility rather than being an Azure flagship, globally distributed NoSQL workforce, and another point would be that it should validate large scale and in-scale for performance characteristics for the specific workload rather than assuming parity with Cosmos DB NoSQL. Lastly, its actual architecture potentials are still emerging, so we cannot actually judge with already established architectures on MongoDB and others.

Regarding the needed improvements around Microsoft Azure DocumentDB's architecture or performance, it is still evolving, and at this point in time, it would be very early to give this feedback. I want to see it after some adaptations.

The improvements needed for Microsoft Azure DocumentDB that I have not mentioned yet include my observations that the maturity needs improvement, as it currently has a small community. When it grows to larger communities, real-time testing could happen, which may lead to advancements in real-time use cases and adaptations in Microsoft Azure DocumentDB, which would be great. Large-scale global deployment may require re-engineering for that reason. The enterprise support ecosystem still seems to be in acceleration and evolving situations. These are the couple of things I feel should be addressed for Microsoft Azure DocumentDB to have good support.

I have been using Microsoft Azure DocumentDB since it started; initially, from 2015, I worked over there when it was initially introduced as a DocumentDB. Then it merged with Cosmos DB, and they came up with the Azure DocumentDB flavors again, which they built up with PostgreSQL SQL. It is more compatible with MongoDB. Its biggest strengths are openness, portability, and modernization on MongoDB workload. Its biggest challenges are providing the same long-term ecosystem maturity and hyper-scale implementations as MongoDB Atlas and Azure Cosmos DB for NoSQL. It is built on top of PostgreSQL, which would be a great advantage of Microsoft Azure DocumentDB.

Microsoft Azure DocumentDB is not yet stable, as there are a couple of issues we are reporting to Microsoft to work on.

The scalability of Microsoft Azure DocumentDB is dependent upon our deployment architectures, and that is not an issue actually; it depends on our infrastructure.

Customer support for Microsoft Azure DocumentDB is pretty good; that is what I want to see regarding Microsoft Azure SLA and other aspects.

I would rate the customer support of Microsoft Azure DocumentDB a nine out of ten, just deducting one point because sometimes the best engineer is not able to resolve an issue immediately.

I was using Cosmos DB and MongoDB applications earlier, and I switched as we have started evolving into Microsoft Azure DocumentDB, which is quite good and developed on top of the same framework, making it a good fit to work on.

I have seen a return on investment with Microsoft Azure DocumentDB, as I compare it to other offerings, where we see literally faster developments. Even quite good local environmental support and microservices-based capabilities are available, which have increased rapid development, meaning I think it is time-saving and money-saving. These are all types we can put up.

My experience with pricing, setup cost, and licensing for Microsoft Azure DocumentDB is that it is comparatively less than what it is for other open source offerings, and I do not think there is a major cost impact here. I think it is okay to proceed with that and start digging into it; at this point in time, it seems good.

Before choosing Microsoft Azure DocumentDB, I evaluated many options in the market; however, compared to reliability, data security, microservices-based architecture capabilities supported, and AI adaptations, I found that Microsoft Azure DocumentDB has all these aspects. Therefore, we considered integrating it into our architectures.

I would rate Microsoft Azure DocumentDB a seven out of ten. I chose seven out of ten due to some improvement areas that I already mentioned. If those worked out, then probably I could give a better rating.

I have not explored Microsoft Azure DocumentDB's AI capabilities and adaptations much yet, but I have seen that it has the capability of vector support, and I am keen to look into that capability and exploring that proof of concept. It is very early for me to give feedback over there.

I have not yet tested the accuracy and reliability of output with Microsoft Azure DocumentDB.

My advice for others looking into using Microsoft Azure DocumentDB is that it is Microsoft's modern MongoDB capability platform built on PostgreSQL, with its biggest strengths being openness, portability, and modernization on MongoDB workloads. Its biggest challenge is proving the same long-term ecosystem maturity and hyper-scale adaptations as MongoDB Atlas and Cosmos DB for NoSQL, so we have to utilize this capability to benefit. My overall rating for Microsoft Azure DocumentDB is seven out of ten.

My main use case for Microsoft Azure DocumentDB is storing unstructured data. A specific example of the kind of unstructured data I store is from my previous company, where we measured occupancy by tracking how many meeting rooms, people, and workplaces were used in a specific building. Sometimes we shifted from one type of sensor to another, and we did not know in advance which data we could use. In that case, it was helpful to store it in Microsoft Azure DocumentDB.

That is the main reason for my use case. I also use it with electronic exams, which feature open questions rather than multiple choice. Sometimes people provide answers that we think are incorrect, but we want to analyze them later. Microsoft Azure DocumentDB is ideal for finding every answer that was ever given.

Microsoft Azure DocumentDB has positively impacted my organization because it is not a black box. You can see what is happening, the resources, and all the details, making it easy to implement from many platforms, APIs, and languages.

The ability to see all the details and resources from Microsoft Azure DocumentDB has made a difference for my team and projects. We had used some other similar products, for example, AWS and S3 containers where we dumped data. When something went wrong or performance was not sufficient, it was difficult to see what the root cause was. With Azure DevOps and especially with Microsoft Azure DocumentDB, it is easy to zoom in deeper if needed. That is a really big benefit.

The best features Microsoft Azure DocumentDB offers are numerous. First of all, it is not expensive, which is always beneficial. Even for smaller companies, I can introduce this product, and it is pretty fast. In the Azure DevOps environment, it is easy to maintain, so there are several benefits.

The most valuable feature of Microsoft Azure DocumentDB in my daily work is the speed. That is the biggest benefit, but it is also easy to maintain. It is helpful when I can introduce it in other projects, even for smaller companies, not multinationals or big banks. For those organizations, the price is also a benefit, but mainly it is about the speed and how to maintain everything.

Regarding Microsoft Azure DocumentDB's governance and security, I think they are always really good. You can see that everything is built around security. Even the tiniest piece is secure, and then a layer is built on top of it with additional security, so security is everywhere.

Regarding Microsoft Azure DocumentDB's accuracy and reliability of output, I find them really good so far. I have nothing to complain about.

At this moment, I do not know any improvements needed for Microsoft Azure DocumentDB. Maybe in the future, I would like to see relationships between one document set and another document set. Inside the set, container, or whatever it is called, it is easy to find the data, but to see what the relations are from a bird's perspective, I do not know if it is possible now. I never tried it, but I can imagine that could be a really big benefit.

That is the main thing on my mind regarding needed improvements for Microsoft Azure DocumentDB. I do not even know for sure if that is already possible.

I would rate Microsoft Azure DocumentDB overall as a nine out of ten.

I chose nine because I do not know all the details about Microsoft Azure DocumentDB, but as far as I can see, there are no downsides. I could also give it a ten, but I do not know every detail well, and that is why I think it must be at least a nine, which is part of my personality. A nine for me is a ten from someone else.

I do not know which cloud provider is used for our private cloud deployment. I did not set it up myself, so I see it when I am at work but cannot provide those details.

The advice I would give to others looking into using Microsoft Azure DocumentDB is to explain my positive experience. You can use it in more cases than we are using it. We are using it for unstructured data or as an entry point for a RAG, and you can do so many things with it, so I can recommend it and provide examples of how we are using it. My overall rating for Microsoft Azure DocumentDB is nine out of ten.

Our main use case for Microsoft Azure DocumentDB is using it as a NoSQL database for an application that needs to store and retrieve semi-structured data, such as user profiles, application configurations, and frequently changing application records. The application interacts directly with Microsoft Azure DocumentDB for read and write operations, while the managed service provides the scalability and availability needed as the application's data and usage grow.

That setup has worked well for our team because it provides a flexible way to manage semi-structured and rapidly changing application data without requiring us to manage the underlying database infrastructure. It has also simplified scaling and routine database operations as application usage grows. Overall, the main benefit for us is the combination of a NoSQL data model, managed operations, and the ability to integrate with Azure services.

Microsoft Azure DocumentDB has positively impacted our organization because its managed service model reduces routine database administration, while scalability and availability help support changing application demands. It has also allowed our development team to work with data models that can evolve more easily, reducing the operational effort involved when application requirements change.

The best features of Microsoft Azure DocumentDB are its flexible document-based data model, scalability, managed database operations, and the integration with Azure services. The schema flexibility is particularly useful for applications where data structures can change. We also value the reduction in effort required for routine database administration.

Another useful aspect of the features is the flexibility to work with semi-structured data without requiring a rigid relational schema for every change. The combination of flexible data modeling, scalability, availability, and Azure ecosystem integration makes Microsoft Azure DocumentDB suitable for modern application workloads. The managed nature of the service also helps reduce routine database administration operations.

Microsoft Azure DocumentDB could be improved with more straightforward cost and performance optimization guidance, especially for understanding resource usage and selecting the right configuration for different workloads. More detailed built-in performance diagnostics, proactive recommendations, and clearer monitoring of query performance would also be useful. Additionally, better documentation for complex production scenarios and stronger integrations with third-party monitoring and management tools could make day-to-day operations easier.

Performance and cost management could be made more transparent and easier to understand, with more proactive alerts and recommendations for identifying inefficient queries, resource usage, capacity requirements, and potential cost optimization opportunities. Clearer troubleshooting guidance for complex production issues and more practical examples in the documentation would also make the overall experience easier for teams managing Microsoft Azure DocumentDB at scale.

I have been using Microsoft Azure DocumentDB for three years.

We have immeasurable benefits mainly in reduced operational effort and the time saved on routine database administration. I estimate we have saved the team around 15 hours per month by reducing manual effort related to database maintenance, scaling, and infrastructure management. The flexible document model has also reduced development effort when application data structure changes, since we do not need to make extensive schema changes for every modification. The main benefit has been reduced operational overhead for changing application requirements.

I would rate Microsoft Azure DocumentDB a 10 out of 10 because it provides the flexibility we need for semi-structured application data while offering scalability, availability, and managed database operations. The managed service reduces routine administration and the integration with the Azure ecosystem makes it easier to operate as part of our overall cloud environment. My overall rating for this product is 10 out of 10.

My main use case for Microsoft Azure DocumentDB is when we modernize applications that use MongoDB on-premise and move them to Azure using DocumentDB.

By doing this migration to Microsoft Azure DocumentDB, we avoid having to maintain servers, we have greater resilience in terms of the database and higher performance, where we can scale infinitely and have very good cost control and management.

Microsoft Azure DocumentDB allows us to scale quickly during peak load times, without having to provision the servers to the maximum peak as was done previously with on-premise servers.

The migration from MongoDB has not been complicated; it just needs a small adjustment.

I would highlight the very high compatibility with MongoDB, the high performance, the flexibility it offers, and the security as the best features offered by Microsoft Azure DocumentDB.

In fact, the software has been kept exactly as it was, almost 100% with no specific functionality that I found especially useful or any process that was simplified thanks to the compatibility with MongoDB.

Performing backups and restores with Microsoft Azure DocumentDB is very convenient.

Microsoft Azure DocumentDB has positively impacted my organization by allowing us to reduce database costs by 30%. I achieved that saving due to the scalability possibilities, which allow us not to have the maximum peak provisioned, but to always have it at the minimum necessary computing needs, scaling according to the load that the application is receiving.

I think perhaps in some aspects the documentation could be better, as well as the integration with Azure Backup to improve Microsoft Azure DocumentDB.

I have been using Microsoft Azure DocumentDB for about four years.

I consider Microsoft Azure DocumentDB a stable solution.

Microsoft Azure DocumentDB has responded well to my organization's growth needs in terms of scalability.

My experience with customer support for Microsoft Azure DocumentDB has been fine; I have not had any relevant experience with support.

I was previously using MongoDB before Microsoft Azure DocumentDB, and we decided to change because we wanted to migrate to the cloud that application which was on-premise.

We considered MongoDB Atlas before choosing Microsoft Azure DocumentDB.

I would advise others considering using Microsoft Azure DocumentDB that they consider it a good alternative to MongoDB.

I have seen a return on investment with Microsoft Azure DocumentDB, specifically a 30% saving in database costs.

My experience with the pricing, implementation cost, and licensing of Microsoft Azure DocumentDB is good overall.

I would rate this product an 8 out of 10.