Microsoft Azure Cosmos DB Reviews

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

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

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

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

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

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

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

The topic of RU consumption needs better documentation. 

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

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

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

Neutral

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

I have been using this solution for six years.

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

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

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

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

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

Positive

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

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

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

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

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

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

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

Our primary use case for Azure Cosmos DB is storing information for our large accounting application, which integrates several sites on SharePoint Online. We use event programming to store all calls in Cosmos DB, so we can redo them and have them persist in the database.

Cosmos DB has helped our organization handle large amounts of data. For example, we had a customer who collected data from 100,000 sites, and we increased that to a million without significantly increasing search query time. We can now search in nearly real-time, which has been crucial, especially with AI workloads.

Azure Cosmos DB's resiliency is valuable. It is available in every Azure region, allowing quick information storage and retrieval. We can partition it to improve indexing, enabling us to retrieve information and recreate website content quickly. 

It's easy to use for our use case because we use it to store and retrieve information, but it will be more complex if you are configuring a Redis cache or something similar. 

Cosmos DB also integrates well with Azure app services and functions, allowing us to scale by efficiently storing calls. Its ability to scale workloads is impressive, and features like partitioning and Azure replication enhance its scalability. Its interoperability with solutions is better than that of other NoSQL databases we assessed. It's native to Azure and integrates with the networks and security.

Cosmos DB should continue evolving in AI features. We expect Cosmos DB to lead on that. There is potential for improved security features, which is important for data storage, especially for Dell Technologies. We must ensure data security remains the top priority.

I have been using Cosmos DB for over eight years, starting from its preview release.

There have been no notable issues with the stability of Cosmos DB. Any problems encountered were not directly related to Cosmos DB but perhaps coding errors or usage methods.

Cosmos DB scales workloads impressively through features such as partitioning and Azure replication. Its design as a NoSQL database has helped us transition from traditional SQL, impacting costs positively.

We previously used MongoDB, but Cosmos DB's integration within Azure provided better network and security options, making it a preferred choice. I've worked on Microsoft technologies since the beginning, and I love how Microsoft solutions are integrated. Everything works together securely, and moving from one technology to another is simple.

The initial setup was easy. The transition from MongoDB was seamless as Cosmos DB has improved upon existing NoSQL structures without reinventing them.

Cosmos DB has decreased our organization's total cost of ownership, particularly with decreasing overhead costs due to its scalable features.

We prioritized fine-tuning operations to optimize costs, and Cosmos DB’s pricing model allows room for improvement. We are assessing its use in other areas to potentially eliminate third-party solutions.

I rate Microsoft Azure Cosmos DB nine out of 10. To avoid migration challenges, data storage methods in Cosmos DB should be carefully considered.

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

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

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

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

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

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

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

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

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

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

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

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

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

Neutral

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

It took a couple of hours.

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

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

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

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

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

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

The main use cases involve creating some kind of dashboards in near real-time. Our use cases focus on manufacturing, where we used Microsoft Azure Cosmos DB to maintain data for the very intensive manufacturing processes. In the end, we performed data analysis on the operational processes in manufacturing.

Big data, along with data analysis, is one of the valuable features. We are able to have insights into how to make improvements in the processes for operational people.

Microsoft Azure Cosmos DB is a Microsoft solution specifically, but we can develop with different developer kits for different databases.

We had some performance issues with a data segregation query. We worked closely with Microsoft to solve the problem of performance where, for example, one query had a delay of almost two or three minutes for this one use case. Microsoft tried to improve the product, but in the end, the solution was to change to MongoDB. MongoDB had better performance. We reached the performance required using MongoDB instead of Microsoft Azure Cosmos DB.

I used it for one year or less than one year.

Microsoft Azure Cosmos DB has good performance and latency. We only faced performance issues with the data segregation query.

I would rate Microsoft Azure Cosmos DB a nine out of ten for the capability to scale workloads. 

On a scale of one to ten, I would rate customer service a seven. For example, when I created a ticket with them, they gave us feedback very often, even each week. This went on for four or five months, but they did not solve the problem. They only gave feedback, and in the end, it did not resolve the problem.

Neutral

We changed from using Microsoft Azure Cosmos DB to MongoDB because Microsoft Azure Cosmos DB did not give us the correct performance for certain data segregation, so we replaced it with MongoDB.

People who helped us implement MongoDB were more specialized or had more expertise than Microsoft people.

The setup of Microsoft Azure Cosmos DB was very easy. It took us a few weeks.

We received help from Microsoft directly. They helped us to get started with it. 

Our use case was a failure with Microsoft Azure Cosmos DB, and we do not have any other opportunity to use Microsoft Azure Cosmos DB.

Its pricing is not bad. It is good. 

We have a contract with Microsoft to use their technology. In my opinion, Microsoft Azure Cosmos DB is a good option for the total cost of ownership.

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

We have a very large team of developers who develop a solution for our customers. In the part where they need some infrastructure on Microsoft Azure, we deploy entire environments of different types such as development, testing, production, and pre-production to Microsoft Azure and configure it, monitoring that infrastructure, one of them being Microsoft Azure Cosmos DB. Later, we hand over those resources to our development team and they can start to use it.

For example, we have one application which is for one of our post offices where they host their main application for tracking packages, for sending packages, and for everything they provide to their customers. The main database for this solution is Microsoft Azure Cosmos DB.

In production, we definitely are using automatic scaling because of the workload, since some days there is a workload which is very high and some days it is not so high. For non-production environments, it's a minimal setup with minimal SKU on Microsoft Azure.

Availability is for sure better with Microsoft Azure Cosmos DB, but it's also the biggest cost for that setup. We are a very small country from Slovenia, so our customers don't require so much high availability for their applications. This leads us to set up resources only in one region for most of them, while for the most critical workloads, such as those from banks, we use the multi-region setup and auto-scaling.

About the performance, I monitor everything that's going on, what is possible on the resource level in Microsoft Azure, and we also do the FinOps solutions for our customers, utilizing different metrics to optimize resources and the entire setup.

Overall, I think Microsoft Azure Cosmos DB works fine. I don't remember any case where our developers or our clients have been disappointed with it.

The benefits we and our clients have seen from using Microsoft Azure Cosmos DB are similar to those most platform-as-a-service solutions provide, where you don't need to take care of the underlying infrastructure, which is the main reason.

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

I'm not in the position to decide if clients will use Microsoft Azure Cosmos DB or any other database. However, I notice that there is more and more Microsoft Azure Cosmos DB setups in different applications.

The only problem I face is more with infrastructure as code templates that don't cover everything that can be set up or configured on the portal, requiring some manual work which is additional work for us. Some resource providers don't provide certain configurations, which I think is on Microsoft's side because they need to change Azure Resource Manager and the version of templates. Other aspects involve different providers for those templates including Azure Verified Modules with pre-configured templates on the community and the team working on them.

I have been using or working with Microsoft Azure Cosmos DB for the last year and a half.

I usually use Microsoft support, and I would evaluate them around one to ten as very bad for the first level. They have some instructions and procedures they follow without listening to customers, primarily seeking to get their checkboxes rather than fully understanding the customer's needs. However, upon reaching the product group or a higher level, the support was great. I currently have one critical ticket open for another solution and it has been handled excellently.

Based on my experience with Microsoft support, I would rate them around eight on a scale of one to ten. To make it a ten for me, they need to listen to customers more instead of just going through their automatic process.

Positive

Our clients see metrics in terms of ROI after some time, but not at the beginning. They usually observe cost saving and time saving post-optimization when we find the right SKUs because most of the time, they don't know what they need regarding the required SKU or size of some resources. After time, for sure, they see ROI.

On average, the kind of savings we see ranges from 15 to 25 percent. The savings I refer to are in money saving.

In my opinion, the main difference between Microsoft Azure Cosmos DB and other types of databases is hard to say. It's mostly how developers see everything, as it depends more on the development side—what they want to use and what features they need, such as relational databases or document databases. This leads us to select the right database based on those inputs. The selection is based on the use case.

We are implementing other Microsoft Azure solutions like Azure SQL and Postgres. We focus only on Microsoft Azure and do not work with other vendors like AWS. I gave this review a rating of ten out of ten.

Our current use cases for Microsoft Azure Cosmos DB include IoT-based applications such as home automation, conferencing, and industrial automation, utilizing devices like microphones and speakers.

The best feature about Microsoft Azure Cosmos DB is its interface, which is awesome for accessing data. Additionally, its indexing capabilities and responsiveness allow us to get information with a very excellent SLA, making it suitable for our IoT-based applications where we can update the statuses of devices in real time, which is an outstanding feature. Microsoft Azure Cosmos DB has helped us improve the search result quality in meaningful ways.

The user interface of Microsoft Azure Cosmos DB is the best part of the entire Microsoft ecosystem; I find it to be the best user interface you can ever hope for, especially when compared to AWS and GCP, which do not measure up as well.

The only area Microsoft Azure Cosmos DB can improve on is its documentation; while it is solid and very useful, enhancements in the indexing documentation would help users save costs and make it more cost-effective. This is often a missing piece from Microsoft's side regarding how we can utilize it in the most cost-effective manner. The documentation for the FSx tab was not very good, and we faced a lot of struggles with it a few years back. I believe that has improved, however, Microsoft should really focus on these features since data analytics is very important today.

I have been using Microsoft Azure Cosmos DB for the past seven-plus years.

I have never encountered any issues with lagging, crashing, or downtime in Microsoft Azure Cosmos DB.

Recently, we have started using AI in Microsoft Azure Cosmos DB, particularly in AI-based search and related capabilities, which is pretty good.

I have contacted their technical support, and I find them to be pretty good. The speed of Microsoft Azure Cosmos DB support sometimes depends on the tier of support you have. I have noticed that even if you have the highest tier support, the attention given may vary based on the business relationship; I experienced this in organizations where the level of investment in Microsoft services differed, impacting the quality of support.

We have used AWS DynamoDB as an alternative to Microsoft Azure Cosmos DB, which has similar features, however, I found AWS to be much more user-friendly. AWS provides a slightly better SLA, but Microsoft Azure Cosmos DB is not far behind in that regard.

The initial deployment of Microsoft Azure Cosmos DB was pretty easy and went smoothly for me.

Deploying Microsoft Azure Cosmos DB requires only a couple of people, which is good enough. That's how we started, though now we have a very large team. We considered deploying Microsoft Azure Cosmos DB across multiple regions. However, we ultimately decided to keep it in a single region.

There's a feature called AWS FSx tab that allows data in Microsoft Azure Cosmos DB to be utilized for data analytics purposes. However, querying data on Microsoft Azure Cosmos DB incurs costs, especially after crossing their tiers.

The pricing for Microsoft Azure Cosmos DB is more or less the same as its competitors, making it challenging to declare a clear best option.

I haven't used the built-in vector database feature of Microsoft Azure Cosmos DB.

In my previous company, we were partners with Microsoft about six or seven years back. Currently, we are just customers, and the same holds true for my current company as well. 

I would rate Microsoft Azure Cosmos DB an eight out of ten for everything.

We use it for our internal operations, including order history and other things related to e-commerce.

We do not use the built-in vector database capabilities since they are driven by another team in our organization. We just access through the API.

We find Microsoft Azure Cosmos DB easy to use. We are provided APIs for each and every write or edit access, even for read operations. We don't directly query the database. API-based access makes it easy.

Previously, we used to have maintenance or server issues. We don't have those issues anymore.

The best feature of Microsoft Azure Cosmos DB is API access, which makes it very easy to interact with the database without needing to write queries. It's also fast. As it's Microsoft-provisioned, the cloud is very accessible and reliable as well.

There is room for improvement in Microsoft's maintenance aspect. For example, we had a major incident at the end of December where the entire South Central region was down for our application, causing many problems due to a lack of access to the database. It led to missing data in some systems. They can implement a better backup system or alert system on Microsoft's end. We do receive notices for regular maintenance or updates, but sudden issues create significant problems.

I've been using Microsoft Azure Cosmos DB for more than one year.

In the past year, I have only been using Microsoft Azure Cosmos DB for a year, and previously we encountered Microsoft issues such as maintenance or server problems, but these days we are not observing that as much.

For stability and impressions of latency and availability, I would rate it an eight or nine; we have not seen significant issues recently.

I rate scalability as pretty good. Because it's in the cloud, scaling is easy.

We are a very large organization. It is hard to know how many teams use Microsoft Azure Cosmos DB or still rely on the older systems. I am in India, and our team uses Microsoft Azure Cosmos DB, and I believe teams in the U.S. use it as well.

I would rate the technical support a nine out of ten.

Positive

Before using Microsoft Azure Cosmos DB, we had a different database system in place. The main factor for switching was cost-related. It was a leadership decision, and as a fresher, I wasn't involved in these discussions.

We were not part of the deployment. We were involved in migration activities, but I'm not very sure about the deployment experience. We aren't seeing any major issues now.

Maintenance of Microsoft Azure Cosmos DB is ongoing. There is a Cosmos DB team in our organization conducting maintenance, though not very frequently.

I'm not aware of the exact costs. We received one report a long time ago regarding savings after we started using Microsoft Azure Cosmos DB, but I don't remember the details. It seems to have helped significantly. We were using a different database system previously, and one of the reasons for acquiring Microsoft Azure Cosmos DB was cost.

I definitely recommend Microsoft Azure Cosmos DB, although I'm still learning. It's been just two years, but I've taken courses on Microsoft Azure. I recognize the advantages in scalability, availability, and cost factors, with maintenance issues being minimal as well. 

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