Microsoft Azure Cosmos DB Reviews

Our company uses the solution to develop a certain sort of products for our internal companies. We have some child or franchise companies and are developing software for them. 

We use the solution where transactions display to provide views or reports for the console. We also use the solution for an online learning application or portal. 

We have 20,000 to 30,000 users across multiple products, franchise companies, and customers at the backend. Centralized data is global and accessed from all over the world including India, the US, South America, and Asia. 

We have several new projects with the same backend, so our user volume will definitely increase day by day. 

The solution's read capacity and write access functions are very fast so users don't have to wait when fetching or displaying data on a screen. The main feature of an application is how it behaves toward the user. Users get uncomfortable when having to wait a long time. The solution's high-value data processing helps application performance data. 

The solution easily integrates with the Microsoft cloud and other Microsoft products like Azure Active Directory. We use cloud storage for databases so this integration is very beneficial. 

The solution cannot join two databases like Oracle or SQL Server. Joins have to be done programmatically through our sysHUB. We use .NET code so need the middleware to join databases. 

There are certain restrictions for inner classes or employee roles. 

Data retrieval is slightly more difficult than in SQL Server or other SQL databases. 

Documentation needs some improvement to help end users. Documentation for joining includes some generic or peculiar cases but needs to be more comprehensive. It should lay out how to join databases and what procedures to use. 

I have been using the solution for more than three years. 

The solution is stable. We haven't received news of any issues with platforms.

Stability is rated a nine out of ten. 

There are a lot of things we still have to fight out such as joining databases. Most probably for the high-transactional use cases, the solution cannot be used at all. 

Currently, scalability is a seven out of ten. 

Our infrastructure team handles all communication with support and reports that they are good. We have a premium account with Microsoft so support always helps us. 

From 2009 to 2018, we used Oracle at our company. We switched to the solution for various projects due to the needs of users. We needed a product that met our business and users' requirements with the lowest cost possible.

The setup is very easy so I rate it a nine out of ten. 

In the initial days in 2018, setup was uncertain and coding was needed from our side. Now, we use the library to access the database or read and write. Things become so much easier over time. 

Two members of our core infrastructure team handle all implementations. 

On average, deployments take three to five hours. We have to deploy the DevOps side and the data backup. If we consider all things, deployments hardly take a full day. 

The solution doesn't require any ongoing maintenance. 

Obviously, we want to be on the profit side as a business or we can't grow. Money and usability are the most important things for us. 

The solution has already realized some ROI. The pay-as-you-go usage methodology helps us because it saves money. 

At this point, I rate ROI a six out of ten. 

Pricing is one of the solution's main features because it is based on usage, scales automatically, and is not too costly. As usage scales up or down, the price moves accordingly. 

For example, we might have 30,000 users and the requirement is high so the solution automatically scales up. If the requirement lowers because the application isn't being used all the time, then the usage automatically grades down and so do our costs. 

Technical support is included as a free service.

I rate pricing a seven out of ten.

We still use Oracle for some projects but it is costly to acquire. 

We are using SQL Server for an ongoing project. 

The solution is less expensive than Oracle, especially with all of our DLLs. It is easier to work on from a developer's perspective and we realize a good cost savings. 

We choose the best database based on a customer's budget and need. 

Everyone can use the solution where the database hits or the transactional data is placed. 

The solution is not a good fit for companies in the banking industry who have a high volume of transactions every second. The solution always needs a proper SQL database like Oracle.

Companies with non-transactional applications must use the solution because it helps users and achieves a lot of success in terms database costs.

I rate the solution an eight out of ten. 

At the end of the day, Cosmos DB is a database. It is a wrapper over different APIs.

We use Cosmos DB both internally and with our customers. Our internal use is quite extensive. The usage with our customers depends on whether it is an approved technology within their ecosystem.

Because Cosmos DB uses multiple APIs, it is the go-to database for us internally.

Cosmos DB makes life easier because if we want to use Mongo-type data, or Cassandra-type data, or maybe even just a simple cable storage-type data, then graph, there are multiple ways to do this. With Cosmos DB, we can put together a holistic solution when it comes to Azure security policies, Azure Monitor, and access control.

By design, Microsoft Azure Cosmos DB provides multiple APIs. You have to decide where to write to. Will you write to Excel, Word, PPT, or OneNote? You have to do the homework properly. If there is no tool, then there will be no provision, then there is no database.

I would like to see Cosmos DB introduce a feature that would convert machine language to human-readable queries. For example, if we want to generate a simple diagram that shows the relationship between devices and how frequently have they failed at various locations, we have to consider that the IoT data that is put into Cosmos DB, called byte codes, is not readable to humans. This is a machine language type of data. So when we push that type of data it looks like gibberish, because it is not meant for us, meaning we can't write a normal query. We have been asking for years for them to work with the IoT partner to provide a feature to convert the machine language to readable human queries.

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

The solution is stable. I would give it a five out of five for reliability.

The architectural decisions will tell you how the scaling will happen. 

Scalability is based on the requirements that are set. Configuration decisions can be implemented pretty fast, so the solution scales well. We are predominantly in the US and India, so it is easy to decide which geographies we need to have and which data we need to synchronize. 

For some of our customers, there are data residency rules like the UAE for example, where patient data must stay within the UAE, making it only one geography. When this is the case, we go for multiple replicas. 

Internally, we have more than 150 developers who use Cosmos DB. Overall, the scalability of the solution is a five out of five.

Product support is pretty good. They have a very good roadmap and the team provides regular patches and regular service updates, and they have a very good release plan.

Microsoft's technical support is good, I rate it a five out of five.

Positive

The initial setup depends on the type of setup you require. You have to design it properly. The architects need to do some homework to ensure the purpose and the requirements are clear. There are many design decisions that must be made first. Once those decisions are made, the initial setup is quite easy. 

Deployment of the solution was completed within a week.

Overall, I would give the solution a four out of five for ease of setup.

The deployment of Cosmos DB was completed internally, we managed it ourselves.

The cost is intricate, the calculator is complex because the cloud is all about counting every penny. It may look like small numbers, $0.001 per GB per day, but when we are talking of terabytes of data per day and the numbers will stack up. One month, we had over 500 terabytes. That's why you need a database expert to design it carefully and spend ample time number crunching. If done properly, the ROI will be good.

I would rate Cosmos DB a four out of five in terms of ROI.

Cosmos DB is expensive compared to any virtual machine based on conventional RDBMS like MySQL or PostgreSQL. The reason it is expensive is that it is scalable, reliable and there is no latency. So while Cosmos DB is considered expensive, what a lot of people miss is that the cost includes reliability, scalability, and responsiveness.

Cost also depends on the number of databases, number of replica locations, synchronization, number of queries per minute, and storage. Every client will have a different usage pattern. 

Overall, I would rate Cosmos DB a three out of five in terms of affordability. It is easy to over-provision, and it is easy to under-provision the solution.

Prior to choosing Microsoft Azure Cosmos DB, we did try other tools extensively. Because we have servers, we tried MongoDB, SQL Server, MySQL, and PostgreSQL. We settled on Cosmos DB internally because we didn't want to go for machines and trojaning. We wanted to adopt a platform as a service.  

Cost also ended up being a driving factor.

Overall, I would rate Microsoft Azure Cosmos DB a 10 out of 10.

Cosmos DB has multiple use cases. For instance, we recently developed a custom application for a customer in India. We used Cosmos DB to store data fetched from the initial front end to reduce access times to the application, which is significant for improving user experience. 

For example, when creating a virtual machine through our custom portal, it is essential to check whether a VM with the same name exists in the same or a different subscription. 

Additionally, we needed to enforce naming conventions and limitations on the number of VMs that can be created within the same network. These conditional parameters were managed using Cosmos DB, allowing the initial provisioning process to validate data and configurations instantly. 

This enables us to inform the user right away if there is a duplication or if the creation adheres to predefined rules, offering suggestions based on the UI. This demonstrates the real-time application and benefits of Cosmos DB.

We use Cosmos DB for its key-value storage capabilities. For structured data, we always use SQL Database.

The low-latency data access provided by Cosmos DB improved application performance.

Application performance improvement depends on what kind of optimization you're looking for. Do you want to improve latency or efficiency? Performance tuning depends on that specific goal.

Here's an example: A customer with an application running in an internal system noticed their outbound data flow and charges increasing every month. They were exporting a lot of data for users in Excel format, which was heavy.

I suggested they export the data in CSV format instead. It's lightweight and users can still open it in Excel. This optimizes data usage and costs without compromising user experience.

Cosmos DB now supports unstructured data. It's a key-value store, so we can send data without worrying about strict structure, data types, and so on. Since it's unstructured, it's lighter than a structured database.

We use Cosmos DB for its key-value storage capabilities, while SQL Database is used for structured data.

There is room for improvement in terms of stability. 

I have been using it for a year. 

In my experience, Cosmos DB is definitely stable. But, for any service or application, I wouldn't give it a perfect score. There's always room for improvement. A perfect score would mean no room for improvement. So, I always consider some buffer for improvement.

I would rate the stability a seven out of ten.

Since it's a managed service, Azure backend handles scalability. From a user's perspective, we don't need to worry about scalability.

Right now I'm dedicated to customers of one of India's largest certificate authorities, Reliance Jio. They have a lot of customers and two dedicated Azure data centers in India. I focus on those two data centers, and I see at least 10 to 15 customers heavily using Cosmos DB there.

From the user's perspective, it's a managed database service, so all scalability is managed in the backend. Users shouldn't worry about scalability itself, but they might need to consider if paid region support is needed or if other regions are available. Otherwise, scalability shouldn't be a concern for them.

But if you're configuring Cosmos DB in a non-Azure solution, you'd have to manage scalability yourself. In that case, you'd have to be more conscious about it.

We have dedicated technical support in India for each Azure service, including Cosmos DB. Since I provide the framework, design, and initial implementation, I'm involved in most calls to ensure everything is deployed as designed. 

But for any issues or troubleshooting, there's dedicated support that gets involved and fixes them. I also stay engaged with the product team.

The product team is very proactive.

Positive

I predominantly work on Microsoft SQL Database, among others. Sometimes, we use Cosmos DB for specific adaptations or APIs within Azure.

We've also assisted some customers in migrating from MongoDB to Cosmos DB.

If you're familiar with it, it's not complex at all. But for someone new, it can be a little tricky.

Cosmos DB itself is a cloud-based solution. However, I'm currently working primarily with a hybrid solution: Azure Stack HCI with software-defined networking for the environment.

We don't directly deploy Cosmos DB itself; it's a service within Azure. We use our DevOps pipeline to deploy the entire environment, which includes the application, database, environment (including the virtual network), and any connected service endpoints. 

Everything gets incorporated into the provisioning source or the DevOps pipeline and then deployed from there. It's a pretty streamlined process for us.

If the cost is affordable and you're looking for a managed service for unstructured data, I would definitely recommend using Cosmos DB from Azure. It also has seamless migration options from MongoDB, MySQL, and others. 

So, a managed service is the best way to go if the cost is affordable.

Overall, I would rate the solution a seven out of ten. 

Our current project primarily relies on the file system to handle incoming source tests. Within this setup, we capture both metadata and result data from these tests. We extract metadata information from these files and store it in Azure Cosmos DB and we have several software services in place to facilitate this process.

It is one of the simpler databases to work with in terms of code management, tracking, and debugging due to its straightforward data storage and retrieval mechanisms.

There is room for improvement in their customer support services.

In one of our recent projects, we stored metadata information and log data within Cosmos DB.

It offers good stability capabilities.

It offers impressive scalability, both in terms of throughput and storage. Its ability to scale dynamically allows us to align the database resources with the specific demands of our applications. Given its scalability and performance capabilities, we highly recommend it for use in large enterprises and organizations.

There were instances where their customer support services were slow. As previously mentioned, when it came to setting up Azure Cosmos DB, not everyone was proficient in cost considerations, and our team lacked extensive prior experience. Our main support was provided by Microsoft's documentation and we were able to successfully navigate these challenges. I would rate it eight out of ten.

Positive

The initial setup presented some challenges and required us to delve deeper into understanding the daily implications. Microsoft documentation proved to be a valuable resource in navigating this process.

The initial setup, planning, and configuration took approximately one to two weeks to complete. The timeline for implementing the solution varied based on the specific use case and the discussions held with the client. We conducted regular reviews, documented our progress, and established a static attack system. Due to some design-related confusion, the overall implementation process was extended to about one to two months. Still, Cosmos DB and related components were set up within one to two weeks.

Its pricing structure is quite flexible. It operates on a pay-as-you-go model, which means the cost is directly tied to the resources you consume and the throughput you require. Initially, our expenses were relatively low because we didn't store a significant amount of data, but as our storage needs increased over time, our expenses naturally grew in proportion to the resources and capacity we used.

Initially, we encountered some challenges in understanding it, as it wasn't as straightforward as managing an SQL Server database or setting up environments within Azure Data Factory and DevOps. This complexity is related to the fact that Cosmos DB offers a range of additional features and capabilities. Our initial difficulties could also be attributed to our team's limited prior experience with Cosmos DB. Considering these factors, I would rate our experience with it at an eight out of ten. Beyond these initial hurdles, we found it to be a valuable and capable solution.

We mostly use it for NoSQL use cases. We use it for web applications, mobile applications, and social applications in the financial sector.

It is deployed on-premises and on the cloud, and we are using its latest version but not the one in the public review.

Its wide support to the ecosystem is valuable. We can use this database with a lot of use cases, and that's one of the reasons why we prefer it. We have a lot of vendors, databases, and use cases, and wherever possible, we are trying to standardize databases. It is also secure.

At this stage, we would like more enterprise support. We use MongoDB a lot, and we're trying to get rid of MongoDB. So, I would like to see more features in the Cosmos DB API for MongoDB space.

I have been using this solution for almost two years.

It is stable. It has 99.999% availability, and it is backed by SLAs.

We have thousands of users.

We use the cloud version and the on-prem version. We have our on-prem database engineering team. For the cloud, we are okay with their support.

We use MongoDB a lot, and we're trying to get rid of MongoDB.

It is easy to install. I tried it in a testing environment, and it was easy. Database experts should be able to do it easily.

For the cloud, we don't pay for the license, but for the on-prem versions, we do pay.

I would rate Microsoft Azure Cosmos DB a nine out of 10.

I find SQL API suitable. I used it in my last project. Previously, I worked for a client called EPS, which has a product called BOS (brokerage operation support system). There I have used the SQL API.

I have used it in a product called BOS, and we achieved many things with Microsoft Azure Cosmos DB, which helped improve our products efficiently.

It helps in many ways in my current projects such as brokerage operation, which shifts multiple data in different regions. It helps significantly in storing and retrieving data from different countries for shipping details, shipping ID, and all data records in different countries.

Microsoft Azure Cosmos DB is a fully managed globally distributed NoSQL database. It is highly available with low latency and scalability. It supports multiple data models and APIs, making it flexible for different applications. Its features include multi-model support, global distribution, automatic scaling, and support for multiple APIs such as SQL API, MongoDB API, Gremlin, and Cassandra.

We can use Microsoft Azure Cosmos DB for storing and managing all types of data manipulations including inserting, fetching, and updating records. These operations can be performed efficiently.

The storage in Microsoft Azure Cosmos DB is globally distributed and highly efficient. Storing and retrieving data is much faster and more efficient.

It is cloud-friendly and easy to use. We can easily insert data and retrieve information from this cloud platform. The UI is better, faster, and efficient.

It supports various types of APIs and is a fully managed, globally distributed database that helps in different regions. Microsoft Azure Cosmos DB is a distributed and multi-model NoSQL database that supports SQL, MongoDB, and other platforms. Its scaling is managed using the request per unit, and it has auto-scaling based on business requirements.

The features include support for multiple NoSQL data models such as documents in JSON format, key-value store, graph database, wide column store, and MongoDB compatibility. In the document model, we can use the SQL API, while in the key-value store, we can use the table API. The Graph database is used for Gremlin.

It has a large capacity of up to 12 GB per physical partition per container. I have used up to three to four GB.

Its latency is high and impressive. The support is very high, with read-write latency at 10 ms per second.

It handles large-scale operations efficiently, such as tracking views, logs, or events. It has high write throughput and handles partition issues and storage growth effectively.

There are some disadvantages as it is costly compared to other NoSQL databases. It has a complex pricing model and has a strict partitioning strategy. There are limited SQL query capabilities in Microsoft Azure Cosmos DB.

It is more expensive than other server cloud service providers with its request units pricing model.

I have one year of working experience with Microsoft Azure Cosmos DB in my current organization.

The solution scales very well.

I'm not sure about technical support. I haven't worked with them. 

Before Microsoft Azure Cosmos DB, I used SSMS and MySQL server management. For cloud solutions, I have only used Microsoft Azure Cosmos DB.

Initially, we logged into the Azure portal and create a new Microsoft Azure Cosmos DB account. Then we chose an API such as SQL API or MongoDB. We set up account details, subscription, region, and enable geographical replication and multi-write regions. After that, we created a database and specify the name and provisional throughput. Then we created a container inside, providing the container ID, partition key, and index policy.

It took around 15 to 20 days for full-fledged training.

Initially, it took approximately three months to get comfortable for learning purposes. I encountered some difficulties while learning, however, through the project, I learned many things.

It's fully cloud-based, so there is no maintenance.

We have six developers for deployment and related tasks in Microsoft Azure Cosmos DB.

AWS is another choice available. I find Microsoft Azure Cosmos DB better suited for my needs.

Microsoft Azure Cosmos DB and AWS DynamoDB are basically the same, however, Microsoft Azure Cosmos DB supports multi-region support and can replicate and auto-replicate the data. It is highly manageable, which is why I chose Microsoft Azure Cosmos DB.

I definitely recommend Microsoft Azure Cosmos DB. It handles large amounts of data, is highly reliable, and operates in a very fast and efficient way. Users can deploy their applications in the cloud, and it supports various APIs. On a scale of one to ten, I rate Microsoft Azure Cosmos DB a nine out of ten.

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

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

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

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

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

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

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

The topic of RU consumption needs better documentation. 

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

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

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

Neutral

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

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

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

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

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

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

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

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

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

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

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

We are streaming some data from Azure Stream Analytics, which will be stored in Microsoft Azure Cosmos DB. Our application will be taken from Microsoft Azure Cosmos DB.

The solution's most valuable feature is its global distribution. We work globally and currently have Azure operating in fire regions. From a global distribution perspective, Microsoft Azure Cosmos DB is good and easy to handle. Since Microsoft handles the solution's main operation, we don't have many headaches regarding its operation.

The solution’s pricing could be improved.

I have been using Microsoft Azure Cosmos DB for more than 2 years.

Microsoft Azure Cosmos DB is a stable solution.

I got instant technical support from Microsoft during an outage issue.

Positive

We use Terraform scripts for the initial setup of the solution, which doesn't take much time.

We implemented the solution through an in-house team. We select which region to host Microsoft Azure Cosmos DB based on the resource group. We use Terraform scripts in the deployment process. We create a database and a document inside the database.

The solution is pretty good in terms of support, but we have some pricing issues with it. We are currently evaluating MongoDB and Apache Cassandra. Apart from the pricing, we didn't face any issues with the solution. We once faced an outage issue with Microsoft Azure Cosmos DB because some back-end updates from Microsoft changed the settings.

Microsoft Azure Cosmos DB is a cloud-based solution. Based on our experience, the solution is pretty good because we operate in multiple regions. There will be a lot of machines sending IoT data, dashboards, and alarm messages. Customers need to be updated simultaneously, which should not take much time. The solution's processing is pretty good.

I would recommend the solution to other users. The solution's usage is pretty good, but users should be careful about the IO threshold value, which is a little bit high.

Overall, I rate the solution eight and a half out of ten.