Microsoft Azure Cosmos DB Reviews

We have numerous healthcare AI use cases, including utilization management, documentation, letter generation, and voice call creation. These are both real-time and non-real-time use cases. My team is the platform team that enables the services. The ML teams are the practitioners who work on these products. 

The vector database has had a significant impact by making everything searchable, and the number of potential use cases exploded when GenAI was added. We've transformed many tasks into AI machine-learning problems. We have a ton of institutional expertise across the enterprise. It's crucial to be able to bring all of that into one place, ask questions, and get answers.

The most valuable feature of Microsoft Azure Cosmos DB is its real-time analytics capabilities, which allow for turnaround times in milliseconds. This is crucial for applications like fraud detection. 

Using and optimizing Cosmos DB is relatively straightforward. We talk regularly with the Microsoft team, and hands-on help is available when needed, so the experience was seamless. 

We have migrated to Cosmos' vector database search from Azure AI Search.  We don't face too many challenges with interoperability because everything is built on Azure, and we don't have any multi-cloud applications.

Azure AI services integrate and perform well with the vector database. Sometimes, we struggle to customize the RAG pipeline instead of using the embedded settings. Those are rare use cases, but they are useful for most use cases. 

The search capabilities work well once you have your data set up. It's more of a challenge in the knowledge-based integration than the modeling side. Our data is scattered. SharePoint, Confluence, and meeting minutes data are separate. We are working actively to make all the data flow. 

It would be beneficial if Cosmos supported batch and real-time use cases to make the system more seamless. Our biggest challenge migrating data is the fact that we're a multi-cloud organization with data stored in multiple platforms like AWS and Snowflake. It's all over the place, so we are using solutions like Fabric to migrate the data. If you want to bring the data from AWS, you must pay data egress costs. That's a pain point.

I have been using Microsoft Azure Cosmos DB for about two and a half years.

The latency numbers of Cosmos DB are satisfactory and align with expectations for clinical decision support engines.

While I have not personally tested it, the information I have suggests that Cosmos DB has robust scalability capabilities.

We have regular connections with the Microsoft team, which provides hands-on support and makes the use of Cosmos DB straightforward.

We previously used Azure AI Search, but we are transitioning to utilize built-in capabilities in Cosmos DB.

The platform team is responsible for enabling the services, while the ML teams use these products.

The pricing model aligns with our budget expectations, and we get a significant corporate discount from Microsoft because we're a partner.

I would rate Microsoft Azure Cosmos DB eight out of 10.

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

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

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

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

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

I have been using it for about a week now.

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

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

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

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

Neutral

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

We compared Cosmos DB with DocumentDB and other document databases.

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

We utilize the solution for big data, which is collected from IoT devices and streamed through a number of Azure services. The data is then landed in the Cosmos database for analysis later.

The solution is used because we get faster response times with large data sets than with SQL. It's essential for us because we have half a billion rows, and we need to return them quickly. 

Using it is easy. We are having trouble optimizing it. I'm not a technical person, so I couldn't explain why, but we're not getting the performance we were expecting. I'm sure it's probably an us problem instead of a product problem, but that's where we are.

From about half a billion rows, we're returning maybe 20,000 in two or three minutes. We don't know why, but we are working with Microsoft and a third party to figure that out. 

I have used it for about four years.

There are issues with latency between data arriving in Cosmos and showing up in a query. I'm told that's just the nature of the way Cosmos works. It can take up to five minutes to show up, but that's not a significant issue as we have workarounds in place.

I think the ability to scale workloads will depend on the outcome of tomorrow's meeting.

The customer service is lacking. We have a premier support agreement, but support is hit and miss. There are good engineers and not so good engineers. Premier Support has deteriorated compared to what it used to be, especially for small to medium-sized customers like ours.

Neutral

We used SQL previously, but Microsoft told us to use Cosmos DB because SQL wasn't performing.

The initial setup wasn't a long process. It took a couple of weeks. The whole thing was a proof of concept that eventually migrated into live use.

The solution was a new product, so we didn't have a cost of ownership before. The cost has not surprised us. It's not been an issue. If we were doing multi-master replication globally, the cost would increase significantly, but since we're not, it's manageable.

I rate Microsoft Azure Cosmos DB seven out of 10. If we can fix the problem we have, I could rate it a ten because there's nothing else I can point to for improvement if the performance meets our needs.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

It has not been a direct approach for me because all of my enterprise-level applications are deployed in MongoDB. At some point, we usually face issues where we need multi-directional and different contexts to connect with the database. Sometimes we use SQL and need to retrieve data from the database. If using a typical MongoDB, this is not possible. Microsoft Azure Cosmos DB has bidirectional support for cross-platform connections, so we don't need to recreate our entire database structure in our application. We can work with the MongoDB driver and interact with Microsoft Azure Cosmos DB. The applications under my portfolio currently rely on that, mostly indirectly. We created the models, deployed our data, migrated it, and are using it heavily in Microsoft Azure Cosmos DB. 

Recently, we are building an AI-powered application where we heavily rely on Microsoft Azure Cosmos DB to bring data from ServiceNow, SAP, Salesforce, Cisco, and other customers we have at our organization. Reading and inserting data into Microsoft Azure Cosmos DB is a very smooth process.

Its scalability is great. Microsoft Azure Cosmos DB offers auto-scaling both horizontally and vertically. We haven't faced any issues.

For the third-party driver support they are currently providing, they need to ensure it stays up to date with the market throughout development. If MongoDB updates a particular feature in their drivers, we as developers expect that service and support to be available in Microsoft Azure Cosmos DB as quickly as possible in production.

Its scalability is good and depends on the traffic, with auto-scaling functionality ensuring we don't need to worry about database crashes or data loss during insertion. These problems were common when deploying our data on-premises. With Microsoft Azure Cosmos DB, we have overcome those struggles and are now operating smoothly.

It depends on the application. In some cases, we use Microsoft Azure Cosmos DB directly with Azure Functions to store customer details and manage the customer onboarding process through our enterprise applications. In several instances, operations happen directly with Microsoft Azure Cosmos DB.

For legacy applications built on MongoDB that need to transition to Microsoft Azure Cosmos DB, we take a different approach. If a company is migrating from on-premises systems to the cloud—whether it’s Microsoft Azure or AWS—sometimes it’s necessary to adopt different tools for the billing process and other infrastructural needs. In such cases, we may choose to use Microsoft Azure Cosmos DB to avoid having to restructure our entire legacy application. In these situations, we utilize MongoDB and its drivers as a mediator. These drivers interact with Microsoft Azure Cosmos DB to perform the necessary operations within the application.

On another note, when using Azure Functions, we typically handle cases such as creating, updating, or retrieving customer details. This process directly connects Azure Functions to Microsoft Azure Cosmos DB. Currently, we are managing these two different patterns effectively.

If you are an engineer with good experience in microservices and the Azure platform services, it's a one-day setup process, based on requirements. If you are new to the entire Azure platform and services, it can be a bottleneck. It takes time to understand the configurations and related aspects. If you're new, there is a learning curve. You need to understand which version you're using, what features are supported fully or partially, and which features are not supported. For example, when using MongoDB drivers to interact with Microsoft Azure Cosmos DB, understanding which version (4.1, 4.2, or 4.3) you're using and what features are supported by Microsoft Azure Cosmos DB for that particular version is important. Understanding query performance improvements based on supported features is crucial. For newcomers, it might take several days to understand and review documentation. For mid-level engineers with two or three years of experience, it's a straightforward, one-day process.

Pricing is a complex process at the enterprise level. While I'm not handling the pricing directly, through stakeholder meetings and conversations, we understood that having everything in a single platform with billing up and running for all required application services is beneficial. Microsoft Azure Cosmos DB comes into a single billing system for gold or silver partners, though I'm not familiar with specific company policies and terms and conditions as I'm not an infrastructure specialist.

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

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

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

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

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

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

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

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

I have used the solution for five years.

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

Being serverless, the scalability is very good.

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

Positive

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

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

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

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

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

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

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

I develop applications. I developed an application where I had to search the Azure Cosmos DB database for values related to suspicious entities. It involved retrieving, sorting, and manually searching data through queries.

Azure Cosmos DB helped improve the quality of our search results. We could see its benefits immediately after the deployment.

The availability and latency of Azure Cosmos DB are excellent. It handles large data insertions efficiently without any problems related to scalability. It scales workloads very well.

The library of Azure Cosmos DB is like JPA, but it is not exactly JPA. We could not integrate that.

The size of the continuation token in Azure Cosmos DB should be static rather than increasing with more data, as it can lead to application crashes. They should use a static key size.

If we want to update some data, we cannot use the SQL command line. It is not like SQL Server or any other relational database. We have to send the JSON file or send the text to the Azure portal.  These are the only two options. We cannot use the normal SQL statement.

I have been using it since December 2021.

The scalability is very good. We performed performance tests, inserting objects with more than 10,000 records without any issues, although, on the application side, we started to see high memory consumption. That is because, with larger JSON files, you will have more objects in the Java application. These things consume memory, but there are no issues regarding scalability.

I have not contacted their support.

Neutral

I have experience with MongoDB but only for personal studies. I only learned the basic things.

It was easy because we have Terraform embedded in the Jenkins pipeline. Once I deploy the application, it connects with Azure Cosmos DB. It is already configured, so I do not have to worry about this part.

It took us about one month to get onboarded and understand the basic functionalities. 

It does not require any maintenance at our end.

We usually have a team of three to four people.

I am not aware of the price, but a challenge that I have faced occasionally is that running longer queries requires more RUs, so I have to ask someone with permissions to execute the queries.

I would advise learning more about queries and select statements. You can use that on the Java side and Cosmos SDK.

It is easier to learn if you already know relational databases. You can use some of that knowledge to work with Azure Cosmos DB. Also, if you know JPA, it would not be so difficult to work with the Cosmos SDK for Java application development. Inserting data is also simple.

It is at a medium level in terms of ease of use. There is documentation for gathering the information. Azure Cosmos DB does not have any constraints for the column names. If you want to create a specific query, you can find information related to that in Microsoft documentation. You can find queries to solve specific problems.

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

I am using it to store our data. We are using Azure Cosmos DB to store our JSON-based documents.

The high speed of Azure Cosmos DB compared to other competitors is remarkable. It is one of the most powerful features, offering high availability and high speed. Its benefits can be seen immediately after the deployment.

Overall, it is a good resource. I am not aware of the background, but it seems to currently support only JSON documents. They could expand their scope to support other types of data, such as XML or EDI formats. EDI is an old technology, but it is still in high use in supply chain and retail industries.

I have more than two years of experience with Azure Cosmos DB, whereas with Azure, it has been more than four years.

Choosing the correct partition key is crucial, as it affects our database speed and related operations.

Latency and availability depend on the consistency level.

It is a Platform as a Service, so we are concerned about the underlying interface. We can move to a higher tier as all Azure cloud resources are open to easy scaling.

It offers an option alongside the Azure SQL database. Azure SQL database has its own capabilities, whereas Azure Cosmos DB supports all major big data requirements like Cassandra and Gremlin. Azure SQL database is more focused on transactional data instead of analytic data. Azure Cosmos DB covers a wider area.

I have not personally deployed Azure Cosmos DB, but DevOps pipelines provide options for this. It should be easily deployable with the help of Microsoft's documentation.

It takes a couple of minutes to be up and running. It also depends on how we are deploying, whether it is via an ARM template, Azure pipeline, or directly via Azure release.

Azure Cosmos DB is generally a costly resource compared to other Azure resources. It comes with a high cost. We have reserved one thousand RUs. Free usage is also limited.

It is not like a traditional database. Choosing the partition key needs an understanding because it will affect the database speed. By making your partitions in a logical and efficient way, you can improve the speed of search analysis.

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