Microsoft Azure Cosmos DB Reviews

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.

We primarily use Microsoft Azure Cosmos DB as a transactional data store and for some event-driven applications. We utilize the change feed, and the function app triggers quite a bit. MPerks, our customer loyalty application, uses it. It has become our go-to database, and we hardly touch SQL Server for new stuff.

Our developers find Microsoft Azure Cosmos DB easy to use and more scalable. The whole cloud model of only paying for what you use fits our organization well. 

The features most valuable to us in Microsoft Azure Cosmos DB are the auto scale and change feed. These features allow us to do some operations that are not possible with SQL Server. It is super configurable, allowing us to pick and choose the different Cosmos databases we need, whether or not dynamic scaling is the right thing for that workload.

Latency and availability are incredible. Given that our data is partitioned and indexed correctly, we can run queries and get results in less than five milliseconds. This has resulted in happier customers.

Cosmos is super-easy to use. It adopts a whole document database strategy with no relational data, so what you see is what you get. It's straightforward to understand, and you no longer need to worry about entity diagrams.

One of our biggest pain points is the backup and restore functionality needs improvement. They've gotten a little better in this area. SQL Server's long-term retention is amazing, and you can restore data from years ago. You need to open a support Microsoft ticket to restore your Cosmos DB backup, and it comes in on a different Cosmos account. It's just kind of a headache to restore data.

CosmosDB's ability to search through large amounts of data isn't great. It kills the RUs if you're using the transactional store. We use Synapse Analytics for our more analytical workloads. We love Synapse for that purpose.

I have been using Microsoft Azure Cosmos DB for about six years.

There are no critical scalability issues with Microsoft Azure Cosmos DB. It scales well with RUs, and it is never an issue for us. Our issues usually lie more on the application side.

The support experience has been pretty good, and I don't have a lot of complaints.

Positive

Previously, we used SQL Server. Microsoft Azure Cosmos DB was chosen because it is the go-to document data store, and our developers are familiar with SQL syntax.

New developers are able to get jumpstarted on Microsoft Azure Cosmos DB quickly. Although we learned some lessons on how to structure and partition data, the initial setup was not problematic.

I can't specify the exact ROI, but Microsoft Azure Cosmos DB has decreased our total cost of ownership.

We pay for what we use, with the flexibility to reserve our use. Autoscaling is a premium option, but it helps when our database isn't in high demand. It provides flexibility in configuring our RUs, whether we want to do it at the database or container level. We have lots of options to configure and pay for the solution. 

We evaluated AWS solutions, but ultimately chose Microsoft Azure Cosmos DB.

I would rate Microsoft Azure Cosmos DB a nine out of 10. Both Microsoft Azure Cosmos DB and Cosmos SQL DB are familiar to our developers who come from a SQL Server background.

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.

We use Microsoft Azure Cosmos DB to store document-type data, graph data, and key-value type data. It is a globally distributed database, which we mainly utilize to store document-type JSON data. 

In my project, I work with core SQL-type queries. Using the API, we are storing JSON data in Microsoft Azure Cosmos DB with a database and container-level architecture. This involves storing items using a partition key for optimized query performance.

We get data from BLOB storage. After some processing, we are storing it in the JSON format in Microsoft Azure Cosmos DB.

Microsoft Azure Cosmos DB automatically indexes documents. By indexing every field in the document, it is easy to get fast performance to retrieve the records. While fetching, it fetches only specific fields required for further processing, which makes it efficient. Fetching all the fields from a document takes more time.

Storing data with a partition key makes data fetching easier and faster.

Microsoft Azure Cosmos DB helps in fetching data faster. There is a single-digit millisecond response to fetch those records.

Microsoft Azure Cosmos DB supports scalability. At a peak time, it will automatically scale the RUs. When there is less data, it will decrease them.

In Microsoft Azure Cosmos DB, one valuable feature is its ability to store data in multiple regions. If one region fails, it automatically switches to a healthy region, ensuring minimal latency and disaster recovery without impacting data latency in applications. It scales automatically based on query performance and peak traffic.

Microsoft Azure Cosmos DB is very easy to use.

Currently, I have no suggestions for enhancement or new implementations in Microsoft Azure Cosmos DB. However, the cost can sometimes be high, especially during cross-partition queries with large data amounts.

I have been using Microsoft Azure Cosmos DB for the last two years.

Microsoft Azure Cosmos DB provides high availability with 99.9% reliability. When we store documents in Microsoft Azure Cosmos DB, it stores them in multiple regions, not only at specific regions. If one region fails, it automatically switches to a healthy region. 

There is low latency. The partition key helps achieve low latency by ensuring data is stored and accessed efficiently.

Microsoft Azure Cosmos DB offers both automatic and manual scaling. The automatic scaling feature adjusts RUs based on peak demands, which helps manage workloads efficiently. The dynamic scaling feature has helped reduce overhead costs by automatically managing resource utilization.

Our application is being used globally, and we have ten members in our team.

When I joined the organization, Microsoft Azure Cosmos DB was already in use. I have not worked with other NoSQL databases before.

It is a Platform as a Service. It was already implemented before I joined.

I started working with it in the first month. I had the support of the senior developers of the time.

It does not require maintenance from our end.

We monitor the cost daily through Azure Monitor to evaluate how much it is costing for documents, thereby keeping track of the return on investment.

Microsoft Azure Cosmos DB pricing is based on RUs. Reading 1 KB document costs one RU, whereas writing one document costs five RUs. Pricing for querying depends on the complexity of the query. If you increase the document size, it will automatically increase the RU cost.

I would recommend this solution. For e-commerce applications, it is more beneficial because it can store semi-structured data. It is the best option if you want to get data quickly because it organizes the data in a good way. When a region fails, it automatically switches to a healthy region. It has backup storage, and it scales automatically based on the peak time or low time.

I would rate Microsoft Azure Cosmos DB an eight out of ten. It is a good solution, but the cost can increase with cross-partition queries due to data distribution.

Our corporate mission is to help companies achieve more with their data, which often means unifying your data. We have a SaaS solution and have built a Copilot with Copilot Studio on top, as well as some of the Azure AI services, which is now Foundry. We are starting to use it to allow people to use natural language to ask questions of their data. We are early in our journey, but I suspect it will work well for us.

By incorporating Cosmos DB into our Azure ecosystem, we have streamlined costs and improved efficiency. The integration has allowed us to manage Cosmos alongside our other services, providing a comprehensive view of resources. The inclusion of advanced capabilities has been beneficial, positively impacting our internal operations and the services we offer to clients.

We're early in our journey, but I believe it will improve the quality of our search results. We're having a lot of success with Copilot and are excited to see how it'll work in a traditional sense as well. We're in analytics, so we work with a lot of massive data and look at tens of millions of rows. We haven't had any capacity challenges or thresholds. We started with small data, so having a tool that will grow with you is great. 

We love the ability to land data with Cosmos DB easily. Cosmos is native to Azure, so everything works seamlessly with it. You need good data to have good AI, and Cosmos makes it easy to land the data.

The recently added ability to mirror to Fabric has been beneficial. Cosmos DB enables us to handle transactional and analytical workloads in the same database. 

Cosmos DB is easy to use. You can set up a database with a couple of clicks, and it's simple to scale it up and down based on your needs. Within Azure, the Explorer UX has been great for us, too. You don't have to install another tool to run a quick query or explore some data. Additionally, the ability to estimate your Cosmos costs through the portal and manage features has been useful.

Like most database tools, it takes some time to understand. If you come from SQL or even from the Mongo world, many concepts will be familiar to you. While it takes some learning and expertise, it's not a large hill to climb. You must learn the advanced capabilities, but they make your solutions more powerful.

The vector database requires an additional engineering step to move the data from a transactional database to a vector store so that you can query it and use it in AI. However, because the vector capabilities are built in, it saved us engineering time and allowed us to get our solution out faster. 

We would like to see advancements in AI with the ability to benchmark vector search capabilities, ensuring it answers questions accurately. During our initial implementation, we faced challenges with indexing and sorting, which are natively available in other offerings but required specific configurations in Cosmos.

We have used Cosmos SQL for more than five years.

There have been no challenges with Cosmos DB's stability. We have both our SaaS app and the analytical side running without throttling issues.

The scalability is great, both horizontally and vertically.

In the past, we've worked with traditional SQL Server and MongoDB. However, Cosmos being native to Azure and the seamless integration prompted our switch.

The onboarding process was relatively quick for us. We were up and running within two weeks, including a pilot test.

The dynamic scaling during peak times has been crucial in cost management.

The integration of Cosmos with our other Azure services allows us to manage costs proactively. The built-in capabilities help control costs in line with our growth expectations through the portal. 

I rate Cosmos DB eight out of 10.

We use Cosmos DB as a database for the cache mechanism. We have a product integrating e-commerce platforms with backend ERPs, pulling merchandising data. We maintain millions of products in the ERP and store them in Cosmos DB in document format. When a query comes from the e-commerce platform, it goes directly to Cosmos.  

Cosmos is preferred because of its speed, robustness, and utilization. We have all the merchandising information in Cosmos DB, which provides concrete and optimized data when searching for new products on the site. It is faster than other relational databases.

It can query large amounts of data efficiently, depending on how you write the queries. This is a Document Database, and the system needs to read the whole document. If that is correctly clustered, then it will be faster, but if the developer makes some mistakes, it won't be optimized. 

The most valuable feature is the data writing process, where we write data into batch segments. The built-in vector database is helpful. There's one vector for the product and another for the price. I don't have much experience with vectors because we use Cosmos as a cache DB. You won't see any major challenges when you use it as a more significant enterprise application. I would rate the vector database's interoperability with other solutions an eight out of 10. 

The main area of improvement is the cost, as the expense is high. Also, when writing processes into Cosmos, sometimes the threshold is met, which can be a problem if developers have not written the code properly, limiting calls to five thousand. These aspects need addressing.

I have been using Cosmos DB for three years.

I would rate the interoperability of the vector database with other solutions as eight out of ten. It's good, but the performance depends on how well queries are written.

We compared MongoDB and Cosmos DB. Cosmos DB is easier to configure, and our team is already familiar with managing it, providing an advantage.

The initial setup was straightforward, with no major challenges. We onboarded the team in no more than three days. 

The cost of using Cosmos DB is high, which sometimes raises concerns from clients regarding the increased solution cost. While it has helped decrease the overall cost of ownership, the specific figures are not readily available.

I would rate Azure Cosmos DB eight out of 10. The solution is variously challenging but manageable once the team is familiar.

We are using Cosmos DB in several different ways. We receive unstructured and semi-structured documents from partners, and we use Cosmos DB to push the data in and scale it to kick off internal processes. 

We receive notifications from our customers to take action quickly regarding the energy grid. Cosmos DB is also used in a different project for our settlement system, where it is used as a queuing engine for the change notification portion.

The ability to scale efficiently improves our performance and scalability. Although we haven't yet used Cosmos to improve search result quality, we believe it can be useful with vector search and data architecture improvements. We are exploring AI, but I don't think our focus will be generative. We do a lot of ML models, and we plan to restructure our data to use the data lake or leverage the efficiency of already created models to reduce our resource costs and improve efficiency. 

Change notification works well, and the ability to process documents in a scalable way is important. This means we can efficiently thread out different operations and meet our organizational performance and scalability needs. 

Cosmos DB is pretty straightforward. I'm not 100 percent an expert. I have three or four different developers up to speed on it and working on it. They do most of the daily operations, while I do a lot of the prototyping and conceptual aspects.

While we don't use the vector database system, some interesting features might benefit our future data architecture. In one of the workshops, we learned about its capabilities and how it's used as part of Copilot and the backend database. I'm thinking about AI, our data, and some performance benefits.

One area that could be improved is indexing. Some of the developers struggle with the way the indexing works. We are exploring vector indexing, which we haven't examined fully yet. Indexing is an aspect we're looking to improve upon potentially.

I started dabbling in Cosmos before COVID approximately four or five years ago. Initially, I just wanted to test some concepts and figure out its benefits, using the Cosmos local engine to better understand its functionality.

We have not encountered any issues with latency or availability. As we continue to grow and scale, we will keep assessing to ensure our expectations are met.

Scaling the workloads is one of the key advantages of Cosmos, preventing the database from becoming a performance bottleneck.

We assessed other databases like MongoDB but chose Cosmos for its object-style database capabilities, user-friendliness, and ease of access. It aligned well with our needs, and a Microsoft conference initially piqued our interest.

Onboarding to proficiency took a couple of months. The transition from a traditional relational database programmer to an object database was straightforward. The learning curve was manageable and engaging.

I don't know how much money Cosmos DB has saved us. We're still using some of the old databases, but when phase them out, we'll see a significant cost reduction.

The pricing model aligns with our budget. It's expected to lower overhead costs, especially as we phase out older databases. Cosmos DB is great compared to other databases because we can reduce the cost while doing the same things.

We considered Mongo DB among other databases, but Cosmos had the desirable capabilities we were seeking.

I rate Cosmos DB eight out of 10.

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.

Positive

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.