Microsoft Azure Cosmos DB Reviews

In our project, I used Microsoft Azure Cosmos DB primarily for storing new or updated JSON documents.

With SQL Server, we have to use a lot of joins when a lot of tables are present in different databases. When we join tables present in different databases, we first load a table in memory and then apply join on them. With Microsoft Azure Cosmos DB, we do not have to do that. It solves the problem of joining different tables.

We did not have to convert JSON files to a relational database format. We did not have to separate the JSON file into a data model. We could directly use those files. We did not need any primary-foreign key relationships or any relationships between tables. We just needed a partition key. Based on that, we could simply save data into Microsoft Azure Cosmos DB.

Its performance is good. Integrations are very quick. In my project, Microsoft Azure Cosmos DB was at the center of the business. Everything was running around Microsoft Azure Cosmos DB. Performance-wise, it solved all the latency problems that they were facing before.

Microsoft Azure Cosmos DB is very fast. Data retrieval and data storage are very quick. It is known for its speed and efficiency, with quick data retrieval and storage operations without latency. You can do a lot of operations in real time.

One area for improvement is the ease of writing SQL queries and stored procedures in Microsoft Azure Cosmos DB. Writing an SQL query and a stored procedure on top of that is a little challenging. It is not so easy with Microsoft Azure Cosmos DB. It requires some understanding. It is a relatively new product, so the knowledge gap is there. There should either be better documentation or an easier way to implement. We should be able to write a stored procedure in a simple language like SQL.

Additionally, there should be support for maintaining large files. It does not support files that are more than 2 MB in size.

Other than that, I do not have any input. It is a good product. It solves all the problems I have seen.

I have been using Microsoft Azure Cosmos DB for three years. I last used it about six months ago.

I have not encountered any stability issues with Microsoft Azure Cosmos DB. Its stability is commendable. I would rate it a ten out of ten in terms of availability and latency.

There was a challenge concerning scaling related to RU limits, but Microsoft has introduced dynamic RUs to tackle this issue. I am not sure about its recent effectiveness, but earlier, I manually increased RU capacity to address concurrent access.

It is capable of quickly searching through large amounts of data, but our project was not very extensive. We did not have a lot of records. However, it can support a large amount of data. From this aspect, it is a brilliant product.

We had about 40 people on our team using Microsoft Azure Cosmos DB.

I rarely needed to reach out to Microsoft for technical support regarding Microsoft Azure Cosmos DB. After it was up and running, we did not require much support.

Neutral

Other than Microsoft Azure Cosmos DB, I have used SQL databases. I have not used any NoSQL database.

It was a PaaS solution. I was not involved in its initial setup, but it is simple and quick, taking about five to ten minutes. If you want concurrency, some documentation is available, but it would be helpful to have some hands-on examples.

We used the ARM templates available in the Azure portal for deployment. We had CI/CD pipelines, and we deployed them using ARM templates. That is the strategy we use for the deployment of Microsoft Azure Cosmos DB.

It does not require any maintenance from our side.

It takes about three months to train someone on it. They only need to learn how to query the database.

It took me around one and a half years to understand the real benefits of Microsoft Azure Cosmos DB. It is a nice product.

In terms of performance, Microsoft Azure Cosmos DB benefited us greatly by solving latency and data retrieval issues, but I cannot comment on cost savings as the financial aspects were managed by others.

The pricing is perceived as being on the higher side. However, if you have large data operations, it might reduce costs due to performance efficiencies.

I did not evaluate other NoSQL databases; the client chose Microsoft Azure Cosmos DB based on its performance.

I would recommend Microsoft Azure Cosmos DB if you are looking for performance. I am not sure about the pricing, but if you have a large number of users, Microsoft Azure Cosmos DB is helpful.

If you are using proper indexes, data retrieval is fast and search is easy. Otherwise, it will take a lot of RUs to get the results.

If you are migrating from traditional or legacy workflows to Microsoft Azure Cosmos DB, it would require a lot of rework. For new implementations, Microsoft Azure Cosmos DB is advisable.

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

We have been using Cosmos DB for everything involving non-relational data. Recently, we’ve been utilizing it more for AI purposes, especially for conversation histories.

Cosmos DB performs well with production workloads that have many gigabytes of information. You can scale it to add more capacity while providing the level of performance that customers expect.

We value the replication and regional availability features that Cosmos DB provides. The replication includes read replicas and write replicas. The recent addition of vectorization and similarity comparisons add values for AI workloads. The performance and scaling capabilities of Cosmos DB are excellent, allowing it to handle large workloads compared to other services such as Azure AI Search.

The solution is straightforward in terms of the interface, API set, and automation capabilities. The learning curve is short if you're familiar with the world of non-relational data. It takes about three to six months to learn about the distribution capabilities of Cosmos as a service. It takes a bit more time to learn the networking settings that you can use for Cosmos in South Asia, including virtual networks, private networks, finance, etc.

The vector database is interesting. We haven't used it before. We were using Azure AI search for that, but we've had great conversations with the product team, and we realize that a couple of workloads are more appropriate for Cosmos. The search results quality is still determined by Azure AI search, and we haven't used vector databases in production workloads. However, from what we've seen from a hands-on demo, it can help.

A minor improvement would be enabling batch operations through the UI. Currently, to delete all documents in a collection, we must use an API, which some of my team finds inconvenient for admin tasks.

We have been using Cosmos DB for six years.

Cosmos DB performs exceptionally well and has not caused any issues that necessitate adjustments in nodes for improved performance.

The solution scales exceptionally well. Partitioning, indexation for collections, and the dynamic scaling feature allow us to manage performance and costs effectively. We like that it can auto-scale to demand, ensuring we only pay for what we use.

Before Cosmos DB, we used Python for non-relational data. However, choosing Cosmos was straightforward due to our focus on leveraging Microsoft services.

For our migration, we had to integrate both products. The source had its own interface and APIs. In terms of syncing data, we had to incorporate it directly with Cosmos. Cosmos has excellent APIs and operation services. It was easy to orchestrate migration from one data center system to the other.

The challenges were more related to network performance than anything else. We had to build our migration very close to the Microsoft network. We were working with higher network latency by doing it inside a VM on Azure.

Our team built a migration orchestration integrating both the source and Cosmos systems using APIs.

Since starting with Cosmos DB, we have seen an overall reduction in our total ownership cost of 5 to 10 percent.

Everything could always be cheaper. I like that Cosmos DB allows us to auto-scale instead of pre-provisioning a certain capacity. It automatically scales to the demand, so we only pay for what we consume.

I rate Cosmos DB nine out of 10.

Our primary use case for Cosmos is the storage of shell-fed signs and our pricing systems. We use it as a transactional database on the back end.

The ability to scale automatically is very valuable. Additionally, multi-region support automatically synchronizing to a different region for multi-region applications is a cool feature. It's more of a lift with other databases to configure that extra region and set up replication, even if it's on the cloud. With Azure, it's just a button click. It's that simple. 

The learning curve depends on your background. It takes time to learn if you're from a relational database background like us. However, it's fairly straightforward from a scalability perspective once you get the hang of it. You need to be aware of certain concepts like partitions and partition keys. Once you get those, I think it's fairly okay.

The auto-scaling feature adjusts hourly. We have many processes that write stuff in batches, so we must ensure that the load is spread evenly throughout the hour. It would be much easier if it were done by the minute. I'm looking forward to the vector database search that they are adding. It's a pretty cool new feature. 

I have used Cosmos for about five years.

The latency and availability are good. I don't have any complaints there. It goes back to how you're retrieving data and whether it's structured correctly. 

Cosmos can definitely scale well, but it comes with a cost. One of our databases is quite large and scaled up significantly due to our needs.

We have two databases, so it was challenging to define the partition key and ensure the workloads are spread out. We have millions of records in our Cosmos database, so spreading that out was difficult. We had to spread out the load to avoid a 429 error for a request that was too large. Partition key is more of a learning experience to understand the right thing to do. The entire process took around six months. It wasn't too hard.

We have reduced our overhead through dynamic scaling. I can't say precisely how much we've reduced our total cost of ownership using Cosmos DB, but we have a similar on-prem workload running on SQL, and we only pay a fraction for Cosmos.

Cosmos is cheaper than other solutions, but you must be smart about how you use it to keep costs down. We've made mistakes where the cost has increased more than we expected. You have the opportunity for it to be cheap or costly.

We were looking for a multi-region document database, and the ease of configuring multi-region in Cosmos was a significant factor in our choosing the solution. We also wanted to bring costs down, which was the other reason.

I would rate Cosmos an eight out of ten. Be cautious about spreading out the load evenly, especially when dealing with large volumes to prevent getting errors.

I'm the primary systems architect at DocuSign. We just launched a product at called Intelligent Agreement Management, and a central pillar of that is schema understanding. We use Microsoft Azure Cosmos DB as our schema store. It's the brains of our entire system. 

It is integral to our business because it helps manage schema and metadata for all our documents and customers. The AI insights we glean based on Azure OpenAI also end up in Cosmos DB. We need a NoSQL store because the schema is dynamic and flexible, so Cosmos DB is a great fit. It has four nines or possibly five nines availability, excellent geo-distribution, and auto-scaling. 

Cosmos DB has improved search result quality, throughput, and query latency. There are trade-offs to finding the sweet spot among all of these. Having a NoSQL solution that can do that in a 100 percent Azure shop is the best fit we could want. 

The features that stand out as most valuable are the autoscaling and hierarchical partition keys. We use account IDs at a higher level and entity IDs at a lower level. That gives us optimal query performance for our workloads.

AI has been a game-changer for new people without expertise, making it easier to use and optimize. You can ask GPT or Copilot for optimization strategies. If you have queries that are not performing well, you can feed the same queries, execution plan, and other things to the AI. The AI returns reasonable recommendations for what to do. 

Cosmos DB is effective at handling large queries. At DocuSign, we're processing over a billion signers and massive agreements and contracts. These things are being used for business-critical workloads, so performance, scale handling, and latency are crucial. Without these, we wouldn't have a product that anyone would want to use. 

I have worked with Cosmos DB at my company for the past 18 months, but I have used Cosmos at Microsoft for nearly a decade.

Cosmos DB provides impressive stability due to its high availability and ability to handle massive data volumes, which is essential for our business-critical workloads.

We have found Cosmos DB’s scalability to be exceptional, enabling horizontal and vertical sharding and supporting massive scale with efficient auto-scaling.

The team behind Cosmos DB has been highly responsive, providing excellent transparency and high-quality postmortem reviews during incidents, ensuring continuous support and improvement.

Positive

The initial setup was straightforward. Cosmos DB's integration went quickly due to the team's prior experience with Azure services, allowing us to prototype within a couple of months.

The challenge for us is always scale. We needed to move all the tables in lockstep that are involved in join queries. In some cases, we came up with a structured pipeline where stage one would go to SQL, and some of the query hints for the Cosmos DB thing would come from that first stage and so on. That was a migration challenge in normalizing the data, bringing it into Cosmos, and then, again, denormalizing some of the data. 

The critical mass of internal expertise, particularly from people previously working with Azure, enabled a smooth implementation with Cosmos DB.

Cosmos DB has always met our targets. However, we've always had our schema store on Cosmos DB, so it's not like we started with something expensive and brought our TCO down using Cosmos. Still, it's an excellent option for NoSQL or semi-structured data because our agreements start as a morass of raw data from PDF, OCR PDF, or paper OCR scans. 

After that, we match the structure with a known entity and for that known customer and run queries on Cosmos DB to bring out the rest of the structure and use AI to enhance it even further. In some cases, the customer will add custom fields to their entities. Cosmos gives us a low turnaround time from when the dynamic nature kicks into when the results return from that new schema information back to the same customer. It's a rich, complex scenario, but also a massive scale of data and customers.

The pricing model for Cosmos DB has aligned well with our budget expectations. We did not encounter pain points related to costs and found it cost-effective compared to high-end SQL solutions initially considered.

When I joined, the company was already invested in Azure, so there was never a bake-off between Cosmos DB and offerings from AWS. We implemented Cosmos initially because we have a massive transaction database on SQL. On things like the total cost of ownership, Cosmos DB shines. It seems to be the correct approach for our semi-structured data and our schema and entity store. A combination of Cosmos DB and SQL Azure was how we shaped our architecture on this journey, but we didn't evaluate Cosmos DB against non-Azure NoSQL databases.

I would rate Cosmos DB as an eight out of 10 for its overall capabilities, responsiveness, and alignment with our needs.

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.

Our primary use case for Azure Cosmos DB is mainly as a Document DB and vector DB.

Azure Cosmos DB is very easy to use. We do not have to spend a lot of time on its optimization.

There is a lot of reference code we can use. It is very easy. We could grab some code to interact with the database.

We have integrated the vector database with some of the IoT applications and recently, some AI-related topics because it is a cloud-native service. Our company offers professional services to help customers bring their own applications to the cloud. The cost and performance are some of the main benefits of the vector database. 

The integration of the vector database with Azure AI services is great. In most applications right now, we use the logic of vector search and the traditional way of using full-text search. It is easier for the applications to get those search results.

I am more on the presales side. Most of the time, we do a quick demo for our customers. We only spend about fifteen minutes building a simple application with the RAG functionality with the customer's own data. That is very impressive.

It provides good SLAs and requires less effort in maintenance.

The global synchronization feature of Azure Cosmos DB stands out as the most valuable for me. It is a reliable and consistent storage solution, suitable for various data types. It is always available. Additionally, it is cost-effective.

I do not have any specific suggestions for improvements at the moment. However, having more AI capabilities in the future would be beneficial.

I have been using Azure Cosmos DB for three or four years.

The stability of Azure Cosmos DB is very nice, with features like cross-region synchronization that allows fast and reliable performance.

The latency and availability of Azure Cosmos DB are very nice. There are cross-region synchronization features. The speed is very fast.

Azure Cosmos DB scales well, both in terms of capacity and performance. You can adjust the Request Units (RUs) as needed, and the cross-region synchronization allows easy scaling across different locations.

As compared to a traditional RDBMS, Azure Cosmos DB’s dynamic scaling decreases an organization’s overhead costs by half.

We previously used Redis and Postgres for vector databases before they were supported in Azure Cosmos DB. In the beginning, the vector database was not supported with Azure Cosmos DB, so we had to use the Redis or Postgres database, which was expensive. Azure Cosmos DB is cheaper.

Our company offers consulting services for Microsoft-related products. This is one of the reasons for recommending Azure Cosmos DB, but sometimes our customers use MongoDB and other solutions.

The initial setup of Azure Cosmos DB was easy. During the migration or implementation of Azure Cosmos DB, there are sometimes some incompatibility issues, but they are minor issues.

It was easy for our team to use. It took them one week to know the system and work with it. It takes our team members about four weeks to earn their certification for Azure Cosmos DB. There is a special certification for Azure Cosmos DB.

It is cost-effective. They offer two pricing models. One is the serverless model and the other one is the vCore model that allows provisioning the resources as necessary. For our pilot projects, we can utilize the serverless model, monitor the usage, and adjust resources as needed.

I would rate Azure Cosmos DB an eight out of ten. There is room for growth, but Microsoft is constantly releasing new features and moving very fast.

Our primary use case is mirroring the data for reporting.

Cosmos DB has helped us by providing faster response times for everything, which significantly improved our search results quality.

Azure Cosmos DB's graph queries are its most valuable feature. Although I have not yet explored vector search, it's coming to Cosmos DB, and I plan to look into it. Having data in a flat-file format in a document database speeds up processes, which is the primary purpose. Additionally, Cosmos DB's use of the Mongo platform makes it intuitive and cost-effective.

I use Azure AI services, including cognitive services and OCR. I recently built a chatbot using the model. Cosmos DB integrates well with other apps. 

There are no specific areas I believe need improvement as I am happy with what I am getting currently. However, I am open to new features in future versions, like possibly integrating AI features natively into Cosmos DB. Any improvement would be beneficial.

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

I have not encountered any issues related to the stability of Cosmos DB. The challenge does not lie in the technical aspect of Cosmos DB but in the non-technical aspects.

Cosmos DB has impressive scalability. We have been able to scale workloads as needed during peak hours without any issues, effectively meeting our expectations.

The initial setup was quite quick, taking only a few days for the team to be onboarded with Cosmos DB. The primary challenge was non-technical.

The implementation involved just the normal onboarding process for any human resource.

Our organization's total cost of ownership has been reduced by 20 percent due to the backend data mirroring for setting up the repository for reporting purposes. Dynamic scaling has also helped decrease our organization's overhead cost by automating the scaling process and reducing the need for human intervention.

Our experience with the pricing and setup cost is that it aligns with what we expect based on the pricing we see. However, I would absolutely like it to be less if possible.

I rate Azure Cosmos DB eight out of 10. There is always room for improvement, and the company could develop new features that could make it even better, but I am very satisfied with the current performance.

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.