Microsoft Azure Cosmos DB Reviews

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

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

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

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

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

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

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

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

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

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

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

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

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

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

It took a couple of hours.

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

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

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

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

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

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

We have many use cases. We are using Microsoft Azure Cosmos DB for our event streaming framework. We are using Microsoft Azure Cosmos DB to store all the event data for AI activities.

We are also using it for a RAG-based solution, though it is not entirely RAG-based. We are using Microsoft Azure Cosmos DB as a staging solution, and then we are using the AI search to index it and continue to the RAG for the LLM.

We are just using it as a staging solution. We have use cases for extracting huge documents, which can be more than 500 pages or even 10,000 pages. We cannot directly use the LLM, so we have to use a RAG-based approach. For that, we have chosen Microsoft Azure Cosmos DB and we are using the vectors there. However, instead of directly querying the vectors in Microsoft Azure Cosmos DB, we are indexing that in AI search.

The searching capability is exceptional. It is very simple and incomparable to competitors. With SQL, we have to install everything, but this is pretty quick. We have a Bicep template. Using the Bicep template to create Microsoft Azure Cosmos DB containers and partition keys makes everything convenient. Scaling is also convenient.

The RUs still appear to be a black box for everyone. Even though they explain read and write RUs, it remains unclear for many users. With Microsoft Azure Cosmos DB, we are using event streaming in the entire organization. We are using a framework for event streaming, and we suddenly reached a huge amount - the capacity of 20 GB partition key. When it reaches 100% of RUs, we face issues. We have to work on rebuilding the partition key. 

Regarding billing, we need better control. Sometimes it exceeds the forecasted budget. More clarity on RUs would be beneficial, even though documentation exists.

There is a 2 MB limitation for a document, which is a hard limit. Additionally, modeling in Microsoft Azure Cosmos DB is more challenging compared to RDBMS and other NoSQL solutions because we cannot store everything in one place. Since it's NoSQL, we sometimes need to split one document into multiple containers due to the 2 MB limitation.

I have been using it for more than two years.

Its stability is good. I would rate it an eight out of ten for stability.

Scalability is pretty good. I would rate it an eight out of ten for scalability.

I would give a low rating to Microsoft support, as whenever I talked to them, I never got a solution. I had to guide them.

If the support ticket lands in certain regions such as Sweden, they have more knowledge and the ticket gets resolved easily. At times, it moves between departments, requiring escalation to get the correct person involved.

The support team needs improvement in understanding who they are talking to. They should not ask basic questions when speaking with experienced users. I am deeply knowledgeable about Microsoft Azure Cosmos DB, which I have had to explain to the support team.

Neutral

It is very simple. We can't compare it with any competitor. We just use the Bicep template.

Its implementation takes a maximum of one hour.

Because of the lack of understanding about RUs, the costs become unpredictable. It sometimes goes over the budget.

Currently, they are implementing Fabric and OneLake solutions. Fabric appears faster. According to Microsoft representatives, querying in Fabric instead of Microsoft Azure Cosmos DB will be quicker. However, I remain confident in the querying capability of Microsoft Azure Cosmos DB.

It is pretty good, and currently, everyone wants to move from Microsoft Azure Cosmos DB to Databricks, but when I query data in Databricks, it takes considerable time with huge amounts of data. It stores in the BLOB in the backend, but when we use Microsoft Azure Cosmos DB, it retrieves the data much faster. The main consideration is being careful with fixing the partition key.

I would strongly recommend it for new projects. When you create a project from scratch, it is easy to implement Microsoft Azure Cosmos DB because the library is very pretty good. You can just use the library and create a container. I do not see any complexity at all in using Microsoft Azure Cosmos DB. 

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

For retail, all the backend data, such as merchandise items, is stored in Microsoft Azure Cosmos DB. This data is processed by backend APIs, and the UI can perform displays, printouts, edits, creations, etc.

Cost-wise, it is transparent. It supports traceability. Any activity happening in your Microsoft Azure Cosmos DB can be seen from the Azure portal via log events. If you have some sort of observability, you can centralize logging and create historical insights or virtualization based on the activity. By default, Microsoft Azure Cosmos DB provides all of that on their main portal.

It is responsive when you have a large dataset stored in your Microsoft Azure Cosmos DB. It is no problem. You can quickly scale it. Unlike traditional solutions, you do not have to deal with a separate team managing the database.

Search results have been good. It is a good experience because you can search results via the Azure portal, via a query, or via CLI. You have plenty of options. Aside from that, you can do quick scaling of your Microsoft Azure Cosmos DB whenever you have an issue with the workload, capacity, etc.

Traditional database solutions require back-and-forth coordination between teams which can lead to delays in implementing simple tasks. With Microsoft Azure Cosmos DB running on the cloud, the developer can do a quick query, and the operator can do technical analysis or troubleshooting. It is beneficial overall in terms of operational effectiveness.

Optimization is achieved through indexes. It is pretty similar to other SQL or database solutions. Microsoft Azure provides Data Studio, where you can explore your schema, tweak it, create a backup, and restore existing data within Microsoft Azure Cosmos DB. These tools make your life easier if you do not like working with the CLI.

Microsoft Azure Cosmos DB is a good solution for distributed application requirements. We can perform multi-modeling. For modern applications, I would recommend Microsoft Azure Cosmos DB. It caters to different types of applications and also provides an API base wherein you can perform automated updates for your Microsoft Azure Cosmos DB resources.

It provides all the common features that other database solutions offer. The difference is that Microsoft Azure Cosmos DB is cloud-hosted. You can host it on-prem, but running in the cloud simplifies everything in terms of support and availability.

Overall, it works very well and fits the purpose regardless of the target application. However, by default, there is a threshold to accommodate bulk or large requests. You have to monitor the Request Units. If you need more data for a particular query, you need to increase the Request Units.

I have only used the technology for three to four months.

It depends on how you configure your Microsoft Azure Cosmos DB. If you are using it as a standalone service, you are unlikely to gain the full benefits of having Microsoft Azure Cosmos DB running on the cloud. However, if you consider scale sets and scalability, for example, you can achieve higher stability.

With Microsoft Azure Cosmos DB, we created an availability zone to ensure that there is a replica of the primary Microsoft Azure Cosmos DB instance. If the primary goes down, there is a secondary database that they can use for the application. The backend application gets repointed to the secondary instance.

I do not see any problem with the latency. Connecting from your local client like Azure Data Studio to your Microsoft Azure Cosmos DB can take time, but if you are going to connect an application to the database in the same region, there is no latency at all.

It is highly scalable. I would rate it a nine out of ten for scalability.

We can quickly scale using Terraform. We can perform horizontal and vertical scaling with Terraform and apply it. It will automatically reflect in our Azure environment.

Excellent support always comes from Microsoft. If you have a problem with different services, you just raise a ticket, and someone will reach out to you. I can elevate the severity depending on the criticality of your issues and the impact.

Positive

We did not use any other solution previously because this is a new project for modernizing the merchandising area.

The setup is easy, especially in the cloud, so I would rate it a nine out of ten for the ease.

All our infrastructure layers are being controlled by Terraform. If we want to set up a new environment, it can be done within a day for not only Microsoft Azure Cosmos DB but also all resources required for an end-to-end application flow.

You can do it yourself. They have good documentation, which is easy to follow.

You can get an ROI in a year, provided you deploy it properly with the right baseline forecasted plan in terms of resource sizing. There are many factors when it comes to ROI, such as how quickly you can onboard your application and consume the backend Microsoft Azure Cosmos DB. For those new to the cloud, it might be hard to get the ROI quickly, but those with existing resources in the cloud can achieve their ROI in the short term.

It can save a lot if you perform regular monitoring. If you have a monitoring team for checking the overall utilization of Microsoft Azure Cosmos DB resources, it will save a lot of cost. You can react quickly and trim down the specs, memory, RAM, storage size, etc. It can save about 20% of the costs.

Its cost is transparent. Pricing depends on the transaction and data size, but overall, it is cheaper compared to hosting it on your corporate network due to other factors like power consumption. 

Current pricing is fine, and you can scale it afterward. You can start with a small size and scale eventually. That is a benefit of having Microsoft Azure Cosmos DB on the cloud.

It was the primary platform choice of the client at the time.

You can quickly learn Microsoft Azure Cosmos DB if you are familiar with how databases work.

Microsoft Azure Cosmos DB offers all you need for a particular database solution. It is better if you can host it in the cloud, applying security controls like data at rest and data in transit. You must ensure Microsoft Azure cloud is only accessible in a secure manner.

Scalability-wise, you can quickly scale your Microsoft Azure Cosmos DB, unlike on-premises, where you must request and procure additional resources. There is no such need; you can use infrastructure as code like Terraform and adjust the resource specs whenever you like. There are no capacity and workload concerns.

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

Our primary use case is a product to generate insights from several terabytes of data. The main problem was accuracy, as we couldn't get accurate insights because the data was hallucinating. After some trial and error, we found a solution with Azure Cosmos DB and MongoDB and got an acceptable cosine similarity score. We use Azure Cosmos DB collections and Azure functions to get the results we were looking for.

Cosmos DB improved our search result quality. Our response's accuracy rate is higher than 85 percent, which is amazing for such a large volume of data. We are searching several terabytes of data, and our harmonized data layer is pretty big. We get data from multiple global data providers. 

We use Databricks as well. The entire framework is built on Python. We have structured and unstructured data pipelines. There are multiple layers to our architecture, but Cosmos DB is the main one.

Our team has found the vCore index to be one of the most valuable features. We have tokenized and vectorized our entire database and stored this data in MongoDB collections with a vCore index, which works like magic for keyword selection. 

Additionally, the interface is user-friendly and seamlessly connects with other Azure offerings, making integration with other services easy. The learning curve was short. Our experts understand data well, but they had to build knowledge of the AI stack. It took a little bit of learning. However, it was easy to understand. In a couple of weeks, they could do everything.

The vector database is the core feature we use. Our data was not accurate, and we wanted to create a ChatGPT-type functionality where the user could ask a question in plain English like, "Show me the top 10 vegan companies in the US." But the vegan is not tagged as "vegan." It could be "plant-based," so you add that keyword. Then, it's not the end of it. Things are tagged as soya
milk," "oat milk," etc. 

There was no other way to solve our problem with hallucination and deal with a huge volume of structured and unstructured data. The only option is to vectorize. And we looked at several vector databases, but none came close. The vector database integrates seamlessly. When we use the cosine similarity search and retrieve the keywords. These keywords then eventually feed into our SQL query formation. After that, we use OpenAI to summarize everything. It seamlessly integrates with everything.

There aren't any specific areas that need improvement, but if there were a way to achieve the right cosine similarity score without extensive testing, that would be very beneficial.

I have been using Azure Cosmos DB for close to a year. 

Cosmos DB proves to be stable with its seamless integration, accuracy, and consistency, making it a reliable choice for our needs.

We have not extensively tested the scalability, but it appears straightforward with the Microsoft stack. Scalability has never been an issue for us.

Customer service and support have been great. We receive good cooperation not only from the Cosmos DB team but across the entire Azure stack.

Positive

Before adopting Azure Cosmos DB, we tried different vector databases, but none were working. It was suggested by a Microsoft colleague, and it has been fundamental to our architecture since.

The initial setup was seamless. During our proof of concepts (POCs), everything was within the Azure OpenAI stack. It worked for us and seamlessly integrated with the rest.

In the three months prior, our hosting run rate was approximately $550,000 per month, which has since decreased to $280,000 in October. Last year was more about building things. This year, we are trying to optimize things and getting the right support. 

The pricing aligns with our expectations, given our extensive use of the Azure stack. This year, we are focusing on optimization and cost reduction across the Azure stack.

We considered several vector databases. Being an enterprise customer with Microsoft, security and reliability were deciding factors. Open-source vector databases were also considered.

I rate Azure Cosmos DB as nine out of 10. The product is fit for purpose and performs well,

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

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

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

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

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

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

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

I have been using this solution for six years.

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

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

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

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

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

Positive

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Neutral

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

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

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

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

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

Its pricing is not bad. It is good. 

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

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

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.