Microsoft Azure Cosmos DB Reviews

We mainly use Azure Cosmos DB across different projects in our service-based organization. It has been consistently used in projects that require maintaining and creating NoSQL databases. Our team leverages Azure Cosmos DB for these needs.

Azure Cosmos DB is efficient and manageable. These are the advantages of Azure Cosmos DB.

There is a lot of reusability. For instance, for integration, we can copy code snippets, and the connection is taken care of from Azure itself. Creating connections from an application to the database is easy. Doing recalls and running some queries is easy. We did not have any trouble integrating with applications. The only challenge was to apply the search over the large database in real time.

Our use case required minimal usage of the vector database, but there are a lot of personalization opportunities when it comes to the vector database. We can create as many vector embeddings as we want and customize the structure. There are no rigid rules about the structure. It is customizable. It is also AI-driven, so there are enhanced search capabilities. In terms of relevance or context of search, it is quite good to use a vector database over other databases. 

Scaling is very easy. With other databases, we have to take care of a lot of things, such as schemas and how things will transform, whereas with vector databases, scaling is hassle-free. We do not have to worry about a lot of parameters.

With Azure, resource usage is always optimized. Azure automatically takes care of a lot of things. There are many features. It can autoscale and has efficient indexing. You get asset transaction capability as well. 

The latency is quite low when it comes to search. Searching is very easy, fast, and efficient. Using vector databases means that we want to search for specific parameters.

Azure Cosmos DB offers numerous data connectors that provide a platform for seamless integration with various platforms and visualization tools such as Power BI. It allows connection via multiple data connectors to integrate data in any desired format. 

Additionally, its distribution and low latency features are beneficial. We do not need to rewrite things. We can copy a schema from a template.

It offers efficient indexing and low search latency, making searching fast and efficient and ensuring peace of mind in database operations.

For streaming platforms, Azure Cosmos DB could improve efficiency in data storage. Indexing can also be better. Enhanced capabilities are necessary to manage increased data amounts more effectively during searches.

Azure Cosmos DB provides vector search capability. I used it for an AI application. We needed a vector database that could manage and give us a dynamic connection with the application. It was quite easy to integrate with the application. Querying vector databases and writing the queries is very easy in vector databases. There is also an option for semantic search. We can use the search engines present by default in Azure Cosmos DB to search in the database. That is also useful. Most things were easy, but the vector API part was a bit tricky. If we have a lot of data, doing a real-time vector search is a performance challenge because the search happens over a large dataset. It consumes more time. It is computationally intensive and can be optimized.

I would love to see more features because the market is very competitive for cloud databases. There are many startups offering vector database integration at different speed rates or higher velocities.

We have been using Azure Cosmos DB for the last 12 months.

Azure Cosmos DB provides low latency and reliable availability. As long as instances and databases are configured correctly, stability issues are unlikely. Azure Cosmos DB would be a good choice if you have to deploy your application in a limited time frame and you want to auto-scale the database across different applications. From the availability and latency point of view, Azure Cosmos DB is good.

Scaling workloads with Azure Cosmos DB is straightforward. It has auto-scaling and global distribution features for handling dynamic, high-demand workloads. You just need to configure it correctly. 

It has a feature for multi-region scaling to scale across different regions or applications. You can also conduct horizontal partitioning. You can distribute the data across multiple partitions depending on your use cases. Handling workloads is easy.

Personally, I have not needed to contact technical support. The Azure Cosmos DB community and forums have been helpful in finding solutions without requiring direct support.

Neutral

I have used MongoDB for personal projects, but professionally, I have only used Azure Cosmos DB due to project dependencies.

Setting up Azure Cosmos DB initially was easy. We were able to deploy effectively while ensuring continuous operation and handling transaction queries without failures within one or two days.

It took us some time to realize the benefits of Azure Cosmos DB because when the platform went live, we were using it in-house and had a team of three to four people. The search quality was efficient and it ran fantastically in a small test case. After that, we rolled it out to a larger audience. We took the feedback. People liked the quality and relevance of the search. The quality of concurrent searches was also good. Over a period of one month, we observed the performance and found it to be performing well. We knew we would not have any problems from an infrastructure standpoint.

Its maintenance is quite easy. I have not faced an issue with that. Sharing it across user groups is also easy.

We formed a team and took about four to five days to become familiar with Azure Cosmos DB, given our experience in infrastructure and databases. We were able to work on our use cases within a week. 

Azure Cosmos DB's pricing is competitive, though there is a need for more personalized pricing models to accommodate small applications without incurring high charges. A suggestion is to implement dynamically adjustable pricing that accounts for various user needs. There should be smaller subscription options or a lighter version with a limited set of features for small applications.

Its learning curve is a little bit steep for those who are new. If you have a little bit of experience in infrastructure and databases, becoming familiar with Azure Cosmos DB does not take much time. 

It is easy to use if you have knowledge of NoSQL databases in general. If you know how to create schemas, then setting up the infrastructure in Azure Cosmos DB is no hassle. The basic requirement is to know about databases. That is it. Many things are managed by default in the Azure platform. You just need to take care of the specifics of your project and the regions you will be working in. These are the things that are automatic in Azure Cosmos DB.

I would rate Azure Cosmos DB a seven out of ten, considering its ease of use, efficiency, and provision for peace of mind through its features and functionalities. There is still room for improvement, particularly in pricing and feature offerings.

The use case for Microsoft Azure Cosmos DB is that some of the data we have is too large for the SQL database, but we want to be able to access it in a timely manner. I appreciate the ability to use the SQL language through a Linq type query. 

Microsoft Azure Cosmos DB helped improve our organization's search result quality significantly when we started using it about eight years ago. It greatly improved things at that time. We moved to Microsoft Azure Cosmos DB, we were in a round of product development for one particular product. Moving to Microsoft Azure Cosmos DB improved things substantially. We have been using it since then, so it could not improve anything further because we design and build our own Vector Analytics solutions.

Some of the best features of Microsoft Azure Cosmos DB are that it could scale, and we could still use SQL language through a Linq type query. 

The cost is a concern. Microsoft Azure Cosmos DB did not decrease our total cost of ownership. From the standpoint of the old way of doing DBA operations, it did, but our cloud cost increased significantly. 

Unpaid support is not very good at all.

I have dealt with Microsoft Azure Cosmos DB for eight years.

Microsoft Azure Cosmos DB is stable. We did not really have any problems with Microsoft Azure Cosmos DB for the whole eight years.

Regarding latency and availability with Microsoft Azure Cosmos DB, I did not really have a problem compared to other document databases. Compared to other Mongo-style databases, it is not any slower than the rest of them.

The scalability of Microsoft Azure Cosmos DB is fine; we did not scale to Salesforce levels. Our solution was not on that type of scale. 

The environment we are using Microsoft Azure Cosmos DB in involves thousands of devices and different customers across the country. Although we did not face any issues with Microsoft Azure Cosmos DB, our Cosmos operation wasn't complex; the only issues we faced were somewhere else within Azure.

Unranked, because we don't use it, except for the training materials.

Neutral

For the last year or so, we have been moving all of our data out of Microsoft Azure Cosmos DB into RavenDB, and we have plans for a couple of other types of databases too, so we will not be using Microsoft Azure Cosmos DB in the future. The cost is a concern, as we desire to be more agnostic and not just stuck in the Microsoft frame.

The initial setup was pretty simple for me. It took the development team a couple of months to get the UI squared away, but I had already been using SQL. They made it easy for people that were pretty good with SQL, so I did not have a problem with it.

There were six people in the development team that deployed Microsoft Azure Cosmos DB. Some of their job roles included the principal engineer, two UI developers, API developers, and DevOps development.

It's expensive. I would rate it a five out of ten for pricing.

We are still in the process of moving, so we are not completely sold on RavenDB. I have just used it more in the last couple of years than anything else, but things are changing fast. I have looked into Postgres, time series databases, and others, and I have looked into graph databases as well. I do not know if we are going to use one, but they are definitely impressive. We have to prepare for scale, but we do not have to have it to be successful, so I have looked at Apache Ignite, as well as adding open-source pub/sub on top of Postgres, and I have looked at Couch and Mongo, though we are not going to use those.

Microsoft Azure Cosmos DB is pretty easy to use compared to other document database types out there, but I prefer RavenDB more. RavenDB has better automated indexing that makes things really nice. With Microsoft Azure Cosmos DB and RavenDB, the main differences are that with RavenDB, I can move completely off and just use RavenDB while still having SQL type, relational capabilities, whereas with Microsoft Azure Cosmos DB and other document DBs, we are not really getting that. RavenDB is a great solution; it can also have costs that can get out of control, but it has built-in ETL and time series features for your vector analytics, and its automated indexing means it indexes as well as any SQL database without manual work, although you could do it manually if you wanted. Whatever combination of solutions I end up with is going to give me those opportunities as well as having the pub/sub capability, which I do not think Microsoft Azure Cosmos DB has. We never used it if it did.

I did not use Microsoft Azure Cosmos DB with Azure AI services. The core thing is that I did not want to use any Microsoft products. 

I would rate Microsoft Azure Cosmos DB a seven out of ten. It is better than MongoDB and Couch, but not as good as RavenDB.

We are in retail and marketing, and Microsoft Azure Cosmos DB gives us the opportunity as a retail industry to store catalog data. This is essentially used for event sourcing. In my department, it is particularly useful for our catalog data storage and marketing operations.

Microsoft Azure Cosmos DB has improved our overall search result quality. It is very easy to use Microsoft Azure Cosmos DB to search through large amounts of data. This is one of the advantages that I can mention with Microsoft Azure Cosmos DB, which is not available or accessible with other solutions. Searching and working with large amounts of data while using Microsoft Azure Cosmos DB is one of the biggest advantages it provides for enhanced business operations.

The aspect I appreciate most about Microsoft Azure Cosmos DB is the scalability. Horizontally, we can add as many servers as possible, which is very key for us as a company. Another important feature is that it is a globally distributed product that comes with numerous benefits. The real-time analytic features it offers, as opposed to structured query language features, provide real-time analysis for our retail and marketing operations. The integrated features, such as Azure Snipes link, enable easier running analytics for our operations. Additionally, we have noticed that it positively impacts our transactional performances as a company.

In terms of improvement for Microsoft Azure Cosmos DB, while it eliminates the burden of managing database infrastructure, we realized it might not be possible to use various models simultaneously as it only accepts a single model at any given point in time. This is an area that could be improved upon.

The operational complexity of Microsoft Azure Cosmos DB can be challenging for individuals who are not tech-savvy. Making it simpler for companies to navigate through various features would be beneficial for future development in terms of reducing its complexity. However, it remains a good product that eliminates many bottlenecks we experienced before in terms of database management, storage, transmission, and retrieval for our business.

While there is complexity in Microsoft Azure Cosmos DB, we have found that software experts and IT professionals who are passionate about the product can overcome these challenges. We have not yet achieved fifty percent in terms of training our staff due to its complexity. However, the benefits significantly outweigh the complexity, particularly in terms of database storage, management, retrieval, and transmission in milliseconds. The global access, real-time capabilities, and low latency in terms of turnaround time make it an excellent solution once fully embraced and deployed.

We have been using Microsoft Azure Cosmos DB for one year.

The initial deployment of Microsoft Azure Cosmos DB was challenging at the beginning, but we overcame these challenges and ultimately achieved positive results.

The performance and stability of Microsoft Azure Cosmos DB maintains low response times in milliseconds. It is fast, effective, and reliable.

In terms of scalability for Microsoft Azure Cosmos DB, the servers can be horizontally scaled, and we can add as many servers as needed. This capability is possible with Microsoft Azure Cosmos DB, which is not common in other solutions. This is a significant advantage of Microsoft Azure Cosmos DB.

It took us approximately three to four weeks to fully set up Microsoft Azure Cosmos DB and get it operational. Our company utilizes multiple software solutions, so integration was a key consideration. With a team of six to seven software developers, along with additional IT experts, we completed the setup within this timeframe, which we considered reasonable for this type of product.

Our company has multiple software solutions, and integration is a crucial aspect. We have a team of six to seven software developers, along with additional IT experts, who assist in working with these software solutions.

I have used SQL as an alternative to compare with Microsoft Azure Cosmos DB. Having Microsoft Azure Cosmos DB come with additional features beyond SQL capabilities was advantageous for our company's deployment.

I rate Microsoft Azure Cosmos DB a 9 out of 10 because there is always room for improvement in any software.

The benefits of Microsoft Azure Cosmos DB were immediate for us. It was within our budget, and we cannot say it constrained our finances because it was approved. The cost-benefit analysis shows that the benefits outweigh the costs. The maintenance costs are also within our estimated budgeted projections as a company.

I am willing to provide references for Microsoft Azure Cosmos DB and can be a reference for anyone interested in purchasing the same product. I am available to be contacted by Microsoft regarding this review should they have any questions.

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 main use cases involve syncing client accounting data and containers, and we use it as a read database. We do not put much into it; we just sync from their on-premises data or from other APIs, and we collect things.

We have not used enough features of Microsoft Azure Cosmos DB yet, which is why I'm here to try to use more. We're trying to figure out how to do more by linking data from things like documents and our SQL structured databases into Microsoft Azure Cosmos DB. Our goal is aggregating our clients' data to run searches or reporting, and we're trying to learn how to use it more.

I evaluate the enterprise-grade security features of Microsoft Azure Cosmos DB in terms of data encryption and access control as excellent.

The scaling of Microsoft Azure Cosmos DB's automatic elastic scaling of throughput and storage works fine in our current projects, and we use shared throughput successfully.

The scalability and ease of use with the APIs of Microsoft Azure Cosmos DB have allowed us to meet our customers' expectations pretty easily with little barrier to entry.

The features have allowed us to become SOC 2 and NIST compliant relatively easily, so I would say that's been a good success for us.

I have not utilized Microsoft Azure Cosmos DB's multi-model support for handling diverse data types.

We haven't really used the global features; we don't make it multi-regional and only have a backup, so there hasn't been a reason to utilize globalization.

There is nothing right now; that's something that we'd be interested in regarding Microsoft Azure Cosmos DB's consistency models and their role in fine-tuning the performance of our applications.

I have been using Microsoft Azure Cosmos DB for maybe five years.

I faced nothing that we couldn't overcome pretty easily; there were no significant issues. It's always a learning curve, but it wasn't hard to get past.

I wouldn't say we have benefited from the workload management by using it; we just sync data to it and make it available for people to retrieve.

I evaluate my customer service and technical support experience as great; anytime I've needed technical support, it's been excellent.

On a scale from one being the worst and ten being the best, I give my customer service and technical support a ten.

Positive

Prior to adopting Microsoft Azure, I wasn't using a different solution to address similar needs.

My experience with deploying Microsoft Azure has been relatively painless; it has been easy, and we haven't had any problems yet.

I have seen a return on investment.

We use it to sync data that is not easily accessible; the scalability and ease of integration into our system have been where our return on investment is.

We considered all Azure solutions before selecting Microsoft Azure Cosmos DB, including table storage, but Microsoft Azure Cosmos DB was a better fit, and we haven't looked at any other solutions.

I wouldn't know how Microsoft Azure Cosmos DB can be improved because I don't think we use enough of it; I need to learn more about what to use in Microsoft Azure Cosmos DB.

I find the pricing transparency of Microsoft Azure Cosmos DB to be a little confusing, but we're figuring it out.

I would recommend Microsoft Azure Cosmos DB to another organization that's considering using it. I gave this review a rating of nine.

Some of the use cases for Microsoft Azure Cosmos DB include storing log files and generating keys for our clients inside Microsoft Azure Cosmos DB. It helps us solve the problem of generating unique identifiers for our clients in Brazil, as we have many clients in our company. The system serves to generate unique keys for client attendance.

We are at the beginning of use, about 2 months in production, but it has been working well so far. We have not faced any problems.

Microsoft Azure Cosmos DB has improved our organization because there are various plans we can choose for different situations. We can scale and improve when needed, and the solution can be provided very fast when we want. The solution we use operates without problems.

We could see its benefits quickly because we can provide Microsoft Azure Cosmos DB very fast and when we want.

With Azure being our main cloud, the valuable features of Microsoft Azure Cosmos DB include integration with other Azure products that we're using and governance inside Azure. For integration with other products inside the Azure cloud, it was a better choice. 

It was easy to use and optimize Microsoft Azure Cosmos DB, as it was not difficult to configure. 

We are at the beginning of production, and everything is working very well. The price can always be lower, but currently, it's not a problem.

My experience with Microsoft Azure Cosmos DB is less than one year.

My impressions of the latency and availability of Microsoft Azure Cosmos DB are good, as we haven't faced any problems until now.

We have not scaled workloads with Microsoft Azure Cosmos DB yet, as we don't need it.

It's a large enterprise.

I would rate the support for Microsoft Azure Cosmos DB as excellent because the support team was very nice and helpful. We just send an email or call on Teams, and they quickly answer our questions.

Positive

I would rate the ease of setup for Microsoft Azure Cosmos DB as a seven out of ten. The setup was not very difficult because of the SaaS deployment, as we just needed to configure some things, such as the network and type of billing.

We did a detailed research on the solution we needed and decided to go with Microsoft Azure Cosmos DB. It took us one hour to set up the environment, tables, and connections.

In terms of the learning curve, another team is using it more extensively. I don't know if they have had any challenges. The learning curve seems to be pretty good.

For the deployment of Microsoft Azure Cosmos DB, we required two people. The roles involved in the deployment included one person from the network team and one person from the infrastructure on the cloud team.

We have seen a return on investment with Microsoft Azure Cosmos DB because we can have more control over our NoSQL solution. More control over our NoSQL solution helps us manage fraud, which can save money. We can better understand our data using this solution since we can integrate with other data and create views to understand the information.

We are not consuming so much yet since we are at the beginning of using this solution. I would rate the pricing of Microsoft Azure Cosmos DB a six out of ten.

We needed this kind of product. We are using NoSQL for the first time. We previously looked at MongoDB, but we switched to Microsoft Azure Cosmos DB because we preferred to use a native solution from Microsoft. The main difference is that Azure Cosmos DB is a Microsoft-native solution, and we prefer it because we have the support.

My advice to people considering using Microsoft Azure Cosmos DB would be that if they are using Azure and need a native solution, it is a nice choice. If they use MongoDB, they would need some APIs to integrate. 

As it is our first time using a NoSQL solution inside the company, we will probably continue using Microsoft Azure Cosmos DB. 

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

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

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

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

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

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

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

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

The topic of RU consumption needs better documentation. 

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

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

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

Neutral

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

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

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

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

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

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

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

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

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

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

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