Microsoft Azure Cosmos DB Reviews

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.

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.

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.

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.

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.

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.