Microsoft Azure Cosmos DB Reviews

Microsoft Azure Cosmos DB is suitable for several basic use cases. For real-time applications such as order status and shipment tracking, Microsoft Azure Cosmos DB is an excellent choice because it is lightweight. In microservices architectures, Microsoft Azure Cosmos DB is very useful because it is flexible with its JSON document format. For product catalogs with different items, Microsoft Azure Cosmos DB is beneficial because the schema frequently changes, making it ideal for scenarios with frequent schema modifications. Additionally, event-driven systems with triggers are well-supported, where events are triggered and downstream systems process those events.

I am currently using Microsoft Azure Cosmos DB for AI and real-time applications such as Copilots, personalization, and recommendations while working on an application. In this application, we receive messages in IATA standard SMS format, pass them into Microsoft Azure Cosmos DB, utilize the change feed, and send the data downstream for processing.

I have worked with Microsoft Azure Cosmos DB along with Azure Functions and have used hybrid search in Microsoft Azure Cosmos DB.

The hybrid search feature in Microsoft Azure Cosmos DB has added significant value to my AI and search workloads. The response is much faster and far better than querying SQL databases, resulting in very low response times. In my SMS-based messaging solution application where real-time response is critical, Microsoft Azure Cosmos DB has proven very helpful. We send real-time responses to customers such as 'Your ticket is booked', 'Your ticket is canceled', or 'Your flight is changed', and Microsoft Azure Cosmos DB enables us to deliver these responses effectively.

The primary benefits of Microsoft Azure Cosmos DB include horizontal scalability. The main advantage is that it handles data growth without requiring database redesign when traffic increases. Low latency access is another key benefit, as read and write operations are very fast, which is extremely helpful for real-time applications. Flexibility is also valuable because Microsoft Azure Cosmos DB uses JSON schema format, which contrasts with traditional table schemas that require time-consuming changes. In modernization scenarios where client requirements change rapidly, Microsoft Azure Cosmos DB offers more flexible schema design. High availability is another important feature, as it is well-suited for business-critical systems.

My overall experience using Microsoft Azure Cosmos DB with other Azure services shows that it is more flexible compared to SQL Database and more readable because of its JSON format, making it easy to understand. It is more interactive because viewing the Microsoft Azure Cosmos DB schema clearly shows what is happening in the application and what will be achieved. In contrast, SQL requires writing many queries to obtain details, making Microsoft Azure Cosmos DB more suitable and flexible.

My team's experience developing with Microsoft Azure Cosmos DB SDKs and APIs has been very comfortable and easy to implement. We are currently migrating .NET applications to Python-based AI solutions using Copilot, where we performed reverse engineering and are moving to forward engineering. We use APIs to connect with Microsoft Azure Cosmos DB and manage database containers and items, performing CRUD operations such as create, delete, and read operations, and querying JSON. We are using REST APIs with Python-based solutions, with a service layer and Microsoft Azure Cosmos DB integration.

Regarding improvements for Microsoft Azure Cosmos DB, partition key design is critical. Choosing the appropriate partition key based on read and write patterns is essential, and sometimes uneven workload distribution occurs, making partition key design an area for improvement. Performance optimization is another consideration, as it can be difficult to understand request units and query costs. Developers frequently encounter 429 responses due to indexing and query patterns, which is an area that could be improved.

I have worked with Microsoft Azure Cosmos DB for around two to three years in Azure environments. Initially, we were not using Microsoft Azure Cosmos DB in this capacity but primarily used SQL Database before adopting Microsoft Azure Cosmos DB.

Regarding latency and availability, Microsoft Azure Cosmos DB response time is very good. For availability, we use multiple Azure regions with our primary region in the UAE and secondary regions, so we experience no issues.

Microsoft Azure Cosmos DB handles large amounts of data effectively because of its partition key design. Partition design is critical in Microsoft Azure Cosmos DB architecture. For example, in our case, we use message ID, flight number, message type, and status as partition considerations. Microsoft Azure Cosmos DB does not automatically select the partition key, and since some values process a large proportion of the workload, we design our partitioning carefully.

We consider how we would normalize these tables, which queries are executed most frequently, what request units we need, how many requests we receive, and how many heavy indexes we need to write for high write volumes. Currently, with AI integration, our solution is very compatible. For example, we have a large business dataset because of the high volume of messages we receive. We amend these messages and send them to Microsoft Azure Cosmos DB, and then we use hybrid search which connects to Azure OpenAI. This is how we have implemented our solution.

Technical support for Microsoft Azure Cosmos DB is good, and we receive timely support. The 99.9% availability guarantee is very valuable for us. Since we use the enterprise version, the support team is very responsive when we need assistance. I would rate the technical support for Microsoft Azure Cosmos DB around 9.5 on a scale of one to ten.

Prior to adopting Microsoft Azure Cosmos DB, I worked with NoSQL databases including Redis Cache and MongoDB.

Compared to other NoSQL databases I have worked with, Microsoft Azure Cosmos DB is easier to use as a Microsoft Azure product. The UI is interactive and very helpful, and the overall Azure environment provides a good experience.

During the migration or implementation of Microsoft Azure Cosmos DB, we faced several challenges. Migrating from our SQL legacy database was the biggest challenge because moving the data was very complicated. We needed to redesign the data models and ensure performance, cost efficiency, and consistency. Since we were transitioning from a relational model to NoSQL, we faced different considerations such as partition key selection rather than relational database approaches. We also needed to convert queries, stored procedures, and joins, which presented some issues.

Microsoft Azure Cosmos DB has helped decrease my organization's total cost of ownership. Previously, we used SQL Database, which had very slow response times, and when we moved from one node to another, we experienced 15 minutes of downtime. With Microsoft Azure Cosmos DB, we now have only 4.5 milliseconds of downtime, which is negligible in the airline industry.

Microsoft Azure Cosmos DB also eliminates rework through its good partition design, which is very helpful. We are using Microsoft Azure Cosmos DB with dynamic auto-scale, which supports good partition and key design.

Regarding pricing, Microsoft Azure Cosmos DB's dynamic auto-scale and serverless models have helped reduce costs and operational efforts. The dynamic auto-scale feature automatically adjusts throughput to match the workload. We use dynamic auto-scale because we receive a large volume of messages. Since we have a large-scale application operating across multiple regions, with users in different countries, we created regions and partitions that can scale independently based on usage.

Overall, Microsoft Azure Cosmos DB's pricing model aligns with my budget expectations. Higher management makes budgeting decisions based on customer requirements and whether they want to proceed. Our development team considers whether the cost aligns with usage patterns. Currently, the pricing aligns with our usage, which is why we continue using it.

Given my experience with Azure technologies, we are satisfied with our current setup. Microsoft Azure Cosmos DB is very helpful, and whenever we move solutions to Azure environments, we definitely target Azure services such as SQL Database or NoSQL Database options. This is our recommendation.

My primary use case for this solution is collecting compliance data, which we use for SOC 2, SOX, and SOC 1 reporting.

The features that I find most valuable within Microsoft Azure Cosmos DB are probably the cost, as the cost optimization is good. The storage and queryability are good for what we're doing; it's a lot of unstructured data, so having a platform to put that in and then be able to harvest that data out for the reporting we do is essential.

In terms of cost saving, it was probably easily 30 to 40% cheaper than doing a standard SQL, which is what we saw just on piloting and getting in there. We were initially thinking 20 to 25%, but we were probably more at the 35 to 40%.

We are using Microsoft Azure Cosmos DB's hybrid search today.

The value that it has added to my AI or search workloads is that I think it's optimized that process and made it easier. We have a lot of unstructured data coming from different dissimilar systems and different data sources, so correlating those things together and making sense of it has been very beneficial.

Microsoft Azure Cosmos DB has had pretty good performance with searching through large amounts of data; it's been fast, and we haven't seen a lot of performance degradation while building larger queries and bringing in a large set of data.

The dynamic auto-scale or serverless model from Microsoft Azure Cosmos DB has helped reduce costs and operational effort; however, it's hard to quantify how that plays out since you're using a shared service. It shifts my focus away from building, managing, and upgrading to adding value.

Microsoft Azure Cosmos DB can be improved with more native out-of-the-box plugins. We have a lot of good ideas but find it challenging to implement because we're waiting for vendors to provide the necessary plugins. Building out that ecosystem would make the integration of other systems much easier.

I have been in my current role for eight years.

My impressions of the latency and availability of Microsoft Azure Cosmos DB are that we haven't had any latency issues that I'm aware of, and availability seems pretty good.

My impressions of Microsoft Azure Cosmos DB's ability to scale workloads have been decent; we have been able to scale up transparently without much worry from a performance perspective.

Customer service and technical support from Microsoft have been all right.

On a scale of 1 to 10, I would give them probably a six, maybe a hard seven at most. It depends on the issue; you often start with basic troubleshooting that we might have already done, and while there are escalation paths, getting to the right person can sometimes be a challenge.

Neutral

Before Microsoft Azure Cosmos DB, we used SQL and also had some Azure SQL, so we have a mix of those databases.

What stood out in Microsoft Azure Cosmos DB versus all the other competitors for us was just how easy it was to get it up and running; it was fast to get running with the least amount of effort needed from the team.

My team's experience developing with Microsoft Azure Cosmos DB SDKs and APIs has been pretty good; we've actually turned that into a production, internal application where people can self-service themselves on the front-end pieces we've built to the backend.

We have considered other databases; we are multi-cloud, so we've looked at a couple of competitors and piloted those, but we kind of landed on the Microsoft solution.

Microsoft Azure Cosmos DB is where we're going next for AI or real-time applications such as Copilots, personalization, and recommendations. Right now, the data itself is pretty dynamic, but the output is pretty static. The idea is to take that and make it more dynamic so that instead of creating a report, you can ask the AI specific questions about the data, and it should be able to build those reports for you.

The primary reason for switching to Microsoft Azure Cosmos DB was to try something new, which may sound unconvincing, but we wanted to see if it could prove out. There were also cost savings and optimization opportunities that seemed promising for our new projects.

My advice for someone who is considering Microsoft Azure Cosmos DB is to do some testing with it and give yourself time to think about your data in a different way, as it's a different concept from SQL. For us, it has worked out pretty well for a lot of unstructured data. I would rate this solution an 8 out of 10.

I have worked with databases like Azure SQL Database, Cosmos DB, and PostgreSQL. We had a migration where our customer wanted solutions like Microsoft Azure Cosmos DB, where they desired high availability of their databases by enabling replication, so I have worked on those.

My use case for Microsoft Azure Cosmos DB was for one of our clients where their front-end application runs via Java, and they want CRUD operations to be done. They want it to have a proper index configured, so we manually did the indexes in their previous database, which was MySQL, running on their on-premises server. Microsoft Azure Cosmos DB made indexing simpler, significantly saving response throughput and time. We worked on this use case to optimize data fetch and CRUD operations. Additionally, some customers required data replication, where they wanted data recovered from a secondary database in case of issues with the primary source, which is another use case I utilized Microsoft Azure Cosmos DB for. The caching mechanism also allowed faster data access.

I integrated Microsoft Azure Cosmos DB with our standalone and legacy-based applications, specifically One Year.

We decided to integrate Microsoft Azure Cosmos DB with the legacy-based application based on customer feedback, as the One Year application was pretty user-friendly and had a good NPS score for its interface. We faced issues with database-related operations, such as DB locks and slowness when many resources were utilized, hence we wanted to preserve the UI while scaling up the database operations, which led us to migrate to Microsoft Azure Cosmos DB.

Microsoft Azure Cosmos DB improves our organization's search result quality significantly. Previously, database locks occurred due to multiple resources accessing various databases, requiring manual intervention to release locks, which could cause application downtime. Moving to Microsoft Azure Cosmos DB eliminated these database locks, reducing surprise effects, which has been very helpful for us.

The best features of Microsoft Azure Cosmos DB include scalability; whenever we want to scale our databases, it is possible to do so dynamically. In contrast, standalone database services require special work to switch over and expand memory. The scalability with Microsoft Azure Cosmos DB, based on Microsoft Azure implementation, is much easier.

The dynamic auto-scale or serverless model of Microsoft Azure Cosmos DB helps reduce operational efforts and costs since it follows a pay-as-you-go model. Whenever we need additional resources, we can utilize them and scale down when our workload is limited, which makes this model useful compared to standard models.

The efficiency of Microsoft Azure Cosmos DB in scaling workloads is good. When we want to scale by spinning up to a higher tier, it happens quickly, and data retrieval occurs at a fast rate.

I would see improvements in Microsoft Azure Cosmos DB, particularly concerning integration with more tiers. They should integrate products like IBM Watson for analytics and offer more options for enterprise-grade systems such as AS/400, which we had a project for but could not find suitable options from the provided list.

Regarding missing features in Microsoft Azure Cosmos DB, the connectors are easy to use, and the AI feature implementation by Azure is interesting. We have ongoing POCs to analyze our record processing using the AI provided by them.

I have around five-plus years working with Microsoft Azure Cosmos DB.

Performance issues do not occur frequently, but I occasionally face performance issues with Microsoft Azure Cosmos DB. We connected with the technical team, and they mentioned there were issues with particular regions. While they do not happen often, they may occur occasionally.

My thoughts on the performance and stability of Microsoft Azure Cosmos DB focus on throughput, measuring how many data records are processed in seconds.

When evaluating customer service and technical support of Microsoft, I rate it an 8 out of 10. I have worked with AWS as well, and I find both provide good technical support whenever there are outages from their end.

Previously, we used AWS services such as RDS (Relational Database Services) and Redis for cache improvisation. We utilized both Azure and AWS solutions.

In comparing Microsoft Azure Cosmos DB with AWS technologies, I find that Microsoft Azure Cosmos DB has more pros, especially with Azure's focus on AI integration. While AWS is now integrating AI tools, they previously lacked many options. However, AWS generally offers a more budget-friendly pricing strategy compared to Azure, though Azure provides extensive features that justify the costs.

The initial setup and deployment of Microsoft Azure Cosmos DB are straightforward. The user interface is simple, allowing anyone with basic knowledge to start creating databases from scratch, which is much appreciated from the Azure perspective.

Overall, I find Microsoft Azure Cosmos DB to be cost-effective, with a good return on investment (ROI). We are provided with high availability and replication for business continuity, alleviating infrastructure concerns. Although we follow a pay-as-you-go model, the costs bring significant ROI. Previously, we maintained our own servers in Texas, which required space and maintenance from employees, leading to issues during outages. Since switching to cloud services, we have seen a satisfactory ROI.

Onboarding our team with Microsoft Azure Cosmos DB takes around a month, as we need to understand, structure, and format the setup, conducting workshops to facilitate understanding.

The learning curve for Microsoft Azure Cosmos DB is good, supported by a separate documentation page for an ideal learning process.

My advice for organizations considering Microsoft Azure Cosmos DB is to ensure that all users are well-informed about how to utilize its features. Many onboard quickly without proper understanding, which can lead to excessive replication and cost issues. Organizations need to ensure awareness of how to use Microsoft Azure Cosmos DB effectively and what services and features should or should not be utilized.

I give this product an overall rating of 8 out of 10.

My main use cases for Microsoft Azure Cosmos DB are usually warehouse software, as I'm mainly using it for storing all our data that has to go in and out very quickly.

My favorite feature of Microsoft Azure Cosmos DB is multi-region; being able to automatically enable multi-region is something I appreciate, as well as Synapse.

I appreciate it because we have a read-only data store and we don't have to do anything but check a checkbox and it's magically there.

These features benefit my company as a whole because we're multi-region, so we can handle a region down without a lot of problems, and Synapse lets us do the analytics part of it, allowing us to send that to our analytics people without causing any problems for our normal operations.

I think Microsoft Azure Cosmos DB can be improved by providing continuous backup for multi-region rights. I believe it's available for non-multi-region rights, but there are many features that are locked behind continuous backup that I can't use because it's not enabled yet.

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

I haven't noticed any problems with the latency of Microsoft Azure Cosmos DB at all, so it seems to be really solid.

The scalability regarding scaling workloads works effectively; for holidays, we had to scale it up, and that basically solved the problem.

I evaluate customer service and technical support for Microsoft Azure Cosmos DB as good; whenever I've called them, I've had good results getting a response.

Positive

Prior to adopting Microsoft Azure Cosmos DB, I was using another solution that met those same needs.

I switched because it was an on-premises solution that's being end-of-lifed, and I wanted something in the cloud.

The migration or implementation process faced challenges mainly in going from a relational to a DocumentDB; that was the biggest obstacle, trying to get the models right and the partition keys right.

I solved it through numerous iterations.

Microsoft Azure Cosmos DB has helped decrease my company's total cost of ownership.

It's easier because we have less to maintain; we're not trying to set up multiple SQL servers with replication and everything. I can maintain only Microsoft Azure Cosmos DB, and it handles everything for us. We can have one Cosmos account that has all our things, so centralization helps a lot.

Microsoft Azure Cosmos DB's pricing model has aligned with my budget expectations because I can tune the RU as I need to, which helps a lot.

Microsoft Azure Cosmos DB's dynamic auto-scale or serverless model has helped reduce costs and operational effort.

Auto-scale has been beneficial because we use it in many places, as we have a lot of cyclical things; it'll run heavy for a couple of hours and then it doesn't run at all for the rest of the day, so we use auto-scale almost everywhere.

I have probably not considered switching to another solution while using Microsoft Azure Cosmos DB since we've done other things in addition to it, but I appreciate Cosmos.

I am not using Microsoft Azure Cosmos DB for AI or real-time applications yet.

I have not used Microsoft Azure Cosmos DB's built-in vector or hybrid search.

I assess Microsoft Azure Cosmos DB's ability to search through large amounts of data by saying if I provide it the right information, it does really effectively; otherwise, if I can't provide it the partition key, then it's much more painful.

I have integrated Azure Functions with Microsoft Azure Cosmos DB.

I assess the overall experience of using Microsoft Azure Cosmos DB with these services as good; it's worked really effectively.

I have not had any problems with the integration of Azure Functions and Microsoft Azure Cosmos DB since it's pretty straightforward.

I would describe my team's experience developing with Microsoft Azure Cosmos DB SDKs and APIs as good; we've had pretty good results with it. We're starting to try managed identity; we haven't used that yet, but we're moving to that in the next couple of months. However, we haven't had any trouble with it at all.

Microsoft Azure Cosmos DB's dynamic scaling has helped decrease my company's overhead costs.

I would say it has decreased costs by at least ten percent to a quarter.

My advice to other companies considering Microsoft Azure Cosmos DB is to be prepared for the fact that DocumentDB is the trickiest thing, especially getting used to the partition keys, but other than that, the replication and everything is very handy and makes it easy to manage.

I would rate my overall experience with Microsoft Azure Cosmos DB as a nine out of ten.

We use Microsoft Azure Cosmos DB because it provides a scalable database for applications. We have multiple applications which need fast data access from multiple data models. Our model is hybrid, which is in-house and on Azure. The alignment of Microsoft Azure Cosmos DB with the Azure ecosystem is comparatively good, which is why we use this.

Scalability is the major benefit for Microsoft Azure Cosmos DB. You can scale the database very quickly. We use it for a major portion of our applications where the number of activities increase very rapidly during peak days or peak incidents. This helps in monitoring rather than manually managing the database infrastructure for our engineering team.

We use Microsoft Azure Cosmos DB for all other Microsoft Azure ecosystems, such as Azure Functions, Fabric, and Microsoft Entra ID. We use it in most of these ecosystems where it is compatible.

Our clouds are hybrid. For Microsoft Azure Cosmos DB, we only use a cloud environment, the Azure environment.

We can easily scale whenever we have spikes. The database itself stores the data and refines it, so it is highly scalable.

For my project, we have multiple members who are using it, and it is on Azure.

Scalability is the major feature of Microsoft Azure Cosmos DB. You can scale the database very quickly. We use it for a major portion of our applications where the number of activities increase very rapidly during peak days or peak incidents. This helps in monitoring rather than manually managing the database infrastructure for our engineering team.

This solution helps in lowering the operational cost for us and also gives us the bandwidth for the engineering team because they do not have to manage the database. They can put their bandwidth into development.

Microsoft Azure Cosmos DB has reduced the database administration for us, reduced our operational cost, and provides an overall good return on our cloud investment.

It is fulfilling our needs for now. However, the request units and the pricing model are major things which trouble us.

Most downtimes occur when the application traffic spikes. That is the only time where we see some sort of downtime, but with time it gets corrected.

I give a nine out of ten for scalability. It works very well for high-scale workloads and understands our EU consumptions. It does require some technical expertise, but it is highly doable.

It is good. We have less operational cost and good bandwidth for the engineering team to work on development rather than maintaining the database.

It was a lengthy process because initially it takes time. After that, we created IDs, put APIs in place, and configured it.

Microsoft is something most companies are aware of. They have their own mindset and experience with it. If I have to speak about Microsoft Azure Cosmos DB specifically, I recommend this to projects or engineering teams where the data keeps on scaling or has some spikes sometimes. Go ahead and use it. It will help you save time, and you can put more time into development rather than maintaining infrastructure and the database. I give this product a rating of nine out of ten.

My main use cases for Microsoft Azure Cosmos DB involve real-time analysis, where we store information inside Microsoft Azure Cosmos DB as a speed layer, and this use case is now in production. We do analysis with Synapse, and I currently use Microsoft Azure Cosmos DB with a generative application for an agent, serving as the memory cache for the agents. Additionally, we utilize it for user information, such as user settings, and also for microservices applications to store information about products or entities within the customer platform.

In our project, we use Microsoft Azure Cosmos DB for all things related to AI. We utilize it as an object store for tool calling for agents, where we store the results of information that the agents call from the MCP server. This capability helps our agents retrieve information and provide context to the workflow, as well as the reasoning of the model for the agents. Furthermore, another implementation of Microsoft Azure Cosmos DB is for the conversational layer of our application, which involves conversation feedbacks and all types of user information, being essential for the context of our agents and our model.

The features of Microsoft Azure Cosmos DB that I appreciate the most include the CRUD feature: create, read, update, and delete. This feature operates within a logic partition and is fully hosted by Azure, and my colleagues and I understand the primary key which is most usable within our context for utilizing this CRUD feature effectively.

Microsoft Azure Cosmos DB's ability to search through large amounts of data is very useful because it is highly responsive and performs queries faster than other types of databases such as SQL or SQLite, largely due to our capability to index the fields within the document.

We integrate Microsoft Azure Cosmos DB with Azure Kubernetes Services, Azure Functions, and Azure Container Apps, which I find very useful. For instance, Azure Functions provide a trigger for an event when a row is updated or inserted within the Microsoft Azure Cosmos DB container.

A way Microsoft Azure Cosmos DB could be improved is through the introduction of an access control list on a row and on a specific field within the document, rather than relying on application-level coding to manage different access control lists.

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

The latency of Microsoft Azure Cosmos DB is very good, and the service responds accurately, with high availability of data and backup, which is critical for us when our application is in production.

Microsoft Azure Cosmos DB scales workloads effectively, and when I set the scaling to automatic, it responds quickly and performs very well.

I have not had any direct contact with customer support for Microsoft Azure Cosmos DB. However, I have found that support for other services such as Azure Functions is very prompt and effective, with people responding quickly and providing valuable assistance.

Neutral

In my company, I have not used another kind of NoSQL implementation before utilizing Microsoft Azure Cosmos DB.

I can tell you that there has been a thirty percent reduction in costs and operational efforts, with reductions also reaching up to fifty percent and forty percent in different instances.

I find Microsoft Azure Cosmos DB's pricing model correct and aligned with my budget expectations. For all the use cases with my customer, I see considerable potential to arrange a budget for this type of service.

The dynamic autoscale or serverless model of Microsoft Azure Cosmos DB has indeed helped reduce our costs and operational effort by allowing us to scale horizontally in a straightforward manner according to our needs. For example, when the workload increases, we can easily scale manually, and we can also set a threshold for scaling to ensure maximum performance for the service.

My company has not considered switching to other products.

I am using Microsoft Azure Cosmos DB for both AI and real-time applications.

I have not used Microsoft Azure Cosmos DB's built-in vector or hybrid search because our clients prefer to use MongoDB with the RU feature. Therefore, I do not use Vector DB in production; however, I have utilized the Vector Database in simpler cases based on my experience. In other scenarios, we resort to AI Search because our clients opt for Azure AI Search.

In terms of my team's experience developing with Microsoft Azure Cosmos DB SDKs and APIs, I primarily use only the SDK and not the APIs, as the SDK fulfills all our requirements for the deployment lifecycle. I rely heavily on the SDK, for example, to create indices within a container during production maintenance and for configuring the release of our code to production. Creating indices and managing the container is very simple with the SDK, which I find is excellent for Microsoft Azure Cosmos DB for NoSQL, although I notice that the Mongo SDK does not integrate as effectively for this type of database.

Microsoft Azure Cosmos DB's dynamic scaling has indeed contributed to decreasing our operational overhead costs.

My advice to other companies considering switching to Microsoft Azure Cosmos DB is that the SDK makes it very simple to create a container or a database, and managing it, including backup and restoring to a previous data point, is very straightforward. I rated this product with a nine out of ten.

Our previous use cases for Microsoft Azure Cosmos DB were mostly for multi-tenant database storage when we needed it. We recently decided to rebuild one of our major products, which is a content management system, on Microsoft Azure Cosmos DB. It's not necessarily a database as much as a content engine, and it has been a major investment to re-platform.

Microsoft Azure Cosmos DB has reduced our total cost of ownership significantly, allowing us to sell our product at extremely competitive pricing, and Microsoft Azure Cosmos DB is a majority of the reason why that's true.

Super sharp latency and excellent availability are one of the best parts of Microsoft Azure Cosmos DB. Microsoft Azure Cosmos DB's ability to search through large amounts of data is excellent, unless we're talking about AI search with its cost problems. 

The challenge that we wanted to address with the built-in vector database was content discovery. Content discovery is a really hard problem in content management, and being able to find relevant content is a big-scale problem. We are using Microsoft Azure Cosmos DB for semantic search and providing AI capabilities that are beyond just simple vector search. It has been good; however, there have been some challenging issues for us.

We are really happy with the AI services in Microsoft Azure Cosmos DB, particularly their hybrid search feature, which combines AI search with full-text search, providing us with a great mix of search results.

Overall, Microsoft Azure Cosmos DB is phenomenally easy to use; it is very good. As NoSQL databases go, or as an Azure product, it's great. The search, configuration, and ease of cost management have been a really great experience.

The efficiency of search capabilities is significant, particularly when it comes to the flexibility of conducting in-depth, almost recursive searches that are both efficient and cost-effective. We are currently part of an early access program for a new feature called Fleet Spaces. This feature allows us to manage costs across different tenants effectively. In our setup, each customer has a dedicated tenant in Microsoft Azure Cosmos DB, which means we do not share databases. This approach provides the additional security and restrictions our customers desire, and it helps avoid issues related to noisy neighbors. However, the challenge with this model is that we cannot easily share costs between tenants. Fleet Spaces offers a solution that combines the benefits of multi-tenancy while still isolating each customer's tenant. This is especially important for large enterprises and marketing companies that prefer to keep their content completely separate from others. Additionally, we can share bursts of resources across these tenants, allowing us to gain some advantages of multi-tenancy without compromising security. The vector search functionality is also noteworthy, as it provides all the necessary tools for implementing AI-driven search capabilities. My architect could probably add more insights, but overall, we are pleased with the solid uptime and reliability of this new feature.

The speed of search with Microsoft Azure Cosmos DB is 10 to 100 times faster, and while I don't have a metric yet for recall, I can say that the actual ability to get more content does not have a static metric currently.

For integrating Microsoft Azure Cosmos DB with other Azure products or other products, there are a couple of challenges with the current system. Right now, the vectors are stored as floating-point numbers within the NoSQL document, which makes them inefficiently large. This leads to increased storage space requirements, and searching through a vast number of documents in the vector database becomes quite costly in terms of RUs. While the integration works well, the expense associated with it is relatively high. I would really like to see a reduction in costs for their vector search, as it is currently on the expensive side.

The areas for improvement in Microsoft Azure Cosmos DB are vector pricing and vector indexing patterns, which are unintuitive and not well described. I would also like to see the parameters of Fleet Spaces made more powerful, as currently, it's somewhat lightweight. I believe they've made those changes intentionally to better understand the cost model. However, we would like to take a more aggressive approach in using it. 

One of the most frustrating aspects of Microsoft Azure Cosmos DB right now is that you can only store one vector per document. Additionally, you must specify the configuration of that vector when you create an instance of Microsoft Azure Cosmos DB. Once the database is set up, you can't change the vector configuration, which is incredibly limiting for experimentation. You want the ability to try different settings and see how they perform, as there are numerous use cases for storing more than one vector in a document. While interoperability within the vector database is acceptable—for example, I can search for vectors—I still desire a richer set of configuration options.

We've used Microsoft Azure Cosmos DB in smaller circumstances around our ecosystem for about 3-4 years. Recently, we've made a major investment in shifting our main product's database to Microsoft Azure Cosmos DB. While we've been using it for 3 years, our bigger investment project has been ongoing since December.

The stability of Microsoft Azure Cosmos DB is excellent. The uptime is five nines and very consistent.

I rate the scalability of Microsoft Azure Cosmos DB as excellent, as it is the best.

Approximately 50 users work with Microsoft Azure Cosmos DB in our organization.

We are in a privileged position because we have access to the product team. I don’t think I can speak for most other organizations in terms of customer support, as we are part of many of their early access programs. This means we can easily communicate with the principal product manager of Cosmos DB, and he responds within a day. It's been phenomenal, but I don't think this is how everyone else operates.

Prior to using Microsoft Azure Cosmos DB, we were using SQL Server. The cost model of Microsoft Azure Cosmos DB is great. The SDKs are better than MongoDB. Microsoft Azure Cosmos DB is distinctly better for what we need.

The initial setup for Microsoft Azure Cosmos DB is really straightforward. My developers love using it, and they find it very easy with strong SDKs, so there haven't been many moments where they don't know what to do next. It took us a couple of days to deploy Microsoft Azure Cosmos DB in our lower environments.

Microsoft Azure Cosmos DB does require maintenance. Most of the maintenance involves two main aspects. Firstly, we did not have NoSQL database administrators, which made the transition more challenging. We had to learn the skills necessary for maintaining a NoSQL database, requiring a significant investment in our own development. Secondly, we are constantly engaged in performance tuning and cost management, both of which require ongoing attention.

It took a couple of days to onboard our team with Microsoft Azure Cosmos DB, and I found the learning curve straightforward. The hardest part was for the DBAs since they are trained in SQL and struggle to transfer that skillset, but developers onboarded quickly.

Regarding return on investment with Microsoft Azure Cosmos DB, we estimate a significant amount of time saved. Our competitor took 2 years to rebuild their content management system, while we plan to go live in 8 months, and we feel that a large amount of that is due to using Microsoft Azure Cosmos DB.

This cost model is beneficial because it allows for cost control by limiting resource units (RUs), which is ideal. However, for our needs, we can't engage with their minimum pricing, which ranges from 100 to 1,000 RUs. At the bare minimum, we need to use 4,000 RUs for a customer. I would like to find a way to gain some advantages from the lowest tier, particularly the ability to scale down if necessary. It would be helpful to have more flexibility in cost management at the lower end.

The RU model is great for planning costs, but their vector search is prohibitively expensive and needs to be reevaluated. The pricing just doesn't make sense. For instance, if I want to search a hundred thousand documents in the vector database, it would cost me thousands of RUs, whereas reading from a traditional database only costs one RU, regardless of its size. Even if it's an AI read, the disparity in pricing is concerning. The overall scaling of costs seems off.

We did an extensive evaluation of different databases and different database models. We evaluated SQL Server, Cosmos Postgres, MongoDB, Neo4j.

I would definitely recommend Microsoft Azure Cosmos DB due to its reliability, cost management, and being a really high-performance database.

I would rate Microsoft Azure Cosmos DB overall 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.

I use Microsoft Azure Cosmos DB for creating containers, stocking data, and retrieving data from it.

Microsoft Azure Cosmos DB's dynamic auto-scale or serverless model helps reduce costs or operational efforts, though I don't have extensive knowledge about this feature.

I believe Microsoft Azure Cosmos DB may have helped improve my organization's search results quality, but since the application was developed from scratch, I cannot make a comparison. However, I think it has contributed positively.

I don't have detailed ideas about how Microsoft Azure Cosmos DB can be improved.

Microsoft Azure should clarify the support process itself, as sometimes it requires clarification.

I have been working with Microsoft Azure Cosmos DB for eight months.

I find Microsoft Azure Cosmos DB stable enough at the moment.

Microsoft Azure Cosmos DB's dynamic auto-scale or serverless model helps reduce costs or operational efforts, though I don't have extensive knowledge about this feature.

I would evaluate the technical support by Microsoft Azure as approximately seven.

Before Microsoft Azure Cosmos DB, I worked with SQL Server and PostgreSQL, which are relational databases.

The initial setup and deployment of Microsoft Azure Cosmos DB is easy. I don't have a large database; I have a small database, which is why I didn't encounter any issues.

In the project that I contributed to, there were a maximum of five developers using Microsoft Azure Cosmos DB.

I don't have detailed ideas about how Microsoft Azure Cosmos DB can be improved.

At the moment, I don't have information about latency and availability issues and problems in production, so I cannot provide feedback about that.

I don't use the built-in vector database capability of Microsoft Azure Cosmos DB, and I'm not familiar with this feature.

I don't have knowledge about Microsoft Azure Cosmos DB's ability to search through large amounts of data because I don't work on a large database.

It is somewhat complex to use and understand Microsoft Azure Cosmos DB. To query data, you will need to put good architecture in place and determine how to do it.

For the learning curve of Microsoft Azure Cosmos DB, I would rate it around five.

Integrating Microsoft Azure Cosmos DB with other products on the application side is easy.

I have used the vector database with Azure AI services.

I would rate this review as five overall.

I use Microsoft Azure Cosmos DB for several purposes. The main use cases for Microsoft Azure Cosmos DB include handling distributed databases and managing globally scaled applications.

Microsoft Azure Cosmos DB brings key benefits to the table and helps improve the way our organization functions. Microsoft Azure Cosmos DB makes my life and my team's life easier, and there are specific benefits that I see from having it.

I appreciate several aspects of Microsoft Azure Cosmos DB. The best features include its dynamic scaling capabilities. Microsoft Azure Cosmos DB's dynamic scaling has helped decrease our organization's overhead costs. Microsoft Azure Cosmos DB's pricing model has aligned well with my budget expectations.

I would like to see continued improvements in the developer experience and in making advanced capabilities easier to configure and operate. As I have worked with Cosmos DB SDKs, APIs, vector search, hybrid search, autoscale, and serverless capabilities, simplifying the configuration and operational management of these features would make the platform easier to adopt for development teams.

I have been working with Microsoft Azure Cosmos DB for a period of time.

My impressions of the latency and availability of Microsoft Azure Cosmos DB are positive.

My impressions of Microsoft Azure Cosmos DB's ability to scale workloads are favorable.

I have some knowledge about the customer service and the technical support team of Microsoft Azure Cosmos DB.

I did not previously use a different product and joined the project where Microsoft Azure Cosmos DB had already been implemented.

When I started working with Microsoft Azure Cosmos DB, the onboarding process was straightforward, though I did face some challenges and complexities at the initial stages.

Microsoft Azure Cosmos DB has helped decrease our organization's Total Cost of Ownership.

I have knowledge about the pricing, setup cost, and licensing aspect of Microsoft Azure Cosmos DB.

I am currently working with Azure solutions. I work with Microsoft Azure Cosmos DB for databases. I have experience with Microsoft Azure Cosmos DB and PostgreSQL.

I am using Microsoft Azure Cosmos DB for AI and real-time applications, including Copilots, recommendations, and personalizations. I have integrated Azure services and tools such as Functions, OpenAI, and Fabric with Microsoft Azure Cosmos DB.

My overall experience of using Microsoft Azure Cosmos DB with these services is positive. My team's experience developing with Microsoft Azure Cosmos DB SDKs and APIs has been solid.

I have used Microsoft Azure Cosmos DB's built-in vector and hybrid search capabilities. Microsoft Azure Cosmos DB's ability to search through large amounts of data is strong.

Microsoft Azure Cosmos DB's dynamic auto-scale and serverless model have helped reduce costs and operational efforts for our organization. The auto-scale and dynamic auto-scale features with Microsoft Azure Cosmos DB and the serverless model help improve our operational efforts overall.

I would advise other organizations considering Microsoft Azure Cosmos DB to evaluate their specific use cases and scaling requirements carefully. My overall experience with Microsoft Azure Cosmos DB has been positive.