Microsoft Azure Cosmos DB Reviews

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.

We use Microsoft Azure Cosmos DB mostly for maintaining our user databases in the document databases. Our firm has an app where each individual user's home screen is personalized according to their preferences, web searches, and metadata. For this, we maintain tags, and those tags, associated with each individual customer's mobile number, are stored in Microsoft Azure Cosmos DB to drive personalization and hyper-personalization on the app's home screen.

In terms of how Microsoft Azure Cosmos DB has improved my organization, the latency for API calls is pretty good.

The best features of Microsoft Azure Cosmos DB are the way it maintains the data in partitions and its retention policies. 

Their new feature, dynamic data masking, is very cool and useful for us. 

Since we are one of the largest NBFCs in India, maintaining customer data in a DB, we have to take care of their personal information related to their identity. Microsoft Azure Cosmos DB rolled out this feature about six months ago. Though it's not GA yet, we are running our POC on that feature where you can hit a particular API which Microsoft has provided. Whatever fields you define in JSON, when hitting the API, it will automatically mask all personally identifiable information for each customer in the database. If you have 100 million data points for customers, according to that JSON set of rules, it will automatically mask everything. This is really helpful in our business.

Regarding search capabilities through large amounts of data, Microsoft Azure Cosmos DB performs well when using combinations of primary keys. If we want to search through one of the instances or databases, it works efficiently. The performance largely depends on how optimized your SQL query is and how effectively you use the primary keys.

In Microsoft Azure Cosmos DB, I would suggest improvements in security. If anyone who has the endpoint and has access to infrastructure can potentially affect your DB. Instead of relying on a single endpoint for authentication, having multiple authentication factors would be beneficial. 

I have limited recent information about the use cases as I am about to leave my organization to pursue Masters in the US.

I have been using Microsoft Azure Cosmos DB for the past three years since I joined my organization. This is my first organization, and I have been using Microsoft Azure Cosmos DB throughout this period.

In terms of stability for Microsoft Azure Cosmos DB, we sometimes face problems, and I would rate it a seven out of ten. 

There were instances where the DB was not responding, and we lost some part of our business due to that. My impressions of the latency and availability are that most of the time it's up and running. 

Issues typically occur at month-end when the load is measured due to the nature of our business, as we have to process huge chunks of data during that time.

I would rate scalability for Microsoft Azure Cosmos DB as eight out of ten. It's straightforward to scale Microsoft Azure Cosmos DB. You just have to go over the portal and set your configurations for DB or different subscriptions. While scaling is easy, we were facing some stability issues.

Technical support for Microsoft Azure Cosmos DB is great. Recently for the dynamic data masking feature, Microsoft provided us with an entire support team of three or four people who were present in our office. For any doubts or issues, we could approach them directly, and they would raise tickets accordingly. I would rate the support as eight point five to nine out of ten.

Positive

Previously, we were using Postgres for a document DB before switching to Microsoft Azure Cosmos DB, which is also a document DB.

The deployment was good. I was responsible for a few parts of the installation. Microsoft Azure Cosmos DB requires maintenance. The infrastructure team sometimes switches the data center and geolocation. I cannot provide much information about this since I'm not on the infrastructure team. The DB maintenance is handled by their team.

The solution has reduced costs, although not dramatically. The cost reduction is between 6% to 10%. The actual figures in my organization don't flow to my level, but according to my seniors, they were able to save between 6% to 10% after two years of optimization, not year-on-year.

I would recommend Microsoft Azure Cosmos DB to other users due to the latest dynamic data masking feature. If a company's infrastructure primarily uses Azure products, it makes sense to use Microsoft Azure Cosmos DB for better integration. However, if your business requires a very stable DB, you should explore other options as we faced some stability issues.

On a scale of one to ten, I rate Microsoft Azure Cosmos DB a seven overall.

We use Microsoft Azure Cosmos DB for one workload, which is a recent implementation. Microsoft Azure Cosmos DB has two use cases: storing unstructured documents and serving as a vector DB for AI purposes. We utilize Microsoft Azure Cosmos DB as a vector DB. In our company, we implement RAG (Retrieval-Augmented Generation) implementation of AI. In RAG implementation, we must chunk and store vectors in databases, so for our workload with RAG, we use Microsoft Azure Cosmos DB as a vector DB to store chunks. We have used the vector database with Azure AI services in our RAG applications.

Microsoft Azure Cosmos DB is not complicated to use; it's very user-friendly. The engine is approachable, and it's straightforward to implement, manage, and migrate. Microsoft Azure Cosmos DB improves reliability through replication in many regions in Azure. MongoDB, for comparison, is more complicated for replicating all clusters worldwide.

Microsoft Azure Cosmos DB is simple in many tasks, and for application performance, we can set many models for scaling, autoscaling, and pay-as-you-go models. We are more efficient with these features. The best feature of Microsoft Azure Cosmos DB is the replication all over the world. The second notable feature is the simplicity of deployment.

Microsoft Azure Cosmos DB is a good general-purpose database with low cost. For our little workload, it's very interesting to work with Microsoft Azure Cosmos DB. The ability to deploy a world-replicated Microsoft Azure Cosmos DB is very efficient in terms of cost.

After three months of use, while it's difficult to specify improvements, the RU (Request Units) models could be enhanced. The model with autoscaling for RU is complicated to optimize RU consumption. Currently, we have some difficulties understanding the RU consumptions with our workload. For instance, we consume 200 RU, but the database is not big.

In RU models with autoscaling, we want to have more precision in the consumption of RU. The dashboard could include more detailed RU descriptions, IOPS, and compute metrics. Having more detailed metrics of RU consumption would be beneficial.

We have been using Microsoft Azure Cosmos DB for three months in deployment.

Microsoft Azure Cosmos DB has good scalability. We use autoscaled models which work well for our needs. The system allows setting a range of RU, starting RU, and max RU, with autoscale triggering between these two values. The scalability functionality is satisfactory for Microsoft Azure Cosmos DB.

Technical support in Microsoft Azure Cosmos DB is good. We haven't opened many tickets in support, except one ticket for migration of Microsoft Azure Cosmos DB to another subscription. We experienced some problems with slow transfer during this process. The support provided for this issue was helpful.

I used MongoDB before Microsoft Azure Cosmos DB.

The switch to Microsoft Azure Cosmos DB was simple using the MongoDB plugin.

Microsoft Azure Cosmos DB is a good general-purpose database with low cost. For our little workload, monthly consumption costs about 20 to 25 dollars. For a big initiative, it costs approximately 300 dollars. In comparison, MongoDB deployed in Azure has an initial price of 100 dollars just for support, before computing and storage costs. Microsoft Azure Cosmos DB provides excellent value, particularly for smaller workloads.

Microsoft Azure Cosmos DB can be used to store unstructured documents and serves as a vector DB for AI purposes. The onboarding process took approximately three months with the plugin, which is relatively quick. While all our workloads originally worked with MongoDB, Microsoft Azure Cosmos DB's MongoDB plugin driver makes the transition transparent for developers. This results in a short onboarding time in this context. 

On a scale of 1-10, this solution receives a rating of 8.

We built a chatbot for one of the clients, which is a trading-related chatbot that assists with trading queries. We use Microsoft Azure Cosmos DB to store customer interactions.

It performs very well, especially under high load where it automatically scales up the RUs. The main advantage of Microsoft Azure Cosmos DB is its low latency, with response times in milliseconds, making it great for chatbots.

I am a part of the NLP team. We primarily use Azure Cosmos DB and MongoDB, specifically leveraging their NoSQL capabilities for storing unstructured data, such as JSON formats. This is crucial since we need to access data in milliseconds and handle all operations, including storing, writing, and reading. The client's expectations for response time are quite high. Large Language Models (LLMs) can be time-consuming, so we aim to minimize latency. This serverless architecture helps us significantly in achieving shorter response times compared to other services.

The best features of Microsoft Azure Cosmos DB include its support for asset properties, which makes it more secure and easy to understand. The auto-increment feature is particularly good, and overall, it remains user-friendly and supports multiple SDKs effectively.

Microsoft Azure Cosmos DB demonstrates good capabilities for searching through large amounts of data. It is easy to maintain, offers good quality, and is straightforward to understand and deploy. Its scalability is excellent, supporting all asset properties. 

Microsoft Azure Cosmos DB is quite easy to use. It offers five to six types of inference options such as Python and NodeJS, or REST APIs, and we primarily used Python. Python was very helpful because it is easy to read, easy to understand, easy to implement, and easy to maintain.

The initial deployment of Microsoft Azure Cosmos DB, particularly involving managed identity and service principal, is quite challenging due to insufficient documentation. However, deploying via key access works and is easier. 

I expect improvements in Microsoft Azure Cosmos DB, particularly in the vector database area. In comparison to Databricks, there’s a functionality in Databricks that allows direct updates of the data structure. For instance, we have a use case to display live dashboards to customers where a chatbot is used by around 200 end-user customers concurrently. When wanting to display the queries and responses, along with evaluation metrics, all this information is stored in Microsoft Azure Cosmos DB. However, to show this in real time, we need a live connection that automatically updates in response to new records being inserted. This automated updating feature is lacking in Microsoft Azure Cosmos DB compared to Databricks.

There’s a lack of proper documentation regarding service principals and managed identities, which necessitated referring to various resources to implement these features, making it challenging.

I have been using Microsoft Azure Cosmos DB for the last two years.

I would rate the stability of Microsoft Azure Cosmos DB as eight out of ten. 

I would rate the scalability of Microsoft Azure Cosmos DB as very high, around nine out of ten. One of its best features is the auto-scaling ability, which has no delay. It automatically scales RUs in milliseconds, ensuring a robust security level with Entra ID, managed accesses, and service principals that help with internal products. Utilizing all Azure services with managed access keys provides added security against data leakage, alleviating concerns about key exposure.

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

Positive

The initial deployment of Microsoft Azure Cosmos DB, particularly involving managed identity and service, was quite challenging due to insufficient documentation. However, deploying via key access works and is easier.

Onboarding with Microsoft Azure Cosmos DB is straightforward, as the learning process is simple to understand due to comprehensive documentation.

Maintenance is required when using key-based services in Microsoft Azure Cosmos DB. If we use service principals, the maintenance needs are reduced, but some overall maintenance is still necessary. Regular checks are optional for system status, and we prefer to keep resources minimal.

Only one person was required for the deployment of Microsoft Azure Cosmos DB.

In terms of budget, Microsoft Azure Cosmos DB offers multiple selection options from Azure, allowing us to choose services such as auto-scale and select how many RUs we need at any time. We typically recommend going with auto-scale, as it automatically increases RUs during high load and decreases them during low load. 

The pricing is quite competitive, providing some free GBs to practice before implementation, alongside a good backup system. The backup duration is around 90 days, differing based on the tier we select. 

I am not entirely sure about the exact costs. Before presenting the solution to our client, we compared RU costs on Azure and AWS. The client specifically required a secure platform, prompting us to choose service principals, meaning we commit to all Azure services such as Azure OpenAI, Azure Cosmos DB, Azure Applications, Azure Monitor, Azure App Services, and Azure Kubernetes. Overall, we find the storage costs comparatively high. Comparatively, AWS offers similar features, but at a slightly lower cost, and I would rate their support a seven.

We have not used the AWS marketplace for purchasing; we exclusively go with Azure. Upon comparing costs between AWS Cosmos DB and Azure Cosmos DB, the pricing in Azure tends to be higher.

Vector database capabilities exist in Microsoft Azure Cosmos DB. They allow us to store vectors in NoSQL or other containers such as tables, although we only utilize a few features. While it is possible to store vectors, we can’t search for them directly since vectors have multiple dimensions; thus, we can only store them.

Microsoft Azure Cosmos DB has helped improve our search result quality, although there is no perfect searching technique here. We need to utilize SQL queries, which are not standard SQL queries, requiring us to create specific SQL queries after reviewing the documentation. This includes reading samples provided in the documentation to access the data effectively.

I would recommend Microsoft Azure Cosmos DB to others who seek solid security and wish to protect their data without exposing keys and values. API-based authentication mitigates risks associated with lost keys. Using service principals and managed identities is advisable for enhanced security. However, if the effort to manage keys is unappealing, I suggest exploring other platforms that may not offer the same level of data security. 

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

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.

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.

I have been working with Microsoft Azure Cosmos DB for approximately two years across seven projects. I started by creating basic containers at the free tier level that Azure provides, and when requirements grew, I moved them to the paid version. Azure typically provides two containers for free, after which you need to upgrade to the paid version. Of the seven projects, five were small projects and two were mid-sized projects. We created approximately 20-30 containers in total.

It works similarly to MongoDB when using NoSQL. When deploying on Azure, the communication is rather easy without many steps and complications, though this is more a benefit of Azure rather than Microsoft Azure Cosmos DB specifically. The queries take a similar amount of time compared to other databases, but the database management system provides a better-looking UI for viewing data compared to other solutions. 

The queries in Microsoft Azure Cosmos DB are faster when trying to fetch specific fields from JSON or run particular queries on a container.

The main downside I have faced was with hierarchical partitioning in Microsoft Azure Cosmos DB. When using the second partition within hierarchical partitioning, I encountered issues while fetching queries. Though it retrieves values, the performance is not optimal when using partitioning. For example, when dealing with users and different categories of users in hierarchical partitioning, the query results were not providing all the desired results. The documentation regarding partitioning keys was limited, and despite contacting support, the problem remained unresolved. Additional documentation on this feature would be beneficial.

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

Lag only occurs when different resources are set up at different locations. When all resources are at the same point, there is no lag. In production, we have experienced minimal issues. The project has been live for two years without any database problems. We decreased the timeout for connections and queries to two seconds, and it works efficiently. Our project remains at mid-scale without requiring a load balancer at the Microsoft Azure Cosmos DB level.

Given the RU size, it can scale up significantly. If the user base increases, increasing RUs to handle more database calls is not problematic. Azure has done an excellent job making everything more scalable, including Microsoft Azure Cosmos DB. The plans allow for easy upgrades based on user growth, supporting parallel queries and increased call volumes. You can move to a better plan if you are going to have more users.

The response time you experience mostly depends on the specific service you are using. For instance, For Azure AD-related issues, their support was quite fast and effective. However, with Azure AI speech service, I faced some challenges. They didn't provide a precise answer to my issue, but they did share some documents for reference. It took about 48 hours for them to respond, and even then, the solution they offered wasn't exactly what I needed.

On the other hand, my experience with Azure Cosmos DB was more positive. I had a specific inquiry regarding partitioning, and their support team was helpful. One representative reached out to me, and we discussed the issue. They were able to guide me in implementing different hierarchical IDs to structure the data better. Our goal was to optimize our queries and reduce API calls. Overall, their response took around seven to eight hours, and I felt they effectively resolved my concerns.

Overall, I would rate their support an eight out of ten. Specifically, if I focus on .NET, I find their support to be excellent. However, for other services, such as Azure reports, there are still issues that prevent me from giving a higher score, so in those cases, I would rate it a seven.

Positive

I previously used MongoDB. The choice depends on requirements. Microsoft Azure Cosmos DB is optimal for simpler structures with fewer containers and less complex data partitioning, offering faster queries. For more complex database structures, MongoDB might be preferable. However, when using other Azure services and hosting on Azure, Microsoft Azure Cosmos DB works best. The choice ultimately depends on the entire application architecture and hosting environment.

The onboarding process and learning to run queries is straightforward. It requires only a connection string for the database and container name to run queries. Migration from SQL to NoSQL is relatively simple due to easy syntax and connection process. The initial setup took approximately 5 minutes using the emulator on the local machine, and the Azure subscription setup required only about a minute. An API was used to create all containers efficiently.

The pricing is calculated per query with specific calculations, though I cannot provide detailed information about this aspect.

I have worked with Azure AI services including translation, transcription, and speech synthesis. We used Microsoft Azure Cosmos DB for storing links to storage accounts for AI-generated data, utilizing NoSQL queries for data retrieval. Azure AI services can be somewhat challenging to integrate, in my opinion. I find that integrating Microsoft APIs is generally harder compared to others. In this case, we weren't using the Vector DB explicitly; instead, we utilized Microsoft Azure Cosmos DB. We relied on standard containers, and I believe we had about seven or eight containers in total.

We generated our data using AI services and then stored it in these containers. The links to specific storage accounts for each request were saved in Cosmos DB. When we needed to retrieve that information, we used queries to fetch the data.

In a banking project, we used the Vector DB capabilities of Microsoft Azure Cosmos DB, though limitations were due to our API standards rather than database limitations. The team later discovered and implemented the inbuilt vector DB features when the database grew.

Overall, I would rate it a nine out of ten with the only significant issue being the partitioning key functionality. It's a good alternative to other NoSQL databases.

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.

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.