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.

Azure Cosmos DB is our database of choice for new applications and cloud-native applications. I use it anywhere.

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.

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.

Our primary use case for Microsoft Azure Cosmos DB is as a solution for customers who are not necessarily migrating an existing application but are looking to build something more cloud-ready and scalable. The objective is to provide a scalable and flexible database solution that does not require the compatibility requirements of Azure SQL, allowing for fast data access across regions without latency concerns. They are not looking for all the compatibility requirements for Azure SQL, but they are looking for something that they can scale quickly without latency.

We use Microsoft Azure Cosmos DB for a lot of facets and various production-based products. In one case, we use it to store news articles and process information about them for AI processing. We also use Microsoft Azure Cosmos DB to store conversations with AI chatbots and for managing data pipelines and orchestration. These are just a few of our use cases.

We use Microsoft Azure Cosmos DB as a NoSQL database to store JSON documents for our clients in the Banking, Financial Services, and Insurance sectors, primarily insurance. They require storage for numerous documents, including policy, claims, and costing documents, making Cosmos DB the ideal solution.

Because the company is spread across multiple regions, maintaining consistency with traditional relational databases was a challenge. Cosmos DB solved this by offering various consistency options and geo-replication capabilities. Logical partitioning within Cosmos DB improved routing efficiency, and composite indexes, combined with the partition key, optimized query execution by directing requests to specific documents, minimizing resource consumption.

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.

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

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

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. 

My company developed an anti-money laundering compliance platform specifically for the gaming industry. This multitenant platform utilizes Microsoft Azure Cosmos DB as its core operational database.

We have a high-throughput, large-volume data ingestion process, with substantial data flowing into our environment. We needed to solve two problems: ultra-high volume, requiring speedy reads and writes of hundreds of gigabytes of data per day, and the ability to distribute our platform geographically. Azure Cosmos DB's geo-replication features and the ability to host and scale our database across multiple regions, keeping data close to our customers, were primary deciding factors.

I have three different products using Azure Cosmos DB. The most extensive use is in a survey platform we are developing as a SaaS product. Azure Cosmos DB serves as the primary OLTP database for this platform. We do not have any other RDBMS for this use case.

We also use it to store configuration information for campaigns for the other two products, but it is used extensively for the survey platform.

The primary use case is focused on advanced analytics. We use Microsoft Azure Cosmos DB for storing results from OpenAI models, acting as a temporary information repository for APIs, and for log systems. We also experiment with its Vector Search capability.

In our project, I used Microsoft Azure Cosmos DB primarily for storing new or updated JSON documents.

We have been using Cosmos DB for everything involving non-relational data. Recently, we’ve been utilizing it more for AI purposes, especially for conversation histories.

Our primary use case for Cosmos is the storage of shell-fed signs and our pricing systems. We use it as a transactional database on the back end.

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

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

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

I'm the primary systems architect at DocuSign. We just launched a product at called Intelligent Agreement Management, and a central pillar of that is schema understanding. We use Microsoft Azure Cosmos DB as our schema store. It's the brains of our entire system. 

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

We use Cosmos DB as a database for the cache mechanism. We have a product integrating e-commerce platforms with backend ERPs, pulling merchandising data. We maintain millions of products in the ERP and store them in Cosmos DB in document format. When a query comes from the e-commerce platform, it goes directly to Cosmos.  

As the technical lead of the Microsoft Azure Cosmos DB team in my previous company, I helped our customers. We had a team of around 20 people. We addressed any issues our customers faced when using Microsoft Azure Cosmos DB or related services. Once resolved, I worked directly with our operation manager to engage with customers, checked their user experience, gathered feedback, and made improvements. This work was primarily managed by a manager who collects feedback and monitors KPIs to improve our service.

Our primary use case for Azure Cosmos DB is mainly as a Document DB and vector DB.

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

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

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

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

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

Our primary use case for Azure Cosmos DB is storing information for our large accounting application, which integrates several sites on SharePoint Online. We use event programming to store all calls in Cosmos DB, so we can redo them and have them persist in the database.

We primarily use Microsoft Azure Cosmos DB as a transactional data store and for some event-driven applications. We utilize the change feed, and the function app triggers quite a bit. MPerks, our customer loyalty application, uses it. It has become our go-to database, and we hardly touch SQL Server for new stuff.

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

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

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

Our primary use case is mirroring the data for reporting.

Our current use cases for Microsoft Azure Cosmos DB include IoT-based applications such as home automation, conferencing, and industrial automation, utilizing devices like microphones and speakers.

We use it for our internal operations, including order history and other things related to e-commerce.

We do not use the built-in vector database capabilities since they are driven by another team in our organization. We just access through the API.

Our corporate mission is to help companies achieve more with their data, which often means unifying your data. We have a SaaS solution and have built a Copilot with Copilot Studio on top, as well as some of the Azure AI services, which is now Foundry. We are starting to use it to allow people to use natural language to ask questions of their data. We are early in our journey, but I suspect it will work well for us.

We use Microsoft Azure Cosmos DB to store document-type data, graph data, and key-value type data. It is a globally distributed database, which we mainly utilize to store document-type JSON data. 

In my project, I work with core SQL-type queries. Using the API, we are storing JSON data in Microsoft Azure Cosmos DB with a database and container-level architecture. This involves storing items using a partition key for optimized query performance.

We get data from BLOB storage. After some processing, we are storing it in the JSON format in Microsoft Azure Cosmos DB.

We are using Cosmos DB in several different ways. We receive unstructured and semi-structured documents from partners, and we use Cosmos DB to push the data in and scale it to kick off internal processes. 

We receive notifications from our customers to take action quickly regarding the energy grid. Cosmos DB is also used in a different project for our settlement system, where it is used as a queuing engine for the change notification portion.

We have numerous healthcare AI use cases, including utilization management, documentation, letter generation, and voice call creation. These are both real-time and non-real-time use cases. My team is the platform team that enables the services. The ML teams are the practitioners who work on these products. 

I am using it to store our data. We are using Azure Cosmos DB to store our JSON-based documents.

We use it for different companies and different clients. We have Fortune 500, startups, and mid-sized companies as our clients. They are in healthcare, finance, fintech, tech, manufacturing, construction, real estate, telecom, and a lot of other industries. They all love it.

We are basically a system integrator, so we use Microsoft Azure Cosmos DB in multiple projects for different things, often when migrating from other hyperscalers. We do many AWS to Azure migrations. It's our go-to solution, given its flexibility on the SQL driver and the MongoDB driver. When running a NoSQL database, it's our preferred choice. Recently, with the AI wave, we've been using it as our backing store for many things, from vectors to structured or somewhat structured data. 

We develop SaaS applications for our products and external clients, utilizing Cosmos DB as the data storage layer for all semi-structured data.

We chose Cosmos DB because it addressed our need to store and query data with varying structures. While object storage is cost-effective for large datasets, it lacks querying capabilities. Traditional relational databases like SQL Server or Oracle are expensive and inflexible, posing challenges for our dynamic data models and frequent changes. Cosmos DB provided a solution with its dynamic data model and efficient querying capabilities, allowing us to accommodate diverse customer needs and evolving data structures.

We use Microsoft Azure Cosmos DB emulator to display database contents and occasionally perform manual data edits when necessary. We utilize it for general database emulation tasks.

We mainly use it as the database for our platform, which is an application that users use as an interface for their IoT products. I work in the IoT chapter, and we developed an application where customers can manage their IoT devices and have a holistic view of their deployment. All data is aggregated in our database, cleaned up with ETLs, and stored in Cosmos DB.

When dealing with IoT products, we encounter massive amounts of data, unlike in commerce, where traffic and data fluctuate. IoT devices, especially ours, generate constant data streams every five minutes, necessitating robust handling. We chose Azure Cosmos DB, specifically the PostgreSQL version, for its ability to store massive amounts of data without performance degradation, thanks to its columnar storage feature. This allows us to compress older data, such as telemetry data older than two years, which is crucial for managing the ever-growing volume of information. Even with compression, we maintain fast access to the data, ensuring optimal application performance.

We utilize the solution for big data, which is collected from IoT devices and streamed through a number of Azure services. The data is then landed in the Cosmos database for analysis later.

We use Cosmos DB in a multifaceted manner, primarily as an operational data store. Additionally, it serves as an analytics and reporting synchronization platform through Synapse Link, Azure's connective data warehousing solution. By connecting directly to Cosmos using the change feed, we project data into our data warehouse and data lake, facilitating both operational functions and analytical reporting needs.

We use it primarily to log all events for a particular user and product. A particular users are logged in to see if a product has been modified. If someone modifies the data, we log that information along with the email. This helps when we need to compare modifications to a product.

We use Azure Cosmos DB as our data storage or database technology platform, and we use it as the backing storage of our metering and billing back office system.

We have an energy metering and billing solution, a SaaS billing solution, which is responsible for the whole back office for district heating and cooling suppliers. Our platform is responsible for the ingestion of time series data, and at the end of the processes, we generate invoices, which are sent out to customers. On top of that, we provide a consumer portal where consumers can view their energy usage and consult their bills. 

They are two separate products, and both are using Azure Cosmos DB. The B2C or the consumer portal is using Azure Cosmos DB serverless because of its very spiky nature. It is very unpredictable how many users will be using the B2C portal, and the back office application is using Azure Cosmos DB with provisioned throughput with auto-scale configured, which makes it very scalable and still cost-effective.

We use it as our main database for our network access control software, and we use it to store all of the information we need to authenticate different devices and users to the networks of our customers. It maintains all of the necessary data for our SaaS product.

I find SQL API suitable. I used it in my last project. Previously, I worked for a client called EPS, which has a product called BOS (brokerage operation support system). There I have used the SQL API.

I have used it in a product called BOS, and we achieved many things with Microsoft Azure Cosmos DB, which helped improve our products efficiently.

I was using Microsoft Azure Cosmos DB to store unstructured data.

I have been using Microsoft Azure Cosmos DB for the last five years for IoT-based data saving and other purposes. We use non-structural data for various reasons. For instance, we are using artificial intelligence to save multiple data sets coming from different sources.

We use Cosmos DB as our primary data store for all the different software services we offer.

I develop applications. I developed an application where I had to search the Azure Cosmos DB database for values related to suspicious entities. It involved retrieving, sorting, and manually searching data through queries.

We use it as a data storage platform for several proprietary applications that we have designed and built and now support. We generally use it to be able to scale so that our customers can search a sizable amount of data. We have millions of records that include an extensive amount of text.

It has not been a direct approach for me because all of my enterprise-level applications are deployed in MongoDB. At some point, we usually face issues where we need multi-directional and different contexts to connect with the database. Sometimes we use SQL and need to retrieve data from the database. If using a typical MongoDB, this is not possible. Microsoft Azure Cosmos DB has bidirectional support for cross-platform connections, so we don't need to recreate our entire database structure in our application. We can work with the MongoDB driver and interact with Microsoft Azure Cosmos DB. The applications under my portfolio currently rely on that, mostly indirectly. We created the models, deployed our data, migrated it, and are using it heavily in Microsoft Azure Cosmos DB. 

Recently, we are building an AI-powered application where we heavily rely on Microsoft Azure Cosmos DB to bring data from ServiceNow, SAP, Salesforce, Cisco, and other customers we have at our organization. Reading and inserting data into Microsoft Azure Cosmos DB is a very smooth process.

We use Microsoft Azure Cosmos DB in our loyalty platform, which is based on our proprietary technology, Synapse LX. In loyalty, we need to enroll, score, and deliver rewards communications in near real-time. There are significant volume spikes in those activities, so our use case is to support the writing of information into our database. Cosmos DB is a no-SQL database that allows us to scale quickly and handle large volume spikes. It allows us to auto or manually scale in many different ways. It gives us much flexibility to handle that requirement and ensure we deliver the right customer experiences.

Our primary use case for Cosmos DB is unstructured data. We utilize it to spin up databases quickly.

I am building an extension app for DocuSign. One of the ways for me to demonstrate this is by using a third-party database. I read and write data from Cosmos DB using DocuSign tools.

In my role, I use Microsoft Azure Cosmos DB fairly extensively across various platforms. At ASOS, we utilize it for order processing to record incoming orders and for commercial integration platforms. Overall, we have numerous use cases.

We use Cosmos DB to store the concept of data and how it is entered by the user.

The main use case for Microsoft Azure Cosmos DB is as a key-value store where we store all the user data that we have and perform lookups. We use it at a significant scale, with storage of unique data reaching 12 terabytes and handling up to 3 million requests per second.

I worked on an entity data client project that handled multiple users from digital products, like a Stream player similar to Netflix. It is called Play Plus, with a user base of seven million and a website that attracts eighty-two million unique users per month. We store the data from these users in Cosmos DB to ensure a fast connection between the user profile and the advertising system.

I like to describe it as a programmer's database. .NET developers, in particular, can design and work with the data easily because it's schema-free. Unlike traditional databases, which are considered rigid with their rules, developers really love Cosmos DB because of its schema-free nature and the freedom it offers.

Cosmos is widely used for web applications. You can also use it for inventory management and IoT solutions... there are a ton of different applications.

In our setup, we rely on Azure Cosmos DB primarily for cloud-native applications that demand global scalability. We use it for connecting web apps and implementing search functionalities.

This is an event-driven solution. Most oil and gas companies have folder source systems, where they cannot scale, but they still want to provide real-time data to their end consumers for various analytical use cases and AI/ML processing; this is where we input raw data into the Azure environment of this solution. Then, eventually, we built the API on top of Microsoft Azure Cosmos DB because it's highly scalable. The solution is a little bit expensive, but the businesses are ready to accept it. 

We use Cosmos DB as our entire storage database solution for our application. We don't use any other relational database. We have a file that we use for configuration, but we use Cosmos for user data.

We have about 100,000 users a week who visit our website. We have plans to increase usage to four times what we're using now.

I used to work for a bank in Turkey and used Microsoft Azure Cosmos DB in the bank for reporting. We used the solution for customer relationship management (CRM) and cost management.

Our current project primarily relies on the file system to handle incoming source tests. Within this setup, we capture both metadata and result data from these tests. We extract metadata information from these files and store it in Azure Cosmos DB and we have several software services in place to facilitate this process.

I work for a retail company that uses Cosmos DB internally for access management. You have a graph with a hierarchal model that goes from owner to manager to assistant manager to employee, etc., and you provide access based on this hierarchy. Our workshop manager uses Cosmos DB to track requests for access and who needs to approve them. 

Employees who want to access specific resources will submit a request, and the application owners will approve it. Within the applications, there are often multiple levels of access. So the owner of those processes or files must authorize access. We have nearly 500 users. The security and access management teams mostly use Cosmos DB. 

The company is considering a switch, but that might take many years. Many others have switched and will continue to switch to other solutions. However, after you've invested a couple of years into it, it becomes more challenging because you need to rewrite many things. 

We mainly use it for products that are based on graph concepts. We are using it for mobile applications and real-time analytics.

I use Microsoft Azure Cosmos DB for data engineering.

Cosmos DB has multiple use cases. For instance, we recently developed a custom application for a customer in India. We used Cosmos DB to store data fetched from the initial front end to reduce access times to the application, which is significant for improving user experience. 

For example, when creating a virtual machine through our custom portal, it is essential to check whether a VM with the same name exists in the same or a different subscription. 

Additionally, we needed to enforce naming conventions and limitations on the number of VMs that can be created within the same network. These conditional parameters were managed using Cosmos DB, allowing the initial provisioning process to validate data and configurations instantly. 

This enables us to inform the user right away if there is a duplication or if the creation adheres to predefined rules, offering suggestions based on the UI. This demonstrates the real-time application and benefits of Cosmos DB.

We use Cosmos DB for its key-value storage capabilities. For structured data, we always use SQL Database.

We handle JSON data and it is compatible with Microsoft Azure Cosmos DB. 

Our company uses the solution to develop a certain sort of products for our internal companies. We have some child or franchise companies and are developing software for them. 

We use the solution where transactions display to provide views or reports for the console. We also use the solution for an online learning application or portal. 

We have 20,000 to 30,000 users across multiple products, franchise companies, and customers at the backend. Centralized data is global and accessed from all over the world including India, the US, South America, and Asia. 

We have several new projects with the same backend, so our user volume will definitely increase day by day. 

We are streaming some data from Azure Stream Analytics, which will be stored in Microsoft Azure Cosmos DB. Our application will be taken from Microsoft Azure Cosmos DB.

At the end of the day, Cosmos DB is a database. It is a wrapper over different APIs.

We use Cosmos DB both internally and with our customers. Our internal use is quite extensive. The usage with our customers depends on whether it is an approved technology within their ecosystem.

Because Cosmos DB uses multiple APIs, it is the go-to database for us internally.

We have a massive quantity of data that we need to maintain, and we can't put it in a relational database since we need all of the data and want it to be queried quickly.

We maintain it in non-relational databases such as Microsoft Azure Cosmos DB.

We normally use Cosmos DB for data storage.

Microsoft Azure Cosmos DB can be used for various purposes. The query language used for Cosmos DB is very similar to SQL, which gives it an advantage. It's a globally distributed multi-model database service, meaning it supports multiple data models, including documents, key-value pairs, graphs, and time series data models.

It's highly scalable and supports consistency, security, and multiple security options, such as REST and transit encryption. It also provides automatic support for these options. These are some top-level benefits of using Cosmos DB, making it a highly versatile and useful tool.

We use different Azure services in the development of our solutions. Some of the services we use are Azure Cognitive Services, ADB2C, and SignalR.

For most of the solutions, we use a mixture of Azure SQL Database and Cosmos DB. We use Cosmos DB when the data size is huge, and we need to scale.

Currently, only two people in my organization are working on this solution: one developer and myself. In the future, I think once the system gets deployed, we will have thousands of users. 

We mostly use it for NoSQL use cases. We use it for web applications, mobile applications, and social applications in the financial sector.

It is deployed on-premises and on the cloud, and we are using its latest version but not the one in the public review.

We use the product to input data that doesn't require structuring. 

We provide automated solutions to manufacturing plants. 

I use Microsoft Azure Cosmos DB for some web applications.

I use Cosmos DB for geolocation identification.

If you pass by a market on the street, it sends me your location. My application will then send you a personalized notification about relevant products you can buy at the market.

We use Microsoft Azure Cosmos DB for faster databases.

Primarily, people do not have a clear understanding of the cloud and cloud services. Customers are a little bit scared about taking their data onto the cloud, and they think and they assume that it is not safe. So we just make them understand that databases or services on the cloud are more secure than on-premises infrastructure.

The company is using Microsoft Azure Cosmos DB for business intelligence information, specifically for demand management.

My current title is that of a Solution Sales Architect.

We have a database stored on Microsoft Azure where we fetch records and validate them against the application data that is displayed. We use it as a backend in an application to store data.

Within our organization, there are around 500 people using this solution.

I use the product for storing information related to our automation.

Cosmos DB is a non-relational, NoSQL database. We are a solution provider and we implement this product for our clients. It is used for integrating and managing unstructured data such as videos, pictures, and other big objects that you cannot include in a standard database tablet. It is made for these kinds of activities.

Some of our customers include banks, where together with their main relational database, it provides a place for keeping track of unstructured data. The relational database is used to store the regular data, whereas Cosmos DB stores what is unstructured.

I also use this product for my own purposes.

We are strict users of Microsoft. We use it to deal with huge amounts of data. It's like Databricks and it's structured. All of the sites we have are stored in Cosmos DB.

We have a lot of use cases like for the web API backend where we have most of the processing jobs. It supports mobile, browser, and tablet. We have a single face application that interacts with the users and customers. It's backend and frontend architecture. For middleware, we use Kafka integration. We have PowerBI for reporting. 

In our accounting department, we store data in Azure Cosmos DB. We query and store IO-based data there.

I was using Microsoft Azure Cosmos DB to correct some performance issues or see more about my environment.

Our primary use case for this solution is to call confirmation details booked in Cosmos DB.

We use Microsoft Azure Cosmos DB for storing information from third-party systems in JSON format.