Microsoft Azure Cosmos DB Reviews

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.

We find Microsoft Azure Cosmos DB easy to use. We are provided APIs for each and every write or edit access, even for read operations. We don't directly query the database. API-based access makes it easy.

Previously, we used to have maintenance or server issues. We don't have those issues anymore.

The best feature of Microsoft Azure Cosmos DB is API access, which makes it very easy to interact with the database without needing to write queries. It's also fast. As it's Microsoft-provisioned, the cloud is very accessible and reliable as well.

There is room for improvement in Microsoft's maintenance aspect. For example, we had a major incident at the end of December where the entire South Central region was down for our application, causing many problems due to a lack of access to the database. It led to missing data in some systems. They can implement a better backup system or alert system on Microsoft's end. We do receive notices for regular maintenance or updates, but sudden issues create significant problems.

I've been using Microsoft Azure Cosmos DB for more than one year.

In the past year, I have only been using Microsoft Azure Cosmos DB for a year, and previously we encountered Microsoft issues such as maintenance or server problems, but these days we are not observing that as much.

For stability and impressions of latency and availability, I would rate it an eight or nine; we have not seen significant issues recently.

I rate scalability as pretty good. Because it's in the cloud, scaling is easy.

We are a very large organization. It is hard to know how many teams use Microsoft Azure Cosmos DB or still rely on the older systems. I am in India, and our team uses Microsoft Azure Cosmos DB, and I believe teams in the U.S. use it as well.

I would rate the technical support a nine out of ten.

Positive

Before using Microsoft Azure Cosmos DB, we had a different database system in place. The main factor for switching was cost-related. It was a leadership decision, and as a fresher, I wasn't involved in these discussions.

We were not part of the deployment. We were involved in migration activities, but I'm not very sure about the deployment experience. We aren't seeing any major issues now.

Maintenance of Microsoft Azure Cosmos DB is ongoing. There is a Cosmos DB team in our organization conducting maintenance, though not very frequently.

I'm not aware of the exact costs. We received one report a long time ago regarding savings after we started using Microsoft Azure Cosmos DB, but I don't remember the details. It seems to have helped significantly. We were using a different database system previously, and one of the reasons for acquiring Microsoft Azure Cosmos DB was cost.

I definitely recommend Microsoft Azure Cosmos DB, although I'm still learning. It's been just two years, but I've taken courses on Microsoft Azure. I recognize the advantages in scalability, availability, and cost factors, with maintenance issues being minimal as well. 

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

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

Because it is NoSQL, it has the capability to adapt to changes. As compared to Azure SQL or other SQL databases, Azure Cosmos DB is schema-less. We can add new columns anytime, and the application will not break. It is very efficient for application-facing scenarios.

The most valuable feature of Azure Cosmos DB is its scalability. That is the biggest reason I use Azure Cosmos DB.

I also like its developer-friendliness. It is very easy to begin with. Microsoft and Azure are good with that. With all the getting started information and all the introductions, it is very easy to begin with. Optimization is where it gets a bit trickier. That is where you need to be more active and understand why things are not performing as they used to. Most of the time, performance is not a problem. It is always fast. The problem is more around the cost consequence of that performance.

Its vector capabilities are new. They were implemented just months ago. There are probably three things that we were looking to address by using the vector database. The number one is the cost of Azure AI search and indexing. Before this feature came out in Azure Cosmos DB, the alternative for me was using AI search, which is way more expensive if using it as a vector database. Now with Azure Cosmos DB, that price point becomes much more accessible. That is number one. 

Number two is the developer familiarity aspect of things. AI search is more around enterprise use cases or enterprise search and requires more specialized skills to begin with, whereas Azure Cosmos DB is more or less a commoditized developer platform that is much more accessible to a wider developer audience. That is another aspect that it has addressed. For example, if I have a new starter in my team, it is easier to train that person on Azure Cosmos DB than the AI search. With the explosion of LLMs, AI agents, and things like that, the vector database on Azure Cosmos DB is a good place for developer onboarding. It is just way easier. 

The third part is still related to the developer experience, but in terms of the SDK aspect, the libraries available for Azure Cosmos DB are already well-established in the ecosystem. With the vector database capability, it is just a matter of adding an extension of those existing libraries. That means if the applications that are already using Azure Cosmos DB want to jump to the vector database, the jump would not be that big. It is just a matter of implementing it directly with their existing Azure Cosmos DB that they are already using.

We have used the vector database with Azure AI services. The other aspect is using the vector database with document intelligence. We use document intelligence to process a raw PDF document and things like that. From there, we convert that into embeddings, and then those embeddings will be stored in the vector database. It is something we use as a landing spot for new LLM applications.

The quality has improved because, traditionally, we did things in batches. We processed documents once a day or every twelve hours or so. With this new capability, we are very confident to run those processes in real-time. As new documents come in, the process and the workflow can get triggered. It is not a batch anymore. It is in real-time.

Azure Cosmos DB’s ability to search through large amounts of data is yet to be determined. Large is subjective at the moment. I have only tested it up to 2 gigabytes, and for that, it is working pretty well.

Azure Cosmos DB is our default. We do not question it anymore. After migrating out applications from an SQL database to Azure Cosmos DB, the change in the organization is massive. Especially on the application side of things, app developers are much more productive and lean. Previously, we had to go through a very rigorous process. To add new columns, tables, and other things, we had to work with DBAs. With Azure Cosmos DB, we can have a PoC and POV in weeks, sometimes days, instead of six months. That is how the whole NoSQL ecosystem changed our life cycle and productivity.

Azure Cosmos DB has changed our total cost of ownership for old applications, but not for new applications. For those who are still using SQL Servers or other databases, there was an added TCO because different projects are using different databases, whereas about 10 or 15 years ago, we had just Oracle, SQL Server, or IBM. For new applications, it is the default for us, so there is no change in TCO.

There are several areas for improvement. Firstly, having a local development emulator or simulator for Azure Cosmos DB would be beneficial. It would be very handy to have a Docker container that developers can use locally. Although, I know there is a free tier and so on and so forth, having a local environment would be nice. For example, SQL Server is very portable. You can even install it on your machine. That is the number one thing that is missing in Azure Cosmos DB.

The second improvement area is the IDE of choice. That means how you interact with Azure Cosmos DB. For example, with SQL Server, you have SQL Server Management Studio. I know there is a little bit of support for Azure Cosmos DB in Azure Data Studio, but it is not heavily advertised or it does not feel like first-class citizen support. Developer experience or developer tooling is missing in terms of interacting with the database. Better developer tools or an IDE for interacting with Azure Cosmos DB would enhance the developer experience.

Lastly, there is some mixed messaging about what Azure Cosmos DB is, given its multiple APIs. There are so many Azure Cosmos DB APIs available. There is NoSQL. There are MongoDB, Gremlin, and others. There is still some mixed messaging for others who are new to Azure Cosmos DB about what Azure Cosmos DB is. Is this like MongoDB, but then there is also MongoDB in Azure Cosmos DB? I know it well, and I know that the default one is just NoSQL, but others I have interacted with over the last ten years or so get confused.

I have been using Azure Cosmos DB for over a decade. I have been using it since it was announced.

The solution is very stable, and I cannot recall a time when Azure Cosmos DB let us down. I would rate it a ten out of ten for stability. I never had issues with it.

Its scalability deserves a ten out of ten. I have never hit a limit with Azure Cosmos DB.

We have multiple locations and multiple departments. We are in different countries and regions. For our one project, we have multiple Azure Cosmos DBs. We have about seven developers, and we have tens of thousands of users or consumers. Our clients are enterprises and SMCs.

Early on, about a decade ago, when I started with Azure Cosmos DB, I just played with it and created many things. I ended up having a $10,000 bill. Because it was an accident, I had to send a support ticket. The support team was able to waive that cost for me. That left a very good impression on me up until today. I did not have to pay that money, especially when I was just starting. Now, there are very good partners out there, us included, who are well familiar with Azure Cosmos DB. That ecosystem is well supported now. It is not like you are going to a niche database and hoping for the best. That ecosystem is quite mature.

I would rate customer service and support a nine out of ten. The only reason why it is not a ten is because a lot more triaging is required when raising a support ticket. That is the problem I have.

Positive

Before using Azure Cosmos DB, we primarily used MongoDB and Postgres. I have a mixed experience with both of them. There are also Azure flavors of those. You have MongoDB Atlas on Azure and you have Postgres on Azure. That is why sometimes I am very conflicted about which one to use. Both MongoDB and Postgres have captured the audience around the open-source community and the non-Microsoft enterprise or developer ecosystem.

The deployment is a one-off. It is straightforward. For provisioning Azure Cosmos DB, everything is there. It has been straightforward so far.

Its deployment is done in minutes. In terms of maintenance, Azure Cosmos DB itself does not have any maintenance. However, the application that we are supporting and developing needs maintenance. That is it. Azure Cosmos DB does not require migrations like SQL Server where you have to manage a migration from version 17 to 19 and so on.

We achieved a strong return on investment. Using Azure Cosmos DB enabled us to bring a project to the MVP stage in six weeks. With one recent application that we had, if we had gone through another approach, the project could have taken six months in an enterprise setting where everything is slow and challenging. Getting an MVP of that project would have taken six to eight months, but because we had an active choice of using Azure Cosmos DB and other related cloud-native services of Azure, we were able to get to an MVP stage in a matter of weeks, which is six weeks. That was a very measurable impact that we had. If we went another route, just defining the tables, entities, and other things would have taken us a big amount of time. We had already identified base entities. We knew we could add more columns or remove some columns as we went along. That gave the agility to our project.

We do not have to look at it periodically. I do not get support calls that our application is down.

From a startup point of view, it appears to be expensive. If I were to create my startup, it would not have the pricing appeal compared to the competition, such as Supabase. All those other databases are well-advertised by communities. I know there is a free tier with Azure Cosmos DB. It is just not well advertised.

For mid-tier customers, its pricing is justifiable. The enterprise tier is where it is subjective. For organizations that have built a lot of capabilities around SQL Server, Oracle, or so forth, because of the lack of skills, understanding, and capabilities around Azure Cosmos DB, it would appear to be expensive. The professional services aspect of Azure Cosmos DB is what is driving the cost, not the platform itself. The skills required to manage the service can drive up costs more than the platform itself.

I would recommend Azure Cosmos DB for its scalability and performance. Do not be frightened to give it a try. Because there is no local way of doing things, Azure Cosmos DB will always be considered expensive. It is not very developer-friendly when you have to pay upfront, but there is a free tier. Microsoft needs to do better in terms of communicating that it is free to get started.

I would rate Azure Cosmos DB an eight out of ten because of the lack of local development and so on. It also gets confusing with so many APIs. There is a mixed messaging problem around that. The vector database and so on are also confusing. There is a vector database, but depending on which API you choose, there is a different implementation. It is just a bit confusing. I use this every day, so I know it by heart. I know where it is going, but it is just not very easy to get started for others. Messaging and product categorization are not clear. The way they are bundled or packaged is confusing.

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.

Microsoft Azure Cosmos DB automatically indexes documents. By indexing every field in the document, it is easy to get fast performance to retrieve the records. While fetching, it fetches only specific fields required for further processing, which makes it efficient. Fetching all the fields from a document takes more time.

Storing data with a partition key makes data fetching easier and faster.

Microsoft Azure Cosmos DB helps in fetching data faster. There is a single-digit millisecond response to fetch those records.

Microsoft Azure Cosmos DB supports scalability. At a peak time, it will automatically scale the RUs. When there is less data, it will decrease them.

In Microsoft Azure Cosmos DB, one valuable feature is its ability to store data in multiple regions. If one region fails, it automatically switches to a healthy region, ensuring minimal latency and disaster recovery without impacting data latency in applications. It scales automatically based on query performance and peak traffic.

Microsoft Azure Cosmos DB is very easy to use.

Currently, I have no suggestions for enhancement or new implementations in Microsoft Azure Cosmos DB. However, the cost can sometimes be high, especially during cross-partition queries with large data amounts.

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

Microsoft Azure Cosmos DB provides high availability with 99.9% reliability. When we store documents in Microsoft Azure Cosmos DB, it stores them in multiple regions, not only at specific regions. If one region fails, it automatically switches to a healthy region. 

There is low latency. The partition key helps achieve low latency by ensuring data is stored and accessed efficiently.

Microsoft Azure Cosmos DB offers both automatic and manual scaling. The automatic scaling feature adjusts RUs based on peak demands, which helps manage workloads efficiently. The dynamic scaling feature has helped reduce overhead costs by automatically managing resource utilization.

Our application is being used globally, and we have ten members in our team.

When I joined the organization, Microsoft Azure Cosmos DB was already in use. I have not worked with other NoSQL databases before.

It is a Platform as a Service. It was already implemented before I joined.

I started working with it in the first month. I had the support of the senior developers of the time.

It does not require maintenance from our end.

We monitor the cost daily through Azure Monitor to evaluate how much it is costing for documents, thereby keeping track of the return on investment.

Microsoft Azure Cosmos DB pricing is based on RUs. Reading 1 KB document costs one RU, whereas writing one document costs five RUs. Pricing for querying depends on the complexity of the query. If you increase the document size, it will automatically increase the RU cost.

I would recommend this solution. For e-commerce applications, it is more beneficial because it can store semi-structured data. It is the best option if you want to get data quickly because it organizes the data in a good way. When a region fails, it automatically switches to a healthy region. It has backup storage, and it scales automatically based on the peak time or low time.

I would rate Microsoft Azure Cosmos DB an eight out of ten. It is a good solution, but the cost can increase with cross-partition queries due to data distribution.

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.

Microsoft Azure Cosmos DB is very easy to use once you understand the process, and we have a very good team. Because it is more costly compared to other services, the Microsoft product team takes customers very seriously. If any issue arises, they immediately join calls with customers to troubleshoot problems.

Microsoft Azure Cosmos DB has significantly improved the quality of search results, making searching easier compared to other services such as ADF, data factory, or SQL databases. Compared to AWS, Microsoft Azure Cosmos DB is user-friendly and offers robust features.

The Microsoft product team is proactive and engages with customers, helping to update features and resolve issues promptly, demonstrating a commitment to customer satisfaction. The learning curve for Microsoft Azure Cosmos DB is manageable, as it didn't take much time for me to grasp the basics. With the right information, even new users can learn the fundamentals in about two to three months.

For areas of improvement in Microsoft Azure Cosmos DB, the cost from the RU perspective needs attention. The cost structure differs for internal and external customers, causing frustration among some internal customers. Additionally, outside of SQL and Mongo APIs, there is limited support for the APIs. Developing new features compatible for customers beyond SQL and MongoDB would be beneficial, and reducing the overall cost would make it more accessible for startups.

I have been using Microsoft Azure Cosmos DB for more than 2.5 years.

The stability of Microsoft Azure Cosmos DB is generally good, though there are instances of outages. I would rate the stability at seven because there is room for improvement. 

The scalability of Microsoft Azure Cosmos DB rates at six. We have documented guidelines to help customers scale, but there are still some issues where customers struggle with scaling down after scaling up. It is straightforward, but some customers might need more guidance on using the Cosmos capacity calculator before scaling up. Customers should be able to scale down easily without needing detailed formulas.

In our organization, about 100 users specifically worked with Microsoft Azure Cosmos DB. This technology is utilized across almost every organization today, and Microsoft provides robust support that is taken very seriously. 

Our clients ranged from small to enterprise businesses, and we managed support requests from various types of customers, including premier customers who required extensive assistance.

The technical support of Microsoft Azure Cosmos DB deserves a rating of eight because I have experience with other services where assistance takes longer. In other services, there are multiple layers to check, but with Microsoft Azure Cosmos DB, we can directly reach out to the Microsoft product team members who are developers, and within a day or two, we can get on a call with the customer to help them with their issues and suggest best practices. This quick support is not seen in other services, where it can take five to ten days.

Positive

I observed many customers migrating their data from native MongoDB to Microsoft Azure Cosmos DB, indicating significant improvement.

Microsoft Azure Cosmos DB stands out in comparison to AWS, specifically with DynamoDB. Microsoft Azure Cosmos DB offers unique and cost-effective features that AWS does not. Additionally, it supports various configurations beyond just SQL or Mongo, such as the Table and Gremlin APIs, which many customers prefer.

The deployment of Microsoft Azure Cosmos DB is very easy. With the right approach, migration can be done smoothly and quickly.

I was using the built-in vector database when I was with the previous organization. There are vector search capabilities and other related features.

I recommend Microsoft Azure Cosmos DB to other users because it has significantly improved, especially concerning visible outage scenarios. The portal now provides clear workload choices for production and testing accounts, making it easier for customers to decide what they need. 

I would rate this solution a seven out of ten.

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. 

One of the scenarios in which we have used the MongoDB driver on Microsoft Azure Cosmos DB was an AI project with the NFL. It was called the NFL Combined Copilot, and we needed to ground data and provide real-time insights to scouts and coaches on the sidelines. It had to be fast and precise, with significant stakes involved. The experience was fantastic in terms of performance. One of the most critical aspects was that there was no room for error - it's five days in February with 350 athletes and 32 NFL teams present. It needs to work, scale, be precise, and bring the required results, or you must wait a year. This has been one of the places where we have pushed it to the limit regarding availability, scalability, and the whole concept of search and grounding for AI applications.

Using Microsoft Azure Cosmos DB and getting started with it is super straightforward. As you scale and adapt along the way, it remains fairly easy to work with. However, as the complexity increases, one challenge is that you need to be mindful of properly structuring your data for world-scale applications. Fortunately, there is plenty of guidance, documentation, and examples available to assist with this.

As a developer or development firm, one of the aspects we appreciate most is the ability to prototype effectively. We can take a project from the initial prototype stage to production-ready status without the need to redeploy the database or switch products. This approach allows us to use the same tools for both prototyping and scaling. It's important to note that you don't have to face a super complex scenario to benefit from this product. It is well-suited for prototyping and remains capable when transitioning to world-scale applications.

With the current AI wave, the built-in vector database capability of Microsoft Azure Cosmos DB for model grounding or the RAG pattern is crucial. Previously, we had to consider alternatives such as Pinecone and other third-party software, dealing with all the problems of designing, scaling, and maintaining the database. Microsoft Azure Cosmos DB enabling this feature allows us to get it out of the box with familiar tools and context, along with the benefits of its scalability and elasticity, providing excellent support for the highly relevant RAG pattern for AI search.

We have developed several AI scenarios, one of which was recently highlighted in Gartner research. This scenario involves discovering multimodal media within the context of sports, showcasing how organizations like the NBA and NFL use Azure to locate specific pieces of content through interaction with an agent. This was built using the vector database functionality we have integrated.

The peace of mind that Microsoft Azure Cosmos DB provides regarding global distribution is invaluable. In traditional databases, you need to consider how to scale, whether horizontal scaling is possible, and handle multi-regions, multi-masters, redundancy, and other concerns when building a world-scale solution. We get most of these features with Microsoft Azure Cosmos DB essentially included.

I would discuss two separate streams. The first concerns the local developer experience. Microsoft Azure Cosmos DB is a complex cloud platform service, and when developing applications, the most legitimate way to test it is by using the actual product. The ability to run an emulator locally would reduce development costs and improve accessibility, eliminating the need to provision it for each developer. When developing an application, developers typically run everything on their own machine. With Microsoft Azure Cosmos DB, to get the exact same experience and features, we end up using it in the cloud on Azure and paying for it during development. As we add or remove developers from the project, we need to provision new databases or instances. Having the ability to run an emulator or replica in the local development environment would be fantastic for cost savings and developer onboarding.

The second area involves tooling around projected costs for queries. Microsoft Azure Cosmos DB has a unique way of using units to charge for CPU or compute while running queries. Having a calculator to determine query efficiency and expense based on current data structure and projected volume would be really interesting. However, if I had to choose one improvement, it would be the local development experience.

We have been using Microsoft Azure Cosmos DB since its release, approximately eight years ago, and we have witnessed its entire journey.

The resiliency aspect makes Microsoft Azure Cosmos DB our go-to solution for databases. It has the ability to run in multiple data centers. If there happens to be an outage, which is unlikely, you still have spare nodes and replicas available. The SLA ends up being extremely high from an overall service perspective. Having the flexibility to continue operations even if one Azure region goes down is significant, as you can still write to it and restore functionality when the region returns. With traditional database engines, you would need to implement complex workarounds, such as restoring backups in another location and attempting to sync back to the original location. The stability is excellent, and its resiliency in globally distributed deployments is outstanding.

The scalability is excellent, though it comes with associated costs. When you need more replicas, regions, or additional resources, you will need to pay for them, but you maintain the ability to scale. This contrasts with deploying your own database, where you would need to handle maintenance, and scaling to required volumes might not even be possible due to engine design limitations. Microsoft Azure Cosmos DB has been built with scalability in mind, which is evident throughout the product deployment. The ability to configure regions and replicas is crucial, and it feels unlimited in potential. As long as you can accommodate the costs, you have the opportunity to expand and improve the SLA without re-architecting the entire solution.

I have used MongoDB and AWS Aurora in different combinations, such as self-hosted MongoDB, MongoDB Atlas, Aurora, and Postgres. Compared to others, what stands out about Microsoft Azure Cosmos DB is its scalability. When working with MongoDB or traditional SQL databases, horizontal scaling and multi-region/multi-master scenarios are complicated topics that require significant work and planning. With Microsoft Azure Cosmos DB, it's simply a matter of flipping a switch. Though there is a cost involved, it removes many complexities and saves our team considerable time.

It's really straightforward and easy to get started with Microsoft Azure Cosmos DB. One of the main advantages is its compatibility with various drivers. For example, if you are migrating an application from MongoDB, you can use the same MongoDB driver to interact with it. The same applies if you're using SQL or DocumentDB; you can leverage the existing code with minimal changes. This is a significant benefit, especially in scenarios where you might be considering a switch in database engines. Often, developers worry about having to revise their entire application when changing databases, but with Microsoft Azure Cosmos DB, that's usually not necessary. For developers familiar with DocumentDB or MongoDB, the ability to use the same libraries and code brings a sense of familiarity, which is a major time-saver. Additionally, provisioning through the Azure portal is a breeze—it's as simple as clicking a button to get started.

The initial setup took less than an hour to do properly, approximately half an hour.

It does not require any maintenance, but as software systems are living and breathing things, you might need to adjust usage patterns and queries for efficiency. Compared to running your own database, there is no maintenance - you don't need to worry about indexes, drives getting full, or CPU scaling.

The implementation was completed by one person.

The pricing for Microsoft Azure Cosmos DB is good, but there is a developer factor to consider. It could be economical or expensive depending on usage. Guidance about query consumption of Request Units (RUs) would be beneficial, especially since costs can escalate if not used properly. When building solutions on Microsoft Azure Cosmos DB as intended, following guidance and documentation, it works well. Compared to traditional databases, it has a different pricing structure that factors in multi-region capabilities, number of requests, and multi-master functionality. While traditional managed databases simply consider CPUs, memory, and bandwidth, Microsoft Azure Cosmos DB's pricing involves more variables. When used properly, it can be more cost-effective, offering better value due to the included multi-region capabilities, which are quite expensive to implement in traditional database settings.

My advice is to start with the drivers you are most familiar with. If you have experience working with MongoDB, begin using Azure Cosmos DB with the MongoDB driver and the code you already know. From there, you can gradually learn about specifics such as request units (RUs), indexing, and partitioning—elements that contribute to what makes Microsoft Azure Cosmos DB powerful and scalable. By leveraging SDKs and libraries you are already accustomed to, you'll have one less thing to worry about: how to use the platform effectively.

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

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

The high speed of Azure Cosmos DB compared to other competitors is remarkable. It is one of the most powerful features, offering high availability and high speed. Its benefits can be seen immediately after the deployment.

Overall, it is a good resource. I am not aware of the background, but it seems to currently support only JSON documents. They could expand their scope to support other types of data, such as XML or EDI formats. EDI is an old technology, but it is still in high use in supply chain and retail industries.

I have more than two years of experience with Azure Cosmos DB, whereas with Azure, it has been more than four years.

Choosing the correct partition key is crucial, as it affects our database speed and related operations.

Latency and availability depend on the consistency level.

It is a Platform as a Service, so we are concerned about the underlying interface. We can move to a higher tier as all Azure cloud resources are open to easy scaling.

It offers an option alongside the Azure SQL database. Azure SQL database has its own capabilities, whereas Azure Cosmos DB supports all major big data requirements like Cassandra and Gremlin. Azure SQL database is more focused on transactional data instead of analytic data. Azure Cosmos DB covers a wider area.

I have not personally deployed Azure Cosmos DB, but DevOps pipelines provide options for this. It should be easily deployable with the help of Microsoft's documentation.

It takes a couple of minutes to be up and running. It also depends on how we are deploying, whether it is via an ARM template, Azure pipeline, or directly via Azure release.

Azure Cosmos DB is generally a costly resource compared to other Azure resources. It comes with a high cost. We have reserved one thousand RUs. Free usage is also limited.

It is not like a traditional database. Choosing the partition key needs an understanding because it will affect the database speed. By making your partitions in a logical and efficient way, you can improve the speed of search analysis.

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

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.

I found Cosmos DB to be rather intuitive and straightforward. The documentation is pretty clear because it is a managed service. I could give the custom developers their endpoint and even set up managed identity in a way where we do not have to worry about having secret keys and all of those pieces. We are using private endpoints for everything and found it to be working just as advertised.

The most valuable features include the global write capability, which allows customers to read and write across different regions simultaneously, enhancing performance and availability. 

A lot of my customers like the ability to choose a different API with which they are familiar. The flexibility to choose different APIs, such as MongoDB or Cassandra, allows customers to leverage their existing knowledge while using Microsoft Azure Cosmos DB. 

We are using the vector database a little bit. We often use Azure AI search for that capability. We have an application that is taking in legal documents and needs to do a semantic search against those. It is a combination of using the embedding models and vectorization to get closer to the right chunks of the documents that they are looking for. We, in turn, send that over to Azure OpenAI services to fine-tune and get the best result from our initial results.

We integrate the vector database with another application. It is a custom-built homegrown application that provides a UI for their end-users to be able to use AI search and vector search to be able to get highlighted results of their PDFs.

The vector database absolutely improved the search result quality of our customer's organization. They are partially using Microsoft Azure Cosmos DB in that, but, in general, the two combined absolutely did help by not defaulting only to keyword search and being able to do a hybrid between the two.

For this project, there has been significant improvement in the time to process these documents. There has been a 5x time reduction for the end users in finding the data they are looking for, inputting it into their model, and performing their workflow.

In terms of Microsoft Azure Cosmos DB’s ability to search through large amounts of data, for this specific use case, we are probably on the low end of what Microsoft Azure Cosmos DB can accomplish. We have a decent dataset, but definitely not a gigantic one. So far, our experience has been great, but we are not necessarily testing it to its limits. The one that we are working on is still under a terabyte. We only have several hundred gigabytes for this specific customer. It is a lot of data, but in the grand scheme of things, it is not very much.

Continuing to educate customers on how they can take better advantage of Microsoft Azure Cosmos DB without having to completely rewrite their entire application paradigm would be beneficial. They can help them understand that there are multiple options to interact with it. They do not necessarily have to start from scratch. They can refactor their existing application to be able to use it better.

They can continue to find better use cases for it. It helps to be able to show our customers example documents or example applications. It definitely helps us to be able to show customers how they could be using this.

I have been using Microsoft Azure Cosmos DB for around a year to a year and a half.

The latency and availability of Microsoft Azure Cosmos DB are fantastic. It provides resiliency and business continuity without having to do much. Having it already built in is a big selling point.

I am not working with any customers who are going to have any problems with scalability. We are not going to push the limits of what it can do. My customer base does not have to worry about scaling because none of their applications are ever going to struggle with something as global and as resilient as Microsoft Azure Cosmos DB.

Microsoft Azure Cosmos DB’s dynamic scaling helped decrease the overhead costs for our customers. They have spikes, but most of the time, they have a pretty low baseline. Rather than overprovisioning to handle those spikes, they are able to settle in and ride the waves of their utilization throughout the days and weeks. They have seen a decrease in costs and expenditures. It is still early for a lot of it because a lot of new functionality was added. They did not necessarily have a true baseline to compare against, but they like the idea that it is so elastic.

The onboarding process was relatively quick, taking about six to eight weeks, as the team adjusted to using Microsoft Azure Cosmos DB.

We have not run into many challenges during the migration or implementation of Microsoft Azure Cosmos DB other than being novices and unfamiliar with it. We need to understand all the different components of it, but we have not necessarily run into any technical problems or issues with timelines or things like that.

It takes only a couple of months to onboard customers with Microsoft Azure Cosmos DB. We are able to go pretty quickly. Our onboarding path is about six or eight weeks.

There is a bit of a learning curve for the customers who have only worked with traditional Azure SQL VMs and are not familiar with having a fully managed or PaaS instance. There is some learning curve for them to understand that they do not just have x number of cores or memory available, and it just grows as they use it.

In a couple of use cases, Microsoft Azure Cosmos DB helped decrease an organization’s total cost of ownership. Oftentimes, when we are implementing some of these features, we do not have a baseline to compare against. In my own experience, there definitely is an opportunity if we are able to use the model to reduce cost instead of provisioning a VM or something like that, as we would historically do. It is hard to provide metrics, but when I have done comparisons or cost calculations, I have sometimes personally seen as much as 25% to 30% savings.

Most customers like the flexibility of the pricing model, and it has not been an issue. They can start small, and the cost grows with adoption, allowing efficient management of the budget. Its pricing model has not been a concern at all for any of our customers. They understand it. It is simple enough to understand. Oftentimes, it is hard to forecast the RUs, but, in general, it has been fine.

I would rate Microsoft Azure Cosmos DB a nine out of ten. There is always room to grow, but it is a highly capable solution. I am looking for more opportunities to use it as we help customers move toward more cloud-native technologies, rather than always defaulting back to what they are familiar with, which is sticking with Microsoft SQL Server or Azure SQL.

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.

It has been very efficient so far. The team has been using it for quite a while. I am new to the team, but they always talk about how efficient it is. We are using the NoSQL version. It is easy to use for development. It is reliable and quick. 

It has been pretty efficient when it comes to search. I have no complaints about that. It is easy to use and very compatible with Java.

I had a challenging experience implementing the emulator with a Mac. I had to install the emulator in a Docker container because it is not natively compatible. A significant amount of time was spent researching how to enable HTTPS communication when connecting the container and the emulator. I encountered TLS and SSL errors but resolved most of them by setting an environment variable in the container and using HTTPS protocol communication. I also had to use gateway mode with the Cosmos client in my Java app. I am disappointed with the lack of compatibility of the Microsoft Azure Cosmos DB emulator with Mac. I also found a scarcity of online resources regarding this issue.

It would be great to include compatibility with various databases like graph databases, adding to the existing NoSQL and MongoDB compatibility. I have used that for various projects on other platforms, and such additions would be beneficial.

I have been using it for about a week now.

I do not see any stability issues. I would rate it a ten out of ten for stability.

It is scalable. I would rate it a ten out of ten for scalability. We have had no issues with its ability to search through large amounts of data.

We have thousands of users. We are a big organization, and it is being used at various locations.

I love the community forums. They provide a wealth of useful information, which gives me an advantage when it comes to support. The only disappointment was not being able to find any information about setting it up on a Mac.

Neutral

I have used the cloud-based Firestore database and MongoDB before. They largely perform similar tasks, and I have no problems using either one. They work and get the job done.

For me, the setup was not complex because my team had everything ready.

I watched a couple of videos on YouTube. The onboarding was seamless, especially the database part. It took me no more than two days to learn the basics and necessary setup.

In terms of maintenance, it does not complain if you do not update it, but there are always updates that you can add. For example, for the emulator that I am using, there are a lot of versions I can install, but it works with most of them.

I have no complaints. It does its job efficiently and is easy to set up. Our organization has been using it for quite some time. They must see a value in it. Otherwise, they would go for a better technology in terms of performance or pricing.

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