Microsoft Azure Reviews

Microsoft Azure is a ticketing system that provides support services to Microsoft partners based on their partnership tier. Partners can raise support tickets, particularly for production-related issues. When we had issues with our Cosmos DB instance in production, we would submit a support ticket with screenshots detailing the error. We usually receive a call from a support engineer the same day.

Once a support engineer was assigned, the process was well-organized. Each engineer worked shifts of about eight to nine hours, and before their shift ended, they would email us to let us know who would be taking over, for example, "John Doe." They would also provide a summary of the issue, progress made, and next steps, ensuring a smooth handover without the need to re-explain the problem to the next engineer. It was a very efficient process.

Microsoft Fabric is a new service that integrates various functionalities into one platform, such as data engineering, data science, and data visualization. While promising, some complexities and limitations become apparent only after you use the service.

Fabric is a comprehensive and expensive service with tier-based pricing. For someone new to the platform, like those coming from Snowflake or Databricks, it can be difficult to grasp the cost implications and potential limitations without extensive exploration. The documentation outlines these limitations.

Pricing is very expensive.

I have been using Microsoft Azure for five years.

Cosmos DB offered better scalability. In Azure SQL, we had to carefully manage the storage tier, switching back and forth based on needs. But when we first implemented Cosmos DB, we had a good understanding of the required data volume over the past three months. This helped us select the appropriate tier because we had those figures.

If we had done it the other way around, it would have been much harder to estimate the data accurately. While you can get a rough idea, whether it works out in practice is more of a hit-and-miss approach, which we have already experienced with Cosmos DB. We applied the knowledge gained from that exercise to build a similar solution over Azure SQL.

On the DevOps side, our team consisted of five members, ranging from senior to junior roles. We would raise requests for specific services needed in the development environment. The process typically takes three to five business days, including all necessary approvals, from submitting the request to receiving the resources and verifying access.

Sometimes, even with the documentation available, understanding how it applies to our specific issues requires contacting a system engineer and creating a support ticket. The support engineer often provided additional details that were not included in the official documentation. This highlights the gap between the provided information and practical, real-world use cases.

Deployment was relatively straightforward. We didn’t encounter any issues whether we were using the GUI mode or working with our DevOps team, who utilized Terraform to provision resources as needed.

Most people use Azure Synapse for data onboarding and integration. Synapse is a big data analytics platform where data engineers and scientists can collaborate on a unified platform. Another service called API Management serves data outside Azure to downstream consumers or third parties. This is useful for cases where third parties can't be onboarded onto your system due to compliance or regulatory issues or need a specific slice of your data in near real-time. When we used Cosmos DB, we hosted near real-time data with a latency of about 15 minutes and served it via API M. With API M, authentication tokens were shared with the end users, allowing us to know precisely who was consuming data outside of our Azure environment. This setup was unique because it required providing near real-time information with minimal latency, even as the number of concurrent users increased. Cosmos DB worked well in this scenario due to its horizontal scalability. We also hosted similar data on Azure SQL, as Cosmos DB can be more expensive for cases where a smaller amount of data needed to be served, such as just the last one to three months of data. Azure SQL was a good alternative. The same API M interface was used to serve data from Cosmos DB and Azure SQL. Under the hood, one API would fetch data from Cosmos DB, and another would pull from Azure SQL. This architecture was tested and worked well for our client.

It was easy to maintain. Overall, I rate the solution an eight out of ten,

We utilize various Azure services such as Azure SQL, Azure Cosmos DB, Azure Data Factory, Azure Storage, and Azure Key Vault to build our data platform.

One of the features that I really like about Azure is its comprehensive environment for implementing a data platform. The solution provides multiple well integrated services which happen to work together seamlessly and provides flexibility and scale ability for use cases all around the industry.

There are some minor aspects which require improvement but they are very technical for an explanation. For example, the cost calculation for the services can be an unclear aspect which makes it difficult to estimate the expenses incurred accurately. Therefore, it will be beneficial to have it more transparent and apply your user-friendly cost estimation process to help users plan their budgets in a more efficient manner.

I have been using Microsoft Azure for the past 5 to 6 years.

I would rate it 9 out of 10 since it is stable but can get better.

I would rate the scalability 8 out of 10. The number of users on a platform is high but we do not have active engagement from all the users all the time. We have over 100 users but not all of them interact with the platform regularly. Keeping it aside, our main focus still remains on building data platforms which cater to different needs.

I have mixed feelings about Microsoft's technical support. While they do try to assist most of the time, I believe there's room for improvement. 

Neutral

As a data engineer, I mainly use these services rather than being involved in their implementation.

I would recommend Azure since it offers a variety of features and services which work together. This is quite rare in other platforms because they are not scalable. These features have helped us to promote our growth without any major challenges. The wide range of services make it a valuable option for implementing a data platform. However it is really difficult to quantify its value numerically. Different companies can Paradise different factors such as scalability, cost, or support which makes it different for each company.

It is important to keep in mind that there are also challenges such as the complex nature of estimating costs and no realistic expectations of savings due to the presence of hidden costs. Azure is an efficient solution but it requires improvement in terms of stability, support and pricing.

People use it for various reasons in my organization. Data engineers use it to target databases for data ingestion. 

As a data scientist, I primarily use it for modeling and analytics purposes. 

Databricks is extensively used for these purposes and also for deploying models into production. It's used across the entire pipeline in my division.

Azure offers broad compatibility with both structured and unstructured data. For example, we use PostgreSQL for storing Azure's official data and manage various types of data, including tabular and image data, accommodating the storage of all data types we handle. So, in many ways, Azure simplified the data storage and management needs.

However, we don't use  Azure for all our web hosting requirements. For example, for production and web hosting, we use a combination of Azure and other native languages and hosting services. So, it's not entirely dependent on Azure.

Being involved in building models, I appreciate the integrated version control, which obviates the need to manage different versions of my models manually.

I don't have to do a lot of experimenting; the version control is built-in. 

Azure also offers additional features in the AI space, which are beneficial, making it convenient to have all tools in one place without the overhead of using different tools for different purposes.

Integration with Microsoft products is seamless, facilitating connections to tools like Tableau and enabling easy API creation. 

One thing I find is that there are updates happening all the time, but they don't always roll out information about the changes. For example, the way to connect to Git repositories six months ago was different from how it is now. 

It would be good if the platform incorporated some kind of announcement system, like "This process has changed, here's the revised method." That would be really helpful. So, update announcements should be there. 

I have been using it for two and a half years. 

I would rate the stability an eight out of ten. It's quite stable, although there have been occasional downtimes affecting entire regions, but these issues are resolved quickly without causing significant business interruptions.

Azure's scalability, particularly in terms of cores and threads, significantly enhances our work. 

The ability to easily adjust capacity is crucial, especially when working with large geospatial datasets and running transformations or models requiring substantial computational resources.

In my department, there are around 40 end users using this solution. 

I would rate the scalability an eight out of ten. 

The platform's scalability meets our needs well.

We haven't actually needed to contact Azure support very often. However, the Databricks team, specifically our Service Account Manager, has been very helpful. They've reached out a couple of times to understand the types of projects we're working on and suggest additional functionalities that might be beneficial.

There was also some communication with the GenAI team about potential use cases that could be integrated into our platform or future products. 

I've never had to directly contact an Azure support representative. 

The online documentation is very comprehensive, and there's a large, active community that we can leverage for troubleshooting. 

Basically, all the documentation we need is readily available to resolve any queries.

We also use Cloud Foundry, alongside Azure, for various tasks within our operations.

Deployment is generally stable. But there are specific scenarios, especially in Asia Pacific, where there are limitations.

Microsoft hasn't fully rolled out services in all regions yet. This can cause problems when we try to deploy certain models in those specific regions where services are unavailable.

In contrast, if you have a self-deployed API, something you created in a native language and deployed on your own server, it would be more readily available across regions. 

So, the lack of Microsoft services in certain regions can become a blocker.

Troubleshooting and maintenance for Microsoft Azure products are handled by our internal IT team.

We have around 40 to 50 people, encompassing a mix of roles including administrators, engineers, and developers.

I would recommend using this solution. Internally, we've always preferred it over Cloud Foundry. Here's why: Microsoft Azure has been a much more seamless experience. For example, Cloud Foundry isn't very scalable. It can't handle parallel computation well, and it frequently hangs. Plus, it's not very user-friendly as a platform.

So, for all those reasons, I would recommend Azure. It's highly scalable, supports parallel computation, and offers all the steps needed for a product lifecycle within one platform. That eliminates the need for separate storage, deployment, and production environments – everything's available from the start. Those are the key reasons for my recommendation.

Overall, I would rate the solution as eight out of ten because there are still areas for improvement, like integration with cross-platform. 

We use it for virtual machines, database services, load balancers, and firewall services.

The platform as a service for MySQL and PostgreSQL have significantly improved our operational efficiency. The performance is very good, and it has improved our business performance a lot.

Azure Security and Compliance features:

Azure offers built-in security called Microsoft Defender. This is a paid version that provides posture management for the servers. If there is any setting that needs to be changed or any problem with any network setting that could lead to a disaster, it shows us on the platform that the posture is not correct, and we fix it accordingly. It provides proactive overall management and monitors the entire infrastructure.

AI initiatives:

AI initiatives have been introduced, but we haven't tested it, so we cannot comment on that.

Everything is in the same control panel, and the management is very easy. If there's a new feature, it takes less time to understand the working procedure on the platform. So, it's user-friendly.

I would like to see improvement in the technical support. Sometimes it takes a long time. Only sometimes, not every time. It gets delayed. The response time should be faster.

I have been using it for six years. 

It is a very stable product. We haven't faced any issues with it, and it's been about five or six years.

I find it scalable. Whenever there is a large requirement to increase resources, we can increase them on the fly and decrease them when it's not necessary. It's very easy, and it takes very little time to scale up deployments.

Sometimes there are some problems that need to be taken care of by the Microsoft technical support team. We raise tickets for these cases, which are not solvable by email support. They call us back and resolve the problem.

Neutral

Previously, we used in-house servers. We moved to Azure. As for other solutions, no. There was no other solution. Everything was in-house. We had physical servers, and then we moved to cloud services.

I did not have any challenges or complexities with the initial setup process.

There's a vast knowledge base available. So, there were no challenges. If there's a new deployment, the tutorial is already there. You just have to follow it.

It took a couple of days to complete the migrations.

Maintenance happens at the machine level, not the platform level. There's no maintenance requirement at the platform level.

The initial deployment consisted of about twenty-five virtual machines and five to six services.

We did it in-house.

It has impacted cost savings or business productivity overall. We can easily scale up and scale down the services as per the requirement. 

During some months, the requirement is high. During that time, we scale up the service and scale it down when not necessary. So, it saves a lot of cost for us. The resource cost is not fixed here, and we can scale up and scale down as per the requirement. When we scale it down, the cost gets reduced by a lot.

The pricing is fine. Whenever you deploy a new service or virtual machine, they provide a calculator service that can upfront calculate the tentative cost for the service or resource, monthly or annually. On top of that, the third party through which we have taken the services adds some more discounts.

The recommendation would be to first go through the tutorials, or you have to know the requirement first. As for that, you need to go through the tutorials provided by Microsoft, and then you are good to go.

Overall, I would rate it an eight out of ten because there's always room for improvement.

I integrate our technologies into Azure. I just deploy, do some tests and POC for Azure. A recent project leveraged Azure's analytics services, it was related to Security Hub and Azure B1.

I support customers with their Azure implementations to better understand Azure architecture and how to integrate our technologies.

I like the ease of implementation and development and how quickly everything is ready.

Azure's security and compliance tools are not enough, and that's why other technologies like Fortinet would be much more recommended than the native ones.

Security, for sure, is an area of improvement. 

I have been working with it for three years now. 

I would rate the scalability a ten out of ten. Our customers are medium and enterprise businesses. 

I would rate my experience with the initial setup a ten out of ten, where one is difficult, and ten is easy.

It's cloud-based, both public and private.

The deployment can take a few days. I deploy it in the customer's premises, in their private or public cloud account.

I would rate the pricing a six out of ten,  where one is cheap, and ten is expensive. It's in line with other public cloud providers.

I would recommend it because it's easy to implement. But it always depends on the customer's needs. If their specific needs match with going towards the public cloud, then Azure would be one of my main recommendations.

Overall, I would rate it a nine out of ten. 

Our use cases for Microsoft Azure are financial-related.

Microsoft Azure covers all aspects, and having a single pane of glass becomes easier.

Microsoft Azure's design language is a little too basic. I would prefer Jira over Azure Boards. While Azure Boards offer quite a lot of customization possibilities, they also have their own limitations.

I have been using Microsoft Azure for more than a year.

Microsoft Azure is a stable solution.

I rate the solution a nine out of ten for stability.

Microsoft Azure is quite a scalable solution. More than 5,000 users are using the solution in our organization.

I rate the solution’s scalability an eight to nine out of ten.

I have previously worked with AWS and Jira.

We didn’t face any challenges with the solution’s initial setup.

The solution's deployment time takes less than hours. We continuously keep doing it. We have releases every week, and our pipeline keeps running. We update the product every week.

One or two engineers are involved in the solution's maintenance. Microsoft Azure has positively affected our operational expenses and revenue growth, which is why we switched from Amazon to Microsoft Azure.

I would recommend Microsoft Azure to other users. I can use Azure Boards to do brainstorming. Microsoft Azure covers all aspects, and having a single pane of glass becomes easier.

Overall, I rate the solution an eight to nine out of ten.

I am using Microsoft Azure primarily for application development purposes. Additionally, I am utilizing it for machine learning and Internet of Things.

Microsoft Azure has made it easier for me to use and format my applications effectively.

The most valuable feature is the broad range of services offered by Microsoft Azure, such as computing, networking, and Azure Arc, along with security and compliance. 

Additionally, it has made my applications easier to use and has improved the development process.

There is room for improvement in the pricing structure, user interface design (UI/UX), clear navigation, and documentation support of Microsoft Azure.

I have been using Microsoft Azure for about three years.

I would rate the stability of Microsoft Azure at eight out of ten.

I would rate the scalability of Microsoft Azure at eight out of ten.

I am very satisfied with the technical support and would rate it ten out of ten.

Positive

I did not use a different solution before Microsoft Azure.

The initial setup was a bit complex yet became easier as I learned more about it.

I would rate the pricing of Microsoft Azure at eight out of ten, which makes it on the pricier side for me.

I have already recommended Microsoft Azure to my colleagues, and they are finding it useful. 

Overall, I would rate Microsoft Azure an eight out of ten.

The performance of Azure DevOps services is really impressive. It really demonstrates the value of the service. 

We've had a good experience with their technical support. They are always available when needed.

I haven't been involved with continuous integration and continuous delivery (CI/CD) services, but I hope there will be more training sessions for them. Other companies and platforms provide technical webinars and training on their websites. 

I wish I could learn more about CI/CD, but the resources aren't readily available. I haven't been able to find webinars or training sessions. Maybe I'm not searching correctly, or the information isn't presented clearly.

The price should be lower. My company used the demo product because of the cost. Price is an important point for all clients and companies who want to obtain this solution.

I also have a question: what about review platforms for forensic technology? Is there a way to integrate these platforms with Azure? It would be great to have a web-based review platform integrated with Azure. Currently, we use a third-party software to launch this review platform.  Is there any way to review documents within a web-based solution in Azure? This is something I wonder about.

I've used Azure for a year and a half, specifically for DevOps services. Unfortunately, I worked with a demo product, as the company couldn't purchase the actual product. I mainly used Azure DevOps repos and some basic concepts like Azure state plans.

Azure solutions are stable. Many companies find clients who want the solutions because of the stability and high performance.

It's a good solution for small or medium-sized businesses.

The setup is very straightforward. The Azure services and platform for storage are easy to understand, with clear steps. It's easy for any user to install services on the platform.

I haven't had any issues with integrating Azure services. We use the integration services with Azure with a very intuitive UI. So, I didn't face any issues while integrating Azure services.

I would rate it an eight out of ten. There are a lot of benefits. The integration options for all services are great.