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My main use case for Digital.ai Release is to release Azure-related cloud resources like Azure Key Vault and Application Insights to support any cloud integration on the Azure side.

A specific example of how I use Digital.ai Release for one of those Azure resources is that we normally do an annual release to update the certificate for the Azure Key Vault because the certificate expires every year, so we use Digital.ai Release in combination with Jenkins and Terraform to release the new certificate.

In addition to that, I use Digital.ai Release for most Azure resources we use to support our API like Azure Key Vault and Application Insights, application registration, and Redis Cache.

Digital.ai Release has impacted my organization positively because almost all the teams that handle cloud resources related to Azure are using Digital.ai Release in combination with Jenkins. It has become a standard way for us to release cloud-related resources, although we also use Microsoft Azure DevOps for other code releases. For Azure-related resources, this has become the standardized way.

The best features that Digital.ai Release offers are that through the template, I can view the different phases of my release, so everything is streamlined when I use Digital.ai Release, and the integration with Jenkins is very good.

The integration between Digital.ai Release and Jenkins is seamless. If there are any issues and anything goes wrong for a particular environment, I will see a red flag from Digital.ai Release. From there, I am able to have a link which leads me to the log file of Jenkins to view the details about the release, which is very convenient.

I appreciate the way I can create the template using standard artifacts. I have a section for Terraform and a section to define my release using the YAML file, and it is standardized.

Since using Digital.ai Release, one of the benefits is standardizing the way I release to my Azure environment. I could manually do everything, but that is very error-prone, and everybody might do it differently. By following Digital.ai Release, I am following the naming convention already by using a certain configuration file with variables. The best part is standardizing things, which in the long term will help me reduce costs and improve efficiency.

To improve Digital.ai Release, I think the user interface could be improved. For example, I have a plan phase before my build phase, and sometimes the toggle button is hidden. I have to toggle it before the step can be executed, or it will be skipped. Many people who did not use Digital.ai Release before do not even know there is a toggle button, and the first time when they run into that phase, they will definitely skip that step.

Regarding needed improvements, I did not do extensive reading on documentation or training material directly from Digital.ai Release. My knowledge comes from the team who has been using it. However, I would appreciate standardized training material that would give me hands-on experience.

I have been using Digital.ai Release for four to five years.

Digital.ai Release's scalability seems to be adequate, but I do not think we have done anything challenging in terms of capacity for the framework since we are only releasing a few cloud resources at a time, so we might never run into a bottleneck.

Customer support is good, and we did not run into any issues directly with Digital.ai Release's customer support because we have a release team to help us with Digital.ai Release. If we have any issues, we work with that team directly.

Before choosing Digital.ai Release, we changed many different vendors for release management over the years, but Digital.ai Release is definitely the choice for releasing cloud-related resources.

I did not think we used anything else before Digital.ai Release because this has been the standard way of releasing cloud resources from the beginning, especially since we have team members who had this experience to help us establish the framework.

Since using Digital.ai Release, one of the benefits is standardizing the way I release to my Azure environment. I could manually do everything, but that is very error-prone, and everybody might do it differently. By following Digital.ai Release, I am following the naming convention already by using a certain configuration file with variables. The best part is standardizing things, which in the long term will help me reduce costs and improve efficiency.

My advice to others looking into using Digital.ai Release is that it seems very flexible. I understand we are using Digital.ai Release's Jenkins integration, and for the Jenkins component, potentially I could switch to other solutions. It seems to me it is a flexible framework to release cloud-based resources, so it is a good option for this purpose.

I would like to see more AI capability in Digital.ai Release because AI has improved our productivity in different areas of our daily working environment. When we do development using Copilot, I see improvements when we use the cloud to help us in our development. However, in Digital.ai Release, since I am not a frequent user, I do not see much integration with AI yet, and that is an area where I would like to see further development.

I would rate this review as a nine out of ten.

My main use case for Digital.ai Release involves using Jenkins, XL Deploy, and XL Release. Jenkins typically handles the build, XL Deploy maintains all information related to the packages, such as their location, the latest builds, and which packages are on each server, while XL Release performs the actual release to the servers.

The best features that Digital.ai Release offers are the custom pipelines and the ease of customizing different releases in the same pipeline, especially with different user interactions. We have infrastructure team members who follow the pipeline while we, as customers, approve different roles or different responsibilities within the same pipeline, with one person doing approval and another doing the deployment part, which is valuable from my perspective.

Involving both infrastructure and application teams in the same pipeline has genuinely helped my process, as we have one specific person starting the pipeline, another approving it, and another coordinating as DevOps or monitoring all processes from the infrastructure side, providing excellent assistance because we have different and clearly separated responsibilities.

Digital.ai Release has positively impacted my organization by making the process easier than using a highly customizable Jenkins that is difficult to manage. Since everything was already in place when I started working with this company, I cannot easily articulate what the specific benefits to the company are. However, I can say that I previously worked with a fully customized Jenkins alone, and this approach is significantly easier and more maintainable.

Sometimes it can be tedious when fulfilling multiple different packages with their key and value pairs. If we make any mistakes in the second step and return to the first screen, everything is erased, so there is no way to return to the first screen and start again with the same package or make small updates or changes to the initial screen before restarting the process.

Digital.ai Release is very helpful in saving time, but I wish there was the possibility to use the API as a release manager to automate pre-filling the pipelines.

If we had an API that could be used on the user side, similar to the one in JIRA where we can create a personal token without granting full access to Digital.ai Release, I could have my script automate the process instead of fulfilling the template field by field, which would be excellent.

I chose eight out of ten because I see it is a nice tool. I do not know it in depth to give a higher or lower rating at this point, but for my use and role, I appreciate the parts of the tool I am familiar with. We could have some small enhancements, such as being able to go back to the first screen while keeping everything filled in, but beyond that, I do not have any other suggestions.

I have been using Digital.ai Release for around one year.

Digital.ai Release is very stable from my perspective.

I do not have enough information about the scalability of Digital.ai Release. I am just a user, and I do not even know how many different users are currently using the solution.

I had previously used different solutions, including GitHub, GitLab, and Jenkins, specifically its first version named Hudson. However, in this company, I have only used XL Release and XL Deploy.

My advice for others looking into using Digital.ai Release is that it is a nice solution for corporate companies. It is particularly valuable when you have different roles interacting in the pipeline, and it provides an easy way to connect with other applications such as Jenkins and servers including Linux, Control-M, and others.

As a user from a corporate company, the details of the configuration, possibilities, and features such as the mentioned AI capabilities are somewhat closed off to me. I would appreciate knowing more, and I wonder if you have any demo account or something I could use for a trial period. I also have a small company with solutions in AWS using Python and different Linux servers, Kubernetes, and similar technologies, and I would like to try to see how Digital.ai Release could be useful for my personal company.

I gave this review a rating of eight out of ten.