Digital.ai

I use Digital.ai Release for one of my clients to run Ansible templates in multiple workflows, so we create release automation and use the release script for any Azure resources management.

For one of my clients, I use Digital.ai Release to create an Azure Landing Zone, resource group, and default subnets for multiple layers such as app, data, private endpoint, and presentation, by creating separate templates in Digital.ai that allow us to create the resource group as one template and different resources like app services and Azure SQL databases with multiple templates. We create a release to run those templates by providing input values, and Digital.ai Release is able to integrate multiple pieces, especially with Azure, Ansible, Jira, and other tools within the organization.

Digital.ai Release is primarily used for this purpose and some additional functions related to managing Azure through Ansible.

Digital.ai Release offers the best features such as good integration between multiple tools and flexible options for usage; while I have not configured the template myself, I use existing templates and find that anything can be accomplished using the workflows and integrations.

The integrations I have seen currently include Ansible, Jira, email communication, and ServiceNow, which all really help to complete the full workflow.

Digital.ai Release has positively impacted the organization I currently work for at a very high level, mainly because it has allowed us to automate many tasks that previously required manual intervention; for example, creating a resource group and all these subnets manually would have taken one to two days, whereas now it is completed within a minute using the automated processes.

As I do not work much on the configuration of the templates because I am just a consumer, I do not see much improvement required, as it looks good to me.

I am not certain about further improvements since I have not used the template settings; however, I feel that it could be more user-friendly, as the UI feels a bit old and not very appealing. Additionally, to see the logs is challenging because we need to open multiple windows, and it does not display in full screen, which could definitely be improved.

I have been using Digital.ai Release for more than a year.

Digital.ai Release looks good and is suitable for anyone who wants automation.

Regarding Digital.ai Release's AI capabilities, I find its governance and security to be good and really helpful for multiple integrations, as all OAuth is handled through SSO and connects to ServiceNow and Jira in an authenticated way; I believe it looks good and I do not see any issues with the capabilities related to governance and security.

In terms of Digital.ai Release's AI capabilities, I find its accuracy and reliability of output to be good and reliable, and I do not see any issues.

I rate Digital.ai Release seven out of ten, considering the fact that the user interface could be improved.

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.

It helps with creating documentation, release processes, deploying to lower environments, scheduling meetings, and sending emails to stakeholders. The goal is to reduce manual work and save time.

We start by creating documentation for all these tasks. Initially, this includes all the details of the artifacts and the database. Then, the developer updates these details.

We perform the deployments using Digital.ai Release. What we do is input the artifact information, and it handles all the deployments in the lower environments, usually within a minute or up to five minutes.

This way, we avoid the need to use Jenkins to locate the artifacts and then deploy them, which would take much more time. Here, we simply input all the artifact details once, and later, we only need to update the artifact version for deployment. This significantly reduces our time.

Moreover, once the deployment is completed, Digital.ai Release automatically sends an email notification stating that the deployment is complete, and we can begin testing. So, that's how it works efficiently for us.

I like it because previously we had to manually create documentation, and deployment also would take much time.

Also, for higher environment deployments, we had to create tickets for other teams. That time is also reduced because the manual work has tremendously decreased. We just have to click one button, and it will create everything for us.

It's crucial in cases like deployment errors. Digital.ai allows rollback to previous versions, enhancing our ability to recover quickly.

For higher environments, what we do is roll back to the previous version using Jenkins if there's an issue.

There are many areas of improvement. Currently, we put artifact details manually. What we could improve, in our case, is the deployment instruction base. Developers input all the information, including which artifact and where it needs to be deployed. What Digital.ai could do is automatically go to the deployment instruction page, take those artifact details, and implement them. This way, there would be no need to manually input the details.

It is a stable product. I would rate the stability a seven out of ten because sometimes it gives errors and doesn't work properly. Whenever it happens, it gives a headache because we have to hand over the deployment processes to the other team. That time we have to do things manually.

In our current setup, there isn't an option for auto-scaling. We have a fixed capacity, so there's no need to adjust it on the fly.

We've pre-calculated the capacity needed and proceed with deployments based on that. There really isn't an option to increase capacity as needed.

Regarding Digital.ai, you have to make automation a priority. You can integrate multiple tools with it, which you can use for various automation. However, it's not easy; you can't just get the software and start using it from day one.

You have to learn how to use YAML files, how to integrate other applications, and how to create different tasks, like deployments, Jira tickets, or sending emails. There's a lot to learn, so you have to understand the process as well.

I would recommend it. Overall, I would rate the solution a seven out of ten.