My use case for Digital.ai Release is that I work for an insurance company on a very big project that develops multiple different pieces of software. We use Digital.ai Release to move our software from dev to test, to pre-production. I create release packages, installing different artifacts all at the same time, doing handoff approvals, and it sends emails and Teams messages. It interacts with Jira, logging Jiras for auditing purposes and all those kinds of things.

The features I find most valuable in Digital.ai Release are the integration with MS Teams, because we have MS Teams channels that publish or push notifications to that. When we start deployments, it sends a notification to the people that we are doing a deployment to their environment. It notifies them when the deployment is started, completed, or if attention is required.

I also appreciate the fact that it has plugins for Bamboo and I use lots of Gradle and JSON scripts, and we do SQL upgrades as well, triggering Flyway scripts via Bamboo, along with the integration with XLD and Jira; it's all Atlassian software.

Regarding environment management capabilities, Digital.ai Release is mostly useful for me, as it is more application related and that is managed via my XLD dictionary. We have one artifact that is environment agnostic, which has placeholders that correspond to my XLD keys and values, and at deployment time, it substitutes the placeholders with those environment specific values. We don't need to make a specific deployment artifact for dev, test, or production; it is all the same artifact using environment variables, ensuring what we take to production is what was tested.

Based on my experience, I would like to improve Digital.ai Release by exploring its cloud capabilities as we are currently in the middle of migrating to the cloud, but I actually have no idea what Digital.ai's cloud capabilities are.

As for additional functionality I would like to add to Digital.ai Release, I can't comment on that at the moment, but I think plugins for other deployment tools such as PDQ Deploy, which we use for Windows applications, could make my life easier.

I have been working with Digital.ai Release for about three years now.

My overall impression of the stability of Digital.ai Release is that it is good, although my problem lies with where we deploy to, which is currently not stable at the moment. We deploy most of our stuff to an old IBM WebSphere, which is being deprecated. I think it's important to note that the stability issue might actually be our fault as we need to move over to Liberty and all that kind of stuff.

From my perspective on scalability for deployment, I would rate it as very good, giving it an eight.

Regarding tech support from Digital.ai Release, I would rate them high because as a big multinational company working with people's money, it is crucial to have support, high availability, data integrity, and security, which this product ticks all the boxes.

Positive

The initial setup process for Digital.ai Release was very straightforward, and you just start playing around creating your own templates. You need to play around to learn how to use it, which is part of the fun.

In terms of competitors, I don't have any current experience with anything else other than Digital.ai Release on that scale. I have only dealt with individual technologies such as Ansible, which can also integrate with Digital.ai Release.

I provided a review on PeerSpot about Digital.ai Release two years ago, where I shared my opinion about Digital.ai Release.

I am still working with Digital.ai Release and we still use their product. I have no idea about the pricing for Digital.ai Release, as I don't manage financials.

Overall, I would give Digital.ai Release a rating of nine out of ten; there's always room for improvement, but it's really good. I can definitely recommend Digital.ai Release to other users.

I am Jane-Marie Chuldron, working as a software configuration manager for Sanlam, and my email is jane-marie.chuldron@sanlam.com.za. I am fine with my review on PeerSpot being published with my personal name as my opinion, without contact details or my company name.

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.