Digital.ai Release and CloudBees are competing products in the continuous delivery and release automation market. Based on data reviews, CloudBees often has the upper hand due to its extensive feature set despite a higher initial cost.
Features: Digital.ai Release offers comprehensive release automation, integration options, and capability to coordinate deployments, ensuring consistent releases. It supports systems like XLD, Jira, and Bamboo for enhanced process management. CloudBees provides robust CI/CD pipeline management, seamless Jenkins integration, and scalability for large enterprises. It includes advanced automation features, broad integration capabilities, and real-time visibility for error tracking and resolution.
Room for Improvement: Digital.ai Release can improve in areas such as expanding its flexibility, enhancing automation efficiency for larger enterprises, and broadening its integration scope with more third-party tools. CloudBees could benefit from refining its customer service response time, streamlining its setup process, and easing dependency challenges related to plugin and version upgrades.
Ease of Deployment and Customer Service: Digital.ai Release is praised for its efficient deployment processes and responsive customer service. CloudBees provides a streamlined deployment model with substantial Jenkins support, although feedback on customer service is mixed, highlighting the need for improvements in service responsiveness.
Pricing and ROI: Digital.ai Release generally presents a lower setup cost, offering favorable ROI for businesses pursuing quick-win solutions. CloudBees, with a higher upfront investment, delivers long-term value with its advanced features and scalability, making it appealing for companies prioritizing extensive capabilities and future-proofing their operations.
Digital.ai Release has reduced the error rate up to 80%.
The best part is standardizing things, which in the long term will help me reduce costs and improve efficiency.
This means four to five hours saved for one QA on each release, and with multiple QAs doing multiple releases across our three or four different brands, we are saving days within a week.
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.
Our finance team and our infrastructure team reached out to their team members, and they responded within a few hours.
We have a release team to help us with Digital.ai Release.
Digital.ai Release's scalability is very good, as we can add any number of users and expand it organization-wide or to a handful of teams.
Digital.ai Release's scalability seems to be adequate.
The deployment can take a couple of hours or a couple of minutes.
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.
Digital.ai Release is very stable from my perspective.
If possible, we could sort the logs so we can get the exact error on one page.
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.
New users may take time to understand release pipelines and templates, so more guided onboarding tutorials and documentation would help them adapt easily.
I would appreciate standardized training material that would give me hands-on experience.
Digital.ai Release is affordable in terms of pricing and setup cost.
The pricing, setup cost, and licensing for Digital.ai Release are a little expensive when I look at it, especially the enterprise-level licenses.
The integration part is good. We can use multiple things by integrating with CI/CD pipelines, and this is very feasible for us.
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.
Digital.ai Release standardizes the release process across teams.
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.
| Product | Mindshare (%) |
|---|---|
| CloudBees | 3.4% |
| Digital.ai Release | 3.4% |
| Other | 93.2% |
| Company Size | Count |
|---|---|
| Small Business | 2 |
| Midsize Enterprise | 2 |
| Large Enterprise | 18 |
| Company Size | Count |
|---|---|
| Small Business | 2 |
| Midsize Enterprise | 5 |
| Large Enterprise | 8 |
CloudBees provides a highly scalable and secure platform that supports seamless integration and automation across multiple environments. It excels in managing Jenkins instances and offers flexible deployment options, enhancing efficiency for large teams.
CloudBees is recognized for its integration with Jenkins, SonarQube, and Ansible, allowing companies to leverage its robust automation capabilities for continuous integration and deployment. With its strong support for Docker and Kubernetes, teams benefit from streamlined code management and operational efficiency. Its scalable architecture, real-time feedback, and premium vendor support help manage large-scale applications and microservices. Despite its strengths, users report challenges with pipeline setup, service availability, and GUI accessibility, which suggest room for improvement in these areas.
What are the key features of CloudBees?
What benefits should users expect in reviews?
In tech and software industries, companies implement CloudBees for managing complex CI/CD pipelines. Its integration with DevOps tools facilitates automation and workflow optimization. Industries with large teams managing thousands of microservices use CloudBees to maintain high availability, streamline processes, and ensure security compliance, driving efficient production workflows.
Digital.ai Release enhances deployment pipelines, integrating with tools like GitHub and Jenkins. It enables coordination across development, testing, and production while reducing manual efforts, making it ideal for large projects.
Digital.ai Release is designed to automate and orchestrate application deployments, offering features like email approvals, deployment notifications, and system communication with XLD. It supports integration with tools such as Bamboo, Jira, and MS Teams to create standardized deployment processes. While needing a simpler interface for newcomers, it provides efficient handling of environment-specific configurations and process oversight with metrics and data retention. Challenges include the high cost and complexity, with demands for improved mainframe migration support, automated deployment instructions, differentiated pricing by roles, enhanced cloud capabilities, and additional plugins.
What are the key features of Digital.ai Release?Digital.ai Release has found robust implementation in industries managing large-scale deployments, such as software development and IT services. It assists in orchestrating SQL database upgrades, server deployments, and user orchestration while enhancing release documentation and cross-team communication. This makes it valuable for teams requiring integration and logging through tools like Jira in complex projects like artifact installation and continuous delivery environments.
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