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Many teams are reporting that development velocity is stuck; they've reached a plateau in moving to more and more releases per month, and now need help on how to improve. According to analyst research, 40% of software development team's top priorities relate to speed/automation, so our overriding vision for Continuous Delivery is to help these teams renew their ability to accelerate delivery.
GitLab Continuous Delivery enables organizations to have the latest changes to their software ready for production at any time. Even beyond that, organizations are also able to deploy to production automatically using the Continuous Deployment model.
GitLab provides a complete toolset for organizations to use to improve their deployment frequency and evolve their DevOps capabilities. Teams are able to use org-level standards for delivery and deployment while also ensuring teams are able to move quickly and make updates to their software continuously as desired.
GitLab Continuous Delivery also leverages other GitLab platform capabilities to streamline setup, deploy to different environments, monitor or even simulate the impact of changes to production, and help improve organization performance.
We follow the well-known definitions from Martin Fowler on the difference between continuous delivery and continuous deployment:
Infrastructure Provisioning and Infrastructure as Code, using solutions like Terraform or other provider-specific methods, is an interesting topic that relates to deployments but is not part of the Continuous Delivery category here at GitLab. For details on solutions GitLab provides in this space, take a look at the category page for our Infrastructure as Code team.
For deployment to Kubernetes clusters, GitLab has a focused category called Auto DevOps which is oriented around providing solutions for deploying to Kubernetes. Check out their category page for details on what they have planned.
We are working on a similar experience for non Kubernetes users, starting with Streamline AWS Deployments that will automatically detect when users are deploying to AWS and will connect the dots for them.
This category is currently at the "Complete" maturity level, and our next maturity target is Lovable (see our definitions of maturity levels). Key deliverables to achieve this are:
Auto Deploy (AutoDevOps Flow)
Because CI and CD are closely related, the competitive analysis for Continuous Integration is also relevant here. For how CD compares to other products in the market, especially as it relates to pipelines themselves, also take a look there.
In our conversations with industry analysts, there are a number of key trends we're seeing happening in the CD space:
Cloud adoption of CI/CD is growing, extending capabilities for deploying to cloud environments, including Kubernetes and other modern container architectures are a key metric. While cloud migration is accelerating and more teams are adopting it, on-premises and legacy hardware environments remain.
Users are looking for the ability to not just measure platform stability and other performance KPIs post-deployment, but also provide functionality such as automated release-readiness scoring based on analysis of data from across the digital pipelines. Tracking and measuring customer behavior, experience, and financial impact, after deployment via gitlab#37139 solves an important pain point.
Metrics are a primary source of quantifiable feedback, which is a key objective of agile and DevOps methodologies. Development teams that collect and analyze metrics understand successes, failures and opportunities for improvement better than their peers. Mature development teams actively monitor metrics data and compare results against baselines, which can be industry benchmarks or constant improvements against past results of the individual team. We are actively working on supporting DORA4 metrics as an integral part of GitLab which will allow you to gain efficincy and stability insights into you softwarre development lifecycle.
Progressive Delivery allows you to deploy code incrementally and target the audience that will receive the new code based on user segments and environments. By doing so, it enables experimentation with reduced risk. Progressive Delivery builds on the foundations laid by Continuous Integration and Continuous Delivery. Related categories to this theme are Feature Flags and Advanced Deployments. To read more about this see RedMonk's post.
The ability to monitor deployments and automatically halt/rollback deployment in case of exceeding a specific error rate is frequently mentioned by CS and in the sales cycle as a feature teams are looking for. This will be implemented via gitlab&3088. We have recently added the ability to rollback automatically in case a critical alert is raised via gitlab#35404 (Complete) and will follow up with adding a notification when an auto-rollback accord via gitlab#292019.
The most requested customer issue is gitlab#5902 which adds the ability to create Deploy Tokens with functionality similar to the Deploy Keys, where the admin can create and assign Deploy Tokens per project which will grant non-personal registry-only access to the images without needing an extra seat.
Adding a check for maximum commits before merge via gitlab#26691 is our most popular internal issue. This adds a validation check, to make sure that your merge request is not too far behind master before merging in order to avoid breaking master
Our top vision item is to Natively support hypercloud deployments, and specifically deploying to AWS we want to help make it easier and quicker to get started and deploy to any one of the big cloud providers using GitLab's CI/CD.
We are planning to research user needs for multi-cloud deployments. If you are interested in participating in this user research, please leave a comment in ux-research#1249.