This article is based on analysis originally published on devops.com (June 2020). Market figures and survey data reflect conditions at that time.
Why CI/CD Matters Now
Software teams are under relentless pressure to ship faster without sacrificing quality. Frequent releases keep customers engaged and allow organisations to respond to competitive threats more quickly. To meet this demand, engineering teams have widely adopted DevOps culture paired with Continuous Integration and Continuous Delivery (CI/CD) automation. Analyst firm IDC projected the worldwide DevOps software market would reach $8 billion by 2022, up from $3.9 billion in 2017—a figure that reflects the scale of investment organisations were already committing to this space.
Despite the momentum, actual CI/CD adoption in practice has lagged behind the conversation around it. Skill shortages, organisational inertia, and the complexity of stitching together disparate toolchains all slow progress. Many teams treat DevOps as a trend to follow rather than a set of principles to internalise—and the results are predictably underwhelming. A growing counter-trend is vendors delivering unified, integrated DevOps platforms that reduce the integration burden on engineering teams.
What Is CI/CD?
CI/CD is an umbrella term covering three related but distinct practices:
Continuous Integration (CI)
Every time a developer commits and pushes code to a shared repository, the CI system automatically rebuilds the affected branch and runs the relevant test suite. The goal is to surface integration problems immediately—before they compound into larger defects. CI makes it safe to merge code frequently because regressions are caught at commit time rather than at release time.
Continuous Delivery (CD — Delivery)
Continuous delivery extends CI by automating the build, test, and packaging stages so that a release-ready artefact is always available. Deploying to production remains a deliberate, human-triggered action, but the pipeline ensures that anything deployed has already passed all automated quality gates. Teams can ship on demand rather than on a fixed schedule.
Continuous Deployment (CD — Deployment)
Continuous deployment takes automation one step further: every commit that passes the CI/CD pipeline is automatically released to production without manual approval. This requires a high degree of confidence in the automated test suite and monitoring infrastructure. It is less common than continuous delivery and is typically seen in mature, high-throughput engineering organisations.
Why Adopt CI/CD?
- Faster delivery: Automating build, test, and deployment shortens the cycle time between writing code and users benefiting from it.
- Consistent quality gates: Making the CI/CD pipeline the single path to production ensures every change is tested against the same standards. This reduces human error and prevents untested builds from reaching production.
- Rapid bug detection: Because each small commit is tested independently, defects are easier to isolate. Teams embrace a fail-fast, learn-fast approach rather than discovering issues late in a release cycle.
- Competitive advantage: Organisations that ship frequently can respond to customer feedback and market changes faster than those locked into longer release cadences.
- Developer and ops alignment: A shared pipeline enforces standard environment configurations and automated rollback procedures, reducing friction between development and operations teams.
Anatomy of a CI/CD Pipeline
A CI/CD pipeline is a structured, automated workflow that moves code from a developer’s commit through testing and into production. When implemented well, it minimises manual handoffs and provides fast feedback at every stage. The typical stages are:
1. Test Automation and CI
New code is integrated with the main branch. A CI server automatically runs unit tests and produces packaged artefacts. Failures here block progression through the pipeline.
2. Build Automation
Build artefacts—such as Docker images and dependency bundles—are pushed to a registry (for example, Amazon ECR, Docker Hub, or a private container registry). This stage produces the immutable, versioned artefact that will be promoted through subsequent environments.
3. Alpha Deployment
Changes are deployed to a developer-facing environment. This is where engineers inspect how their changes interact with the rest of the system and verify nothing in the main branch has been broken.
4. Beta / QA Deployment
The build is deployed to a staging environment where testers can perform manual and exploratory testing. Automated tests alone cannot catch every class of defect, and this stage provides a final quality check before production.
5. Production Deployment
The validated artefact is released to production. In a continuous delivery model this is a manual trigger; in continuous deployment it happens automatically after all prior gates pass.
What the Market Data Shows About CI/CD Adoption
Several surveys conducted around 2019–2020 painted a nuanced picture of where the industry actually stood:
- A survey of 500 software testers by test automation company Mabl found that 53% of respondents said their team uses continuous integration, 38% use continuous delivery, and only 29% have adopted continuous deployment.
- A Codefresh survey showed that roughly one-third of companies had automated more than half of their workloads, but only approximately 1% had automated the entire CI/CD process. A plurality of teams had automated less than 10% of their pipeline.
- Codefresh also surveyed 130 developers at KubeCon + CloudNativeCon Europe 2019 and found that nearly 32% did not use any CI/CD platform at all. Sixty percent said their organisation had yet to fully leverage automation to accelerate software development and deployment.
The data collectively points to a large gap between awareness and practice—a market opportunity that unified platform vendors have moved to fill.
The GigaOm Market Radar Report for Enterprise CI/CD (published around this period) evaluated solutions from AWS, CircleCI, CloudBees, Codefresh, GitLab, IBM, JFrog, Micro Focus, Microsoft, Red Hat, and XebiaLabs, rating them against multiple criteria. The report identified JFrog, Codefresh, and GitLab as outperformers in its analysis.
Cloud and DevOps: A Symbiotic Relationship
Cloud infrastructure and DevOps practice reinforce each other. At its core, cloud computing enables infrastructure to be provisioned programmatically—described as code or templates and spun up repeatably on demand. This aligns directly with the DevOps principle of automating every stage of the software delivery lifecycle.
The dynamic, elastic nature of cloud infrastructure is especially well-suited to CI/CD workloads, which are inherently bursty: build queues spike when many developers commit simultaneously and go quiet overnight. Cloud resources can scale automatically to match demand, eliminating the need to provision and maintain dedicated build servers sized for peak load. Organisations pay for what they use and avoid stranded capacity.
Beyond infrastructure elasticity, cloud platforms provide:
- Managed services that reduce operational overhead for databases, queues, and container orchestration.
- Global reach that allows CI/CD pipelines to deploy to multiple regions without building a dedicated data centre presence.
- Built-in security tooling including identity management, secrets management, and network policy enforcement.
- Infrastructure as Code (IaC) support through services and tools such as AWS CloudFormation, Terraform, and Pulumi, enabling repeatable, auditable environment creation.
Cloud Deployment Models for DevOps Teams
Public Cloud
The cloud provider hosts and manages all infrastructure. Teams access resources over the internet. Public cloud is the fastest way to get started with scalable CI/CD infrastructure and eliminates the capital expenditure of owning servers.
Private Cloud
Compute, storage, and networking resources run in a dedicated environment, either on-premises or in a provider’s data centre, reserved for a single organisation. Teams choose private cloud primarily for regulatory compliance, data sovereignty requirements, or the need to process highly sensitive data.
Hybrid Cloud
A combination of private and public cloud services, connected so that workloads can move between them. For CI/CD specifically, a hybrid model allows organisations with existing on-premises pipeline infrastructure to burst build workloads into the public cloud during peak periods without a full migration. Sensitive artefacts and data remain in the private environment while less-sensitive workloads run in the public cloud.
Key Benefits of Combining Cloud and DevOps
- Higher developer productivity: Self-service access to development and test environments removes dependency on operations teams for environment provisioning.
- Faster time to market: Cloud-hosted pipelines eliminate infrastructure bottlenecks that delay builds and deployments.
- Reduced maintenance burden: Infrastructure as Code and managed cloud services reduce the operational overhead of maintaining build and test infrastructure.
- Improved security posture: Modern cloud platforms provide identity-based access controls, automated compliance checks, and auditable change histories that reduce the attack surface compared to manually managed environments.
- Resilience and high availability: Cloud-native architectures—stateless services, automated failover, multi-region deployments—improve application uptime and align with DevOps reliability goals.
- Elastic scale: Infrastructure scales to meet demand automatically, supporting growth without manual capacity planning.
Choosing a DevOps Cloud Platform
The DevOps tooling landscape is crowded. When evaluating cloud DevOps platforms, engineering and platform teams should consider:
- Integration breadth: Does the platform integrate natively with your source control, artefact registry, container runtime, and deployment targets?
- Pipeline flexibility: Can you define pipelines as code, version them, and reuse them across projects?
- Security features: Does the platform provide vulnerability scanning for artefacts, secrets management, and role-based access control?
- Observability: Does it offer pipeline metrics, audit logs, and deployment dashboards out of the box?
- Vendor lock-in risk: Are the pipeline definitions portable, or tightly coupled to a single cloud provider’s proprietary format?
- Support for containers and Kubernetes: Given the prevalence of containerised workloads, native Kubernetes integration is increasingly a baseline requirement.
The shift from measuring “How long can we run without an outage?” to “How quickly can we deliver stable new functionality?” captures the cultural change that cloud and DevOps together enable. Organisations that treat CI/CD as a foundational engineering discipline—rather than a trend to adopt superficially—are best positioned to sustain delivery speed at scale.