Software development and operations have historically operated as separate functions with separate priorities. Developers optimise for shipping features. Operations teams optimise for stability. When these goals conflict, delivery slows, incidents multiply, and fixing one problem creates another elsewhere. DevOps emerged as the structural response to this tension: a set of practices that unifies development and operations around a shared goal of reliable, fast delivery of working software.
This is not a new concept, but the gap between teams that have genuinely adopted DevOps practices and teams that have adopted DevOps vocabulary is wide. The 2024 DORA State of DevOps Report found that elite engineering teams deploy 973 times more frequently than low performers, with a change failure rate below 5%. That performance gap is not explained by better tooling. It is explained by the depth to which DevOps practices are embedded in how the team actually works.
This guide covers the core DevOps practices, how to measure whether they are working, and what the toolchain looks like at each layer, written for both the engineers implementing these systems and the engineering leaders accountable for delivery performance.
What DevOps Actually Changes
DevOps is often presented as a toolchain decision: adopt Jenkins, Docker, and Kubernetes and you are doing DevOps. The toolchain matters, but it is the least important part of the change.
What DevOps actually changes is how development and operations share ownership of the delivery pipeline. In a pre-DevOps model, developers hand off code and operations teams manage deployment and infrastructure. Failures in production are operations problems. Deployment delays are operations problems. This split creates a feedback gap: the engineers who know the code most deeply are not the ones managing it in production, and the engineers managing production do not have context on the code.
DevOps closes this gap. Shared ownership of the pipeline (from code commit through production monitoring) means that issues surface earlier, feedback loops are shorter, and the cost of a bad deployment is lower because the team that caused it is also the team that fixes it.
Core DevOps Practices
1. Continuous Integration
Continuous integration (CI) is the practice of regularly merging code changes into a shared repository, where automated tests run immediately on each commit. The goal is to surface integration failures while they are still small and cheap to fix.
Without CI, integration becomes a batch activity that happens at the end of a sprint or before a release. By that point, multiple developers' changes have diverged, conflicts are complex, and failures are hard to attribute. CI keeps the codebase in a continuously integrated state so that each change is validated in context. For a deeper breakdown of how CI fits into the delivery architecture, see continuous integration in software maintenance.
2. Continuous Delivery and Deployment
Continuous delivery (CD) extends CI by automating the path from a passing test suite to a production-ready build. With continuous deployment, that path extends all the way to production automatically, without manual approval gates for each release.
The distinction matters in regulated environments. Many banking, healthcare, and government teams use continuous delivery (automated to a staging environment, manual approval before production) rather than continuous deployment. Either approach is consistent with DevOps principles. The key is that the deployment process is automated, tested, and repeatable. Not every change needs to ship immediately.
3. Infrastructure as Code
Infrastructure as Code (IaC) means managing servers, networks, and environments through version-controlled configuration files rather than manual provisioning. Tools like Terraform, Ansible, and Pulumi let teams define infrastructure the same way they define application code: in a repository, with review, with automated testing, and with a full history of every change.
The practical benefit is reproducibility. When a new environment needs to match production exactly, IaC makes that straightforward. When infrastructure configuration causes an incident, IaC makes it auditable.
4. Automated Testing
Testing automation is the quality gate that makes fast deployment safe. A practical automated testing strategy covers unit tests at the component level, integration tests at service boundaries, end-to-end tests across critical user journeys, and regression tests that protect existing functionality from new changes.
Automated testing does not replace exploratory testing or user acceptance testing. It replaces the repetitive, manual work that slows every deployment cycle while accumulating risk. These are the checks that no one has time to do manually on every commit but that matter every time something ships.
5. Monitoring and Observability
Monitoring tells you when something is wrong. Observability tells you why. Effective DevOps teams instrument their systems to collect logs, metrics, and traces that give real-time visibility into system health: not just uptime, but latency distributions, error rates, queue depths, and dependency behaviour.
The business case for observability is direct. The 2024 DORA State of DevOps Report lists mean time to recover (MTTR) as one of the four metrics most predictive of overall engineering performance. Teams with poor observability have slow MTTR. Teams with good observability can identify and isolate failures in minutes rather than hours.
6. Version Control as the Foundation
Every DevOps practice depends on version control being used well. Application code, infrastructure configuration, deployment scripts, and environment definitions should all live in version control. Branch strategies, code review requirements, and commit discipline are not optional process overhead. They are the foundation that makes everything else auditable, reversible, and collaborative.
Measuring DevOps Performance
For engineering leaders, DevOps practices only matter if they produce measurable outcomes. The four DORA metrics give the clearest system-level view of DevOps performance:
For a full breakdown of how to set baselines and track these metrics over time, see the DORA metrics guide. Engineering teams using Scrums.com can track all four DORA metrics alongside sprint completion rate and PR cycle time in real time, giving leaders continuous visibility into where the DevOps implementation is working and where it is creating friction.
DevOps Toolchain Essentials
The DevOps toolchain supports each phase of the delivery pipeline. The specific tools matter less than how they fit together. A well-integrated toolchain with fewer tools outperforms a fragmented one with more.
For a more detailed breakdown of toolchain options by use case, see DevOps tools for your workflow.
Frequently Asked Questions
What are the most important DevOps practices?
The practices with the highest direct impact on delivery performance are continuous integration, continuous delivery, and automated testing, because they address the most common sources of slow delivery and production failures. Infrastructure as Code and monitoring and observability follow closely, as they determine how repeatable environments are and how quickly teams can identify and resolve incidents.
How do you measure whether DevOps practices are working?
The four DORA metrics (deployment frequency, lead time for changes, change failure rate, and mean time to recover) are the standard for measuring DevOps effectiveness at the team level. They measure system outputs rather than individual activity, which makes them more reliable as indicators of genuine delivery performance.
What is the difference between DevOps and Agile?
Agile is a framework for how development teams plan and deliver work in iterations. DevOps is a set of practices focused on the pipeline from development to production, including deployment, infrastructure, and operations. The two are complementary. Most high-performing teams practise Agile within their development process and DevOps across their delivery pipeline.
What is continuous delivery versus continuous deployment?
Continuous delivery automates the build and test pipeline to produce a production-ready release, but a human approves each deployment to production. Continuous deployment extends this to deploy automatically on every passing build, with no manual gate. Continuous delivery is more common in regulated industries where approval processes are a compliance requirement. Both approaches are consistent with DevOps principles.
How long does DevOps adoption take?
The toolchain can be established within weeks. The cultural change (shared ownership of the delivery pipeline, developers on-call for their own code, operations teams participating in sprint planning) takes longer. Most teams see measurable improvement in deployment frequency and change failure rate within the first quarter of serious adoption, with continued improvement as practices mature.
If you are building or assessing your team's DevOps implementation, Scrums.com gives engineering leaders real-time visibility into the delivery metrics that matter: DORA metrics, sprint health, and cycle time across your entire engineering organisation.
For hands-on support implementing DevOps practices in your engineering organisation, start a conversation with our team.
