DevOps is a way of building and running software in which the people who write code (development) and the people who run it in production (operations) work as one team with shared goals. It relies heavily on automation: code changes are built, tested and released through automated pipelines, and infrastructure is often defined in code so it can be created the same way every time. The aim is to release small changes frequently and reliably, instead of large, risky releases a few times a year.
At a glance
- DevOps is a set of practices and a culture, supported by tools, not a single product.
- Core practices include version control, continuous integration and delivery (CI/CD), automated testing, infrastructure defined as code and monitoring.
- Teams release smaller changes more often, which tends to make each release less risky and easier to roll back.
- Shared responsibility for running software in production is central: teams that build a service help keep it running.
- Security checks are increasingly built into the pipeline, an approach often called DevSecOps.
What problem it solves
In a traditional setup, developers write code and hand it to a separate operations team to deploy and run. Developers are rewarded for shipping features; operations is rewarded for stability. Releases are large and infrequent, handoffs are manual, and when something breaks the two groups can spend more time assigning blame than fixing the problem. Environments set up by hand drift apart, so code that worked in testing fails in production.
DevOps tackles that by aligning both groups around the same outcomes and automating the path from code to production. Small, frequent changes are easier to test and roll back. Automated pipelines remove manual steps that cause errors. Shared monitoring means everyone sees how the software behaves for real users. For the business, the payoff is usually faster delivery of improvements with fewer outages caused by changes.
How it works
Version control. Code, and often configuration and infrastructure definitions, lives in a version control system so changes are tracked and can be reverted.
Continuous integration (CI). Developers merge changes frequently. Each change triggers an automated build and tests, catching problems early.
Continuous delivery or deployment (CD). Changes that pass tests move through an automated pipeline to staging and production, either with a manual approval step (delivery) or automatically (deployment).
Infrastructure as code. Servers, networks and cloud resources are defined in files and created by automation, so environments are consistent and repeatable. This fits naturally with cloud computing and cloud-native designs built on containers, which container management platforms orchestrate.
Monitoring and feedback. Teams watch application performance and errors in production and feed what they learn into the next change. Documented procedures, such as runbooks, help whoever is on call handle common issues consistently.
Built-in security. Code scanning, dependency checks and application security testing run inside the pipeline, so security reviews keep pace with frequent releases.
Culture. Shared goals, blameless reviews after incidents and teams that own a service from development through operation are as important as the tooling.
When it matters for buyers
- When you build or customize software. How quickly and safely you can release changes affects customers and staff directly.
- When hiring a development partner or MSP. Their pipeline, testing and release practices shape the quality of what you get.
- When moving to the public cloud. Cloud platforms reward automation; manually built cloud environments can be costly and inconsistent. Our public cloud overview covers the platforms these pipelines usually run on.
- When outages follow releases. Frequent change-related incidents are a sign that testing and deployment need automation.
- When security reviews slow releases. Moving checks into the pipeline can reduce the bottleneck.
- As part of digital transformation. Faster software delivery is often a prerequisite for changing customer-facing processes.
Questions to ask vendors
- How do you manage source code and track changes to it?
- Describe your release pipeline: what’s automated, what’s tested and who approves production releases?
- How often do you deploy, and how do you roll back a bad release?
- Is infrastructure defined as code, and will we have access to those definitions?
- Which security checks run in the pipeline, and how are findings handled?
- How do you monitor applications in production, and who is on call?
- If we change partners, what will we receive: code, pipeline definitions and documentation?
How it differs from IT service management (ITSM)
IT service management (ITSM) is about delivering and supporting IT services to users through defined processes: handling incidents and requests, controlling changes and managing assets. DevOps focuses on how software is built and released, with heavy automation and shared ownership between development and operations. The two can clash when ITSM change approvals are slow and manual, but they increasingly work together: automated pipelines can record changes in the ITSM tool, and standard low-risk changes can be pre-approved. Many organizations use ITSM for support and governance and DevOps practices for software delivery.