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Software developers and the operational teams that support them are under more pressure than ever to deliver higher quality faster. In an increasingly competitive SaaS world, data security issues and downtime present more business risks than ever.

woman uses devops maturity models to fix bugs in a software

So, it’s no surprise that organizations are adopting a DevOps model to improve the quality and speed of deployment. There is no single, straightforward path to DevOps implementation. However, understanding DevOps maturity models provides guideposts to measure progress along your journey.

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Table of Contents

What is DevOps maturity?

DevOps maturity is how organizations can assess how far their implementation of a complete DevOps model has progressed. It involves assessing the implementation of specific DevOps processes and practices and measuring their effectiveness.

Since DevOps is a model for the development and deployment of software, measuring DevOps maturity involves the assessment of practices across multiple teams, processes, metrics, and technologies.

There is no definitive state of DevOps maturity. It can vary widely depending on your organization‘s size, the software’s complexity, and implementation goals from one scenario to another. In general, however, DevOps maturity means encouraging a collaborative culture, building agility, and creating feedback loops that improve the speed and quality of deployments.

How to Gauge DevOps Maturity

Gauging DevOps maturity is not always a case of hard and fast numbers. Some aspects of DevOps maturity are more qualitative than quantitative. However, there are core factors that should be incorporated into any DevOps maturity assessment.

How to Gauge DevOps Maturity. Automation Throughout the Software Development Life Cycle. Agility in Workflows and Processes. Data-Informed and Metric-Driven. Collaboration and Overall Culture

Collaboration and Overall Culture

Collaboration between the different arms of a software development team, from developers to QA and operational roles, is critical to a successful and mature DevOps implementation. This can also extend to other stakeholders, such as product design, InfoSec, and customer success.

In a mature DevOps team, accountability for shared objectives and key performance indicators (KPIs) is found. The team works together to experiment and try new things, and open and honest communication is encouraged.

Team members are empowered to own their own areas of responsibility, and continuous improvement is the name of the game.

Automation Throughout the Software Development Life Cycle (SDLC)

The general goal of DevOps is to produce better software faster. So, automation is a critical component because it frees up the team to operate strategically rather than getting bogged down in manual processes.

Mature DevOps teams have incorporated automation across builds, deployment, and testing. They have integrated tools wherever possible to improve insights and automation, and they practice infrastructure as code (IaC) to enable faster scaling and provisioning. The delivery pipelines of these teams are well-defined and reliable.

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Agility in Workflows and Processes

DevOps maturity also means that processes across software development and all the operational components that support it are efficiently integrated. It allows all arms of the team to work together faster and gain the agility needed to produce quickly and effectively.

Mature DevOps teams leverage agile methodologies, focusing on continuous improvement through frequent iterations. The development, testing, and deployment processes are clearly defined and fully integrated for various hand-off points.

When incidents do arise, these workflows make resolution faster and easier. Bigger changes are managed effectively to vastly reduce risk and minimize disruption.

Data-Informed and Metric-Driven

DevOps means taking a data-driven approach to the management of the entire SDLC. Metrics are used to assess and improve all processes along the way. The focus of analysis is always on further optimization.

Mature DevOps teams are aligned on KPIs and look at them regularly to assess and improve performance. This feeds into the culture of transparency and enables effective decision-making. Tools like retrospectives and reviews are used to identify potential improvements, while machine learning is often used to automate the identification of trends.

The Benefits of DevOps Maturity

Accelerated Software Delivery

DevOps maturity results in a faster time-to-market for products, bug fixes, and other updates. Since DevOps focuses on breaking down the walls between different areas of development and operations teams, collaboration and continuous improvement results in increased speed across all processes.

Better Quality Software With Increased Reliability

A mature DevOps team can also be more confident in the quality of their product. With automation and streamlined processes, issues are identified and addressed at a much faster rate. This includes bugs in software functionality and wider issues with security threats and system vulnerabilities.

Mature DevOps teams can, therefore, tie their efforts directly to bottom-line business results like customer satisfaction and large cost savings.

Increased Innovation

Lots of factors in a DevOps model feed into overall improvements in innovation. Mature DevOps teams spend less time on manual processes, are more open and collaborative, and feel more comfortable with experimentation.

This overall culture leads to a motivated development team that engages in idea-sharing and continuous improvement, leading to more innovation and, ultimately, better products.

What is a DevOps maturity model?

DevOps maturity, just like DevOps itself, does not have a singular definition. It’s more of a journey than an end goal, and it looks different from one organization to the next.

The same is true of DevOps maturity models. There are multiple models you can follow with varying numbers of stages and differing criteria from one stage to the next. Overall, however, a DevOps maturity model is a way to define different stages of your journey and assess your progress toward increasing maturity levels.

Without a maturity model, the implementation of DevOps can quickly descend into chaos. There are so many moving parts to track, and a maturity model helps you implement DevOps consistently, methodically, and measurably.

Common criteria along different stages of a DevOps maturity model include continuous delivery, automation, culture, and KPIs. It’s a good idea to explore various maturity models to find one that best suits your products, industry, and team structure.

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The Five Stages of DevOps Maturity

One of the most popular DevOps maturity models involves five core phases. They’re often named differently from one source to the next, but the core principles of each stage rarely vary too widely.

DevOps Maturity Metrics. Deployment Frequency. Change Failure Rate. Time to Restore Service. Cycle Time. Mean Time to Recovery. Code Coverage. Customer Satisfaction

1. Ad Hoc

Technically, the first phase is pre-DevOps or no DevOps at all. Some organizations may incorporate this specific phase as a preparatory stage to implement a defined plan. In this early stage, development and operations teams often work separately.

A focus on deploying software as quickly as possible may dominate the agenda, but without the processes, collaboration, and automation in place to achieve this effectively.

In this phase:

  • Automation is almost nonexistent.
  • Dev teams, operations, data security, and product work are prioritized separately.
  • A semi-agile or complete reliance on waterfall methodologies may still be in place.
  • Work such as infrastructure management, testing, and security checks are manual.
  • Bugs and fixes take up more time than new feature development.
  • Feedback loops are ad hoc, inconsistent, or completely nonexistent.

2. Repeatable

Sometimes referred to as “DevOps in pockets” or “Continuous Integration,” this phase is where the basics are established. Automation may begin in some of the more straightforward processes, such as testing. While silos still exist, development and operations are beginning to coordinate more effectively.

In this phase:

  • Agile practices are introduced, and the focus is shifting towards end-user needs and feedback.
  • Environments are managed through version control.
  • Software is still released in big batches, so testing remains challenging.
  • Communication has vastly improved, and teams are mostly on the same page but still largely working in silos.
  • Production interruptions still require manual monitoring and intervention.
  • Security is regularly tested and much earlier on in the process.
  • Increases in automation are beginning to reduce risk.

3. Defined

This stage is also known as “Pipeline” or “Continuous Delivery.” At this stage, development and operations teams are developing a more shared set of objectives and metrics. Automation and feedback loops are becoming well-established. Continuous monitoring is starting to take flight.

Processes are almost all standardized and defined. Overall, the DevOps model is functioning well, and metrics are all improving.

In this phase:

  • Agile mechanisms are fully implemented and working well.
  • Frequent, incremental deployments are favored over large releases.
  • Engineering team members and operations are well-aligned on new releases and bug fixes.
  • Automation of processes, including integration testing and acceptance testing, is achieved.
  • Security tests and updates are integrated into the overall software development workflow.
  • Clear documentation across shared operating procedure (SOP) changes and release notes means engineering and operations are more closely aligned.

4. Managed

This phase is also referred to as “Continuous Deployment,” “Optimized,” or “Blended Architecture.” Its defining characteristics are full implementation of automation, a strong culture of collaboration, and experimentation. This is a highly mature and optimized version of DevOps. The team is now proactive about their approach to all aspects of software development and the standardization of processes has made things far more predictable.

In this phase:

  • The concepts of Minimum Viable Products (MVPs) and tech debt are fully adopted.
  • End-users experience far less frequent interruptions, and there has been a vast reduction in bugs.
  • Agile methodologies are now fully in use and mature.
  • Both development and operations are centered around product thinking over project thinking.
  • Both development and operations now work more closely with product and security for planning and deployment.
  • New releases happen continuously rather than in large, infrequent batches.
  • Automatic systems scaling means the risk of systems failure is vastly reduced.
  • Metrics and KPIs are established cross-functionally to identify areas for improvement.
  • Software deployment is almost entirely automated.
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5. Optimized

Also known as “Continuous Operations” or “Fully Mature,” this maturity phase means that all processes and automation are fully established, and the DevOps model is operating like clockwork.

At this stage, teams can focus on innovation and align more closely with delivering business value through deployments. The engineering and operations teams can respond swiftly to changing needs. They leverage tight metrics and data-driven feedback loops at every development and deployment stage to optimize output.

In this phase:

  • Deployments are occurring multiple times throughout the week with minimal risk.
  • There are almost zero interruptions to service for end-users.
  • The time to deployment from ideation to delivery is reduced to days rather than weeks.
  • Risks and costs are minimal across all areas.
  • Self-service automation is employed across all arms of the team.
  • Innovation and experimentation are the order of the day.
  • Collaboration between development, operations, product, and security has peaked.

How to Measure DevOps Maturity

Cycle Time

Also known as “lead time,” this metric indicates how long your team moves from initial code commit to deployment. As DevOps starts standardizing processes and incorporating automation, this metric should gradually decrease. It’s an indication that you can release faster and respond in a more agile way to changes.

Deployment Frequency

Somewhat related to cycle time is deployment frequency. This metric tracks how often you’re deploying new releases. Again, a higher deployment frequency can show how quickly your DevOps team responds to change and how improved processes impact the deployment schedule.

Change Failure Rate

Any developer or software ops team member will know the pain of deployment failures or rollbacks. The automation and improved testing processes of DevOps lead to lower failure rates. This results in overall reduced costs and better deployments.

Time to Restore Service

Service failures happen, but mature DevOps teams can move much faster to restore service. Seeing the time to restore service metric reduce over time is a great indicator of DevOps maturity. It includes the time it takes from detection right through to fixing.

Mean Time to Recovery

Related to time to restore service is mean time to recovery. Unlike time to restore service, however, this metric focuses on the repair time, making it a related but more specific recovery metric.

Code Coverage

Code coverage refers to how much of your code is covered by automated testing. The higher the percentage, the greater the level of DevOps maturity is indicated as it relates to a strong testing culture with heavy use of automation.

Customer Satisfaction

The reduction in downtime and an overall improved product lead to happy end-users. Improved customer satisfaction is a useful indicator of DevOps maturity and a great way to connect the importance of DevOps implementation back to business results.

Getting Started

DevOps will drastically improve the way your engineering and operations teams work. You’ll soon move faster and better in one motion. Understanding DevOps maturity models ensures that the framework is implemented robustly, so your team sees the results as quickly as possible.



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