DevOps Maturity Model: 5 Levels, Metrics, and Assessment

A DevOps maturity model scores how your organization builds and ships software, then places you on a five-level scale that runs from Initial to Optimized. Teams usually reach for one when their pipelines are automated, their deployments are green, and they still cannot prove to leadership that anything is actually improving. 

 In this blog post, we’ll cover the five levels of DevOps maturity, the metrics that show where you sit, what the standard model gets wrong in 2026, and how to run an assessment that your teams will accept.

What we will cover:

1. What is a DevOps maturity model?
2. What are the five levels of DevOps maturity?
3. How to measure DevOps maturity?
4. Benefits of a DevOps maturity model
5. How to implement a DevOps maturity model?
6. What DevOps maturity looks like once AI is in the pipeline

A DevOps maturity model is a framework that helps organizations assess their current DevOps capabilities and guide their progression toward more advanced, integrated, and automated software delivery practices. 

The model outlines stages of maturity (typically ranging from initial/ad-hoc to optimized/innovative) and evaluates how well development and operations teams collaborate, automate, and align with business goals. It is used for gap analysis, strategic planning, and continuous improvement.

The model gives you a structured path: work out where you are, agree where you want to be, and pick the one constraint worth fixing next. Maturity is not a destination you arrive at. Teams that treat the assessment as an annual audit stall. Teams that rerun it against a real constraint keep moving.

Key aspects of a DevOps maturity model include:

• Culture and collaboration: Covers how development and operations share responsibility, how decisions get made across team boundaries, and how quickly feedback reaches the people who can act on it.
• Automation: Covers CI/CD pipelines, infrastructure as code, test automation, and deployment orchestration to reduce manual effort and errors.
• Processes and practices: Assesses standardization, monitoring, incident management, release strategies, and lean workflows to ensure reliability and speed.
• Measurement and monitoring: Tracks the DORA software delivery metrics, covered in detail below, to drive decisions rather than dashboards.
• Security and compliance: Integrates security practices early (DevSecOps) and ensures governance without slowing down delivery.

By evaluating these processes, tools, culture, and automation, organizations can identify areas that need enhancement and develop strategies to advance their DevOps practices. The goal is to achieve higher efficiency, faster delivery, and better collaboration between development and operations teams.

A DevOps maturity model typically consists of multiple levels, representing incremental stages of DevOps maturity for organizations. The number and naming of levels might vary, but the methodology and general idea stay the same. 

Typically, the five DevOps maturity levels include:

1. Initial
2. Managed
3. Defined
4. Measured
5. Optimized

These level names come from the Capability Maturity Model Integration (CMMI), maintained by the CMMI Institute, a subsidiary of ISACA, currently at version 3.0. CMMI runs maturity levels from Level 0, Incomplete, through Level 5, and levels one to five are Initial, Managed, Defined, Quantitatively Managed, and Optimizing. 

DevOps adaptations, including this one, usually rename the top two. “Quantitatively Managed” becomes “Measured” and “Optimizing” becomes “Optimized”. The substance is the same. If you are being appraised against CMMI itself, use ISACA’s names.

Level 1. Initial

At this stage, DevOps practices are sporadic and unstructured. This model is found in organizations that still operate with old-school processes. 

There is little or no collaboration between development and operations teams, they operate in silos, and methods are mainly manual. This was a standard model in the early 2000s, but organizations are increasingly moving away from this stage.

Level 2. Managed

Organizations at this level have started to adopt DevOps practices in a more organized manner. There is some degree of automation, and teams begin to collaborate, though inconsistently. 

Teams have started to adopt Agile methodologies, but there are no well-defined DevOps practices across the organization. Automation initiatives are based on individuals and best-effort approaches.

Level 3. Defined

DevOps processes are well-defined and standardized across the organization. Automation is becoming the norm, and team collaboration is more consistent and effective. DevOps teams are established, and processes and tools for monitoring and automation are in place.

Level 4. Measured

At this stage, after DevOps processes have been set, organizations actively measure the performance of their DevOps practices. Metrics and KPIs are used to identify areas of improvement, and there is a focus on continuous feedback and optimization. 

Although many DevOps principles have been implemented during this stage, organizations start to realize that they still have far to go to achieve optimal operational benefits.

Level 5. Optimized

Optimized is the highest level of DevOps maturity. Organizations have fully integrated DevOps practices with extensive automation, continuous monitoring, and a culture of constant improvement. 

Development and operations teams work from the same information, and process changes ship without a steering committee. When inefficiencies or issues arise, teams have all the information they need and are empowered to quickly change processes, tools, and practices. 

Measuring DevOps maturity involves evaluating various aspects of your organization’s DevOps practices. The best approach is to use a mix of data-driven metrics and employee anecdotal evidence to measure an organization’s holistic DevOps maturity.

Metrics and KPIs

At the early stages, track the five DORA software delivery metrics. DORA groups them into throughput and instability.

Throughput

• Change lead time: how long a change takes to go from committed in version control to deployed in production
• Deployment frequency: how many deployments you make over a given period, or the time between them
• Failed deployment recovery time: how long it takes to recover from a deployment that fails and needs immediate intervention

Instability

• Change fail rate: the ratio of deployments that need immediate intervention, usually a rollback or a hotfix
• Deployment rework rate: the ratio of deployments that are unplanned and happen because of a production incident

Two notes on naming, because a lot of published guidance is still out of date here. DORA renamed mean time to recovery (MTTR) to failed deployment recovery time in 2023, to separate failures caused by a change from failures caused by something else, such as a data center outage. DORA then added deployment rework rate in 2024, taking the model from the original four keys to five.

DORA is also explicit that these metrics work best applied to one application or service at a time. Blending them across teams, or across a whole organization, can mislead you because the context differs. Measure per service, then look at the spread.

As you move to more advanced stages of DevOps maturity, look into more specific metrics such as mean time to resolve pull requests, the proportion of time tickets spent in an ‘active’ versus ‘inactive’ status, and mean time for failed builds. 

Read more: What DevOps Metrics Should You Be Tracking?

Processes and automation

Assess the standardization and efficiency of your development and operations processes. Evaluate the extent of automation in your continuous integration and continuous deployment (CI/CD) pipelines, testing, and deployment. Most metrics that a DevOps maturity model focuses on relate to automation and processes. 

Automating repetitive tasks gives developers more free time to focus on delivering value and innovation. Processes must be standardized and efficient in a well-structured and mature DevOps environment. As well as examining pre-defined metrics and KPIs, you should also conduct user interviews to assess users’ experiences with organizational processes.

Collaboration and culture

You should also track and measure the level of collaboration and communication between technical teams. Consider cultural aspects, such as adopting a DevOps mindset and the level of collaboration and trust within teams. 

Collaboration is where maturity assessments most often disagree with the metrics. A team can hit good numbers and still route every infrastructure request through one person. Ask who gets blocked, on what, and how often, then compare that against what the dashboard says.

Read more: Improving DevOps Collaboration: Best Practices & Tips

The DevOps maturity model helps organizations assess where they stand in adopting DevOps practices and identify areas for improvement. It provides a structured way to measure progress across culture, automation, processes, and collaboration. 

Continuous improvement and increased efficiency	Primarily, it provides a clear path for continuous improvement and optimization of DevOps practices.  It helps identify inefficiencies and areas for automation, leading to faster and more reliable software delivery.  It also guides organizations in developing a strategic roadmap for DevOps implementation and maturity.
Better collaboration	A shared scorecard gives development and operations teams the same definition of “better”, which is usually the missing piece. Arguments move from whose fault it is to which constraint to fix next.
Better performance measurement	A DevOps maturity model allows organizations to track and measure the performance of their DevOps practices, making it easier to identify areas for improvement.  Having metrics, goals, and success criteria upfront aligns the technical team to a common target.

To implement a DevOps maturity model, follow these steps.

1. Assess current state.
2. Define goals and success criteria.
3. Develop a roadmap to progress to the next stages.
4. Implement changes and measure progress.
5. Foster a DevOps and collaboration culture.

1. DevOps maturity assessment

Before you define a model and levels, conduct a thorough assessment of your current DevOps practices, identifying strengths and weaknesses. Document the current state of things and define aspirations.

For example, let’s look at automation, culture, and value stream metrics:

Category	Metrics	Maturity stages
Automation (CI/CD & infrastructure as code)	Deployment frequency: How often code is deployed to production. Change lead time: Time from a change being committed to version control to it running in production. Build and test automation: Percentage of the build and testing processes automated. Automated environment provisioning: Use of Infrastructure as Code (IaC) for automatic provisioning and configuration. Pipeline failure recovery time: Time taken to resolve issues in CI/CD pipelines.	Initial: Manual processes for deployment and testing. Managed: Partial automation of CI/CD and environment provisioning. Optimized: Fully automated pipelines, IaC in place.
Culture & collaboration	Cross-functional collaboration: Degree of collaboration between development, operations, and other teams (e.g., security, QA). Communication frequency: Use of collaboration tools (e.g., Slack, MS Teams) and frequency of interactions. Feedback loops: How well feedback is integrated from stakeholders (e.g., customers, operations, and other teams). Blame-free environment: Whether postmortems change the system or find a person to hold responsible.	Initial: Siloed teams, limited collaboration. Managed: Periodic collaboration, still role-defined boundaries. Optimized: Fully collaborative, integrated feedback loops.
Value stream metrics (Business impact)	Cycle time: Time taken from idea inception to delivery of business value. Customer feedback frequency: Frequency of collecting feedback from end-users. Business relevance: Whether the team can name the business outcome its last three releases moved. Customer satisfaction (CSAT/NPS): Measures of customer happiness with the product.	Initial: Poor alignment between business and development. Managed: Periodic alignment and feedback collection. Optimized: Continuous alignment, frequent feedback, and iterative development based on feedback.

2. Define goals and success criteria

Next, establish clear goals. Set up different maturity model levels according to your organization’s needs and precise criteria for reaching each subsequent level.

Score each category, such as automation, security, or collaboration, on a scale of one to five. Add the scores and divide by the number of categories for an overall average. 

Then treat that average as a conversation starter rather than a grade. DORA lists blending metrics across different applications and teams as a specific pitfall, and the same caution applies to a maturity score: one number hides the spread, and the spread is where the work usually is. Two teams that both average 3.0 can need completely different interventions. Record the per-category scores alongside the average and keep both.

3. Develop a roadmap to progress to the next stages

With a solid understanding of the current situation and the desired outcome, continue by creating a detailed roadmap outlining the steps needed to advance through the maturity levels and the changes necessary to get there. Craft a detailed roadmap to guide you on this process.

4. Implement changes and measure progress

Starting from your current state, implement changes incrementally, focusing on areas with the most significant impact. Continuously measure your progress using relevant metrics and adjust your strategies as needed. Celebrate small wins to keep morale high and provide the teams with a sense of continuous improvement.

5. Build a culture that survives the roadmap

None of the previous four steps hold without this one. 

Roadmaps survive reorganizations only when the teams running them believe the measurement is fair. Publish the scores, including the bad ones, and let the teams being measured challenge the criteria.

Classical maturity models assume code arrives at human speed. It does not anymore, and that changes which parts of maturity actually protect you.

Spacelift surveyed over 400 infrastructure decision makers for the 2026 Infrastructure Automation Report and found a wide gap between how governed teams believe they are and how governed they are. 86% of infrastructure leaders are confident in their AI governance. Only 30% have a formal policy in place. Meanwhile 78% use AI to generate IaC without review, 76% will apply AI-generated Terraform to production with little or no scrutiny, and 93% have already had at least one AI-caused infrastructure incident.

The report scores organizations on an AI Maturity Index (AIMI) with four stages: Exposed, Fragmented, Outpacing, and Pioneer. Compared with Exposed organizations, Pioneers are:

• Over six times more likely to have fully automated their infrastructure processes
• 12 times more likely to have adopted platform engineering
• Nearly 18 times more likely to have a formal AI governance policy

Here is the part that runs against intuition. Pioneers use AI more, not less: 86% of them generate IaC with AI, against 69% of Exposed organizations. The difference is that they do it inside governed pipelines, so more volume does not become more risk. Among Pioneers, 43% are planning agentic AI adoption within six months.

This maps cleanly onto the classical levels. Automation maturity and policy maturity were nice-to-have when a human wrote every resource block. They are load-bearing once a model writes them.

The seven capabilities that decide whether AI helps

DORA’s AI Capabilities Model, the companion to the 2025 report, names seven capabilities that amplify AI’s impact: a clear and communicated AI stance, healthy data ecosystems, AI-accessible internal data, strong version control practices, working in small batches, user-centric focus, and quality internal platforms.

Notice what is on that list. Five of the seven are practices a maturity model already measures, and only two are specific to AI. The central finding of the 2025 DORA report is that AI amplifies what is already there: strong teams get stronger, and struggling teams find their existing problems magnified. Raising your maturity on the fundamentals is the AI strategy.

Spacelift is an infrastructure orchestration platform that increases your infrastructure deployment speed without sacrificing control. With Spacelift, you can provision, configure, and govern with one or more automated workflows that orchestrate Terraform, OpenTofu, Terragrunt, Pulumi, CloudFormation, Ansible, and Kubernetes. 

You don’t need to define all the prerequisite steps for installing and configuring the infrastructure tool you are using, nor the deployment and security steps, as they are all available in the default workflow.

With Spacelift, you get:

• Policies to control what kind of resources engineers can create, what parameters they can have, how many approvals you need for a run, what kind of task you execute, what happens when a pull request is open, and where to send your notifications
• Stack dependencies to build multi-infrastructure automation workflows with dependencies, having the ability to build a workflow that, for example, generates your EC2 instances using Terraform and combines it with Ansible to configure them
• Creature comforts such as contexts (reusable containers for your environment variables, files, and hooks), and the ability to run arbitrary code
• Drift detection and optional remediation
• Platform teams publish parameterized Blueprints for freely editable stacks and version-pinned Templates for governed ones, and developers provision approved self-service infrastructure from a form.
• Spacelift Intelligence: AI capabilities are embedded in the platform, such as Infra Assistant and Intent, which operate within the same policies and approval flows as everything else, so that teams can adopt AI-driven workflows with the guardrails already in place.

Spacelift was built for platform and DevOps engineers, and software engineers ended up using it too, because self-service infrastructure with your guardrails already applied is faster than waiting on a ticket. Infrastructure decisions happen in one place instead of across five tools and a Slack thread.

If you want to learn more about what you can do with Spacelift, check out this article.

In this blog post, we analyzed a DevOps maturity model for assessing and improving DevOps practices. We reviewed the framework’s typical maturity levels, each representing a stage of DevOps adoption. 

We discussed measuring DevOps maturity, including processes, automation, collaboration, metrics, and culture. Finally, we looked at a high-level plan and the benefits of implementing a DevOps maturity model according to your organization’s needs.

If you are interested in a product that can orchestrate provisioning, configuration, and governance, across your infrastructure, Spacelift is the answer. Create a free account with Spacelift today, or book a demo with one of our engineers.