DevOps maturity level 2025: How companies measure delivery performance

Why DevOps Maturity Matters in 2025

DevOps has long since established itself as an integral part of modern IT strategies. Companies that want to secure their competitiveness in 2025 will benefit significantly from a high level of DevOps maturity. To remain an industry leader in terms of speed, reliability and innovation, it is no longer enough to evaluate delivery performance on a selective basis. Instead, continuous and standardised improvement processes are becoming increasingly important.

However, many organisations face challenges when it comes to objectively assessing their own DevOps competence. Both at an overall company level and in individual functional areas, the question arises: Where do we stand in comparison to established benchmarks? How can our processes, technologies and corporate culture be systematically evaluated? The following section presents tried-and-tested methods, proven metrics and tried-and-tested approaches that companies can use to measurably increase their DevOps maturity level.

The focus is on tangible results: shorter release cycles, increased system stability, reduced downtimes and faster time-to-market. From start-ups to large corporations, companies can find guidance here to determine their current status, identify areas for action and drive transformation forward in a targeted manner.

Typical stages of the DevOps maturity level

The development of efficient DevOps usually takes place in successive stages - from fragmented individual solutions to comprehensively automated and data-driven processes. Maturity models divide this process into five stages:

• Initial: Many manual activities, hardly any automation, and little exchange between development and operations
• Repeatable: Initial standards and automation are introduced, albeit inconsistently and often depending on the respective team
• Defined: Comprehensive process coordination and standardised tools, automation is the rule
• Managed: Active monitoring of central metrics, cross-functional teams work in an integrated manner
• Optimising: Continuous improvements through evidence-based decisions, high flexibility in innovation implementation

In practice, companies often find themselves in several stages at the same time, depending on the area or team. A differentiated view helps to take targeted measures for further development.

Metrics for delivery performance

Many companies use established metrics to make progress transparent and comparable. In particular, the Four Key Metrics - known from the State of DevOps reports and the book Accelerate - are recognised as a reference for evaluating delivery performance:

• Deployment Frequency: How often do changes reach the production environment?
• Lead Time for Changes: How long is the period from code development to productive deployment?
• Change Failure Rate: How high is the proportion of faulty deployments?
• Mean Time to Recover (MTTR): How quickly are faults rectified?

These metrics can be used to compare progress across teams and identify bottlenecks. In regulated industries, additional metrics such as security lead time or compliance coverage are a useful addition to map industry-specific requirements.

Methods for assessing the DevOps maturity level

There are various ways for companies to determine their DevOps maturity level. The tried and tested methods include

• Self-assessment questionnaires: A simple, resource-saving way to get started, but can involve subjective biases.
• Maturity models from frameworks: Providers such as Deloitte, Microsoft or DORA (DevOps Research and Assessment) deliver structured criteria for reviewing processes, culture, collaboration and automation.
• Automated tool evaluations: By integrating appropriate tools into CI/CD pipelines or monitoring systems, the status can be objectively assessed based on current operational data.
• External assessment: Specialised consultants conduct interviews, workshops and analyses to gain a comprehensive picture.

In practice, a combination of self-assessment and automated data collection achieves the most reliable results. Platforms such as GitLab or Azure DevOps provide support with extensive analysis and reporting functions, enabling a data-based assessment.

Example: DevOps maturity level in a medium-sized organisation

The fictitious "Muster IT GmbH" reflected typical challenges faced by SMEs: isolated IT islands and a lack of standardisation in the tool landscape. in 2023, initial approaches to automation in deployment existed, but errors often went undetected until users provided feedback.

An inventory based on the DORA framework revealed a deployment frequency of just once every fortnight, with a change failure rate of just under 30 per cent. By converting the pipelines, introducing automated tests with Jenkins and targeted monitoring via Prometheus, the lead time was halved and the error rate significantly reduced. The monthly review using clearly defined KPIs and the company-wide transparency of the results were decisive.

This example shows how objective analyses in conjunction with targeted, pragmatic measures - such as the automation of recurring tasks - often have a faster impact than fundamental restructuring.

Continuous measurement: automation as a success factor

Looking ahead to 2025, it is clear that individual point measurements are providing less and less guidance. Today, companies with high standards rely on continuous monitoring of delivery performance and the ongoing elimination of weak points. The decisive factors here are in particular

• Dashboards and automated reporting: systems such as Grafana or Azure Application Insights provide up-to-date performance data across team boundaries.
• Automated quality gates: Integrative tests, security and compliance checks ensure stable, secure deployments without delay.
• Iterative learning: Recurring retrospectives promote a culture of continuous optimisation - based on reliable metrics.

An example of automated measurement of deployment times in CI pipelines can be implemented as follows:

// Example of deployment times in a CI pipeline (pseudo-code) deploy_start = getTimestamp('deploy:start') deploy_end = getTimestamp('deploy:end') deploy_time = deploy_end - deploy_start report('Deployment Time', deploy_time)

Such automated analyses can be expanded flexibly and make bottlenecks in processes immediately visible.

Sources of error and challenges in maturity level monitoring

Misjudgements of the maturity level are often caused by an excessive focus on technical details. Without taking cultural and process metrics into account, the assessment remains incomplete. The most common challenges include

• Self-assessment without a reliable database
• One-sided focus on tools instead of a holistic view of processes
• Little involvement of the specialist departments in the assessment
• Neglect of external benchmarks and tried and tested industry practices

If you address these obstacles early on, you create the basis for robust, meaningful DevOps maturity monitoring.

Best practices for sustainable improvement

Sustainable improvement in the DevOps maturity level is most reliably achieved with a step-by-step model that includes the following aspects:

• Transparency: Disclosure of goals, results and metrics for all stakeholders
• Step-by-step improvements with measurable benefits: Small initiatives with a clear focus on results
• Enablement: Training measures and community building to strengthen skills and motivation
• Automation and self-service: efficiently automate repetitive tasks, decentralise access rights for tools
• Culture and feedback: openly addressing errors and continuously learning from user feedback

Centralised responsibility for maturity level monitoring and close collaboration between IT, operational areas and management promote sustainable further development.

Comparison: Measuring with classic ITSM practices

In contrast to traditional IT service management (ITSM) approaches, DevOps maturity assessment is characterised by a much stronger process orientation and agility. While ITSM structures primarily focus on stability and compliance, a DevOps-oriented approach also emphasises innovation and implementation dynamics. Mature organisations combine both - for example by interweaving ITIL change management with continuous delivery pipelines.

In this way, a clear distinction is maintained between the speed of innovation and the necessary system stability - and the scope for innovation is utilised sensibly without taking stability risks.

Conclusion and outlook

The continuous measurement and improvement of the DevOps maturity level will become a business necessity in 2025. With targeted metrics such as deployment frequency, lead time and change failure rate, tangible progress can be made in terms of efficiency, innovation and employee retention. Success depends largely on the interplay between automation, corporate culture and transparency.

The trend is clearly moving in the direction of automated, data-based maturity assessment. Companies that invest in modern platforms, intelligent tools and competent teams at an early stage will gain sustainable advantages in the dynamic competitive environment of digitalisation.