Mar 24, 2025
Damian Szewczyk
8 minutesOriginally conceptualised by the founder of Conflux Matthew Skelton and advocated by his co-author of Team Topologies: Organizing Business and Technology Teams for Fast Flow, Manuel Pais, DevOps topologies offer a framework for structuring development and operations teams. This structure aims to optimise collaboration, streamline software delivery, and enhance infrastructure management. By adopting an adequate topology, an organisation can improve team interactions, reduce cognitive load, avoid common pitfalls, and implement team types that drive DevOps success.
However, there is no one-size-fits-all approach. Each company must select a topology that aligns with its unique needs. The right structure fosters seamless communication, minimises silos, and accelerates deployment cycles, while a poorly selected one can lead to inefficiencies and bottlenecks, ultimately hindering progress.
To get into the deep dive and fully understand the best approach for your organisational structure and goals, let's explore the nuances of DevOps models and their practical application.
DevOps topologies should be chosen based on a combination of more than one organisational factor to ensure optimal efficiency and collaboration, guiding the team to their engineering maturity. While one topology based on certain DevOps practices may work for some software development and operations specialists, a different team may benefit from other DevOps topology patterns. Key considerations include:
Smaller teams often thrive with an integrated approach, while larger enterprises benefit from a well-defined DevOps team structure.
Company culture plays a crucial role in collaboration, either fostering alignment or creating friction between teams.
The level of automation directly impacts the effectiveness of different topologies, influencing workflow efficiency.
Regulatory obligations may dictate specific team structures to meet industry and regional standards.
Selecting the right topology ensures a streamlined DevOps model, enhancing communication, reducing silos, and driving operational success.
Enterprises can select from a range of proven DevOps topologies, each successfully implemented in real-world scenarios. However, it is crucial to carefully evaluate the strengths and limitations of each approach in the context of specific business needs. While some topologies drive efficiency and collaboration, others, also covered here, have been shown to create bottlenecks and inefficiencies if not properly aligned with organisational goals. Explore the merits and drawbacks of different structures of DevOps teams for modern software delivery.
Proven DevOps structures facilitate seamless collaboration and efficient software delivery. Yet, there are also certain drawbacks of different team designs even in the case of effective topologies. Below are some of the most popular ones.
This operating model fully integrates product development and operations teams, ensuring shared responsibility for deployment, system reliability, and overall software performance. By fostering close collaboration, it enables faster issue resolution, improved efficiency, and a more agile development process.
However, its applicability may be somewhat limited in organisations with deeply ingrained silos, as the successful adoption of this model often requires a significant cultural shift to align teams effectively.
The Ops as a Platform model enhances scalability and efficiency by enabling operations teams to provide developers with self-service infrastructure tools. This allows developers to deploy and manage resources independently, which reduces bottlenecks and increases agility.
For this model to succeed, developers need a strong understanding of operations, which requires thorough training and clear, well-documented guidelines. Without these elements, enterprises may struggle with misconfigurations and inefficiencies, limiting the effectiveness of the approach.
By establishing a DevOps team of specialists with embedded operations expertise, companies can improve automation, streamline infrastructure decision-making, and reduce dependencies on a central operations team. This model accelerates development cycles by fostering autonomy and efficiency.
Yet, while highly effective in smaller or mid-sized teams, it may not scale well for large enterprises with multiple development teams. Its success also heavily depends on the availability of highly skilled personnel, as a lack of expertise can hinder implementation and long-term sustainability.
Pioneered by Google, the Site Reliability Engineering (SRE) model blends software engineering practices with operations, focusing on improving system reliability while maintaining development speed. It achieves this balance by emphasising service-level objectives (SLOs), automation, and proactive incident management.
Although this methodology significantly enhances stability and performance, its practical implementation can be challenging for traditional IT teams. Successfully implementing the SRE model requires a cultural shift and hiring professionals with specialised expertise, which can be challenging for companies unfamiliar with the principles and practices of this approach.
The DevSecOps model integrates security into every stage of the development lifecycle, which allows enterprises to ensure proactive risk mitigation from the outset. By embedding security practices early, organisations can strengthen their overall security posture and enhance compliance, reducing vulnerabilities before they become critical issues. This model relies on automating security policies and incorporating security testing directly into CI/CD pipelines, enabling continuous monitoring and early threat detection.
Despite obvious advantages, as DevSecOps significantly improves security measures, it can also introduce challenges. The additional layers of security, particularly if implemented with overly restrictive policies, may naturally slow down development cycles, creating friction between speed and compliance. To maximise effectiveness, teams must strike the right balance and achieve robust protection without compromising agility.
The topologies known for preventing teams from experiencing the intended benefits of DevOps are commonly known as anti-types. These include:
A conventional approach separates development and operations teams, with developers writing the code and operations specialists handling its deployment and maintenance. This structure hinders collaboration and slows down the feedback loop, impeding the flow of value across the organisation.
Often creating a separate DevOps team, this setup risks becoming an ineffective middle layer, where the DevOps function is disconnected from both development and operations, reducing its potential for driving meaningful change.
This anti-type is adopted by teams who falsely believe that automation can eliminate the need for operations. While automation can indeed significantly streamline processes, minimising the need for manual intervention, the human element and operational expertise remain critical for ensuring reliability, scalability, and security.
This model employs either a single individual or a small group to introduce the DevOps methodology to the company. However, it fails in practice because implementing DevOps effectively requires structural and cultural changes across the entire organisation, not just isolated efforts by a small group or individual.
The proper choice of DevOps topology requires not only a deep understanding of the pros and cons of the available options but also a clear grasp of the specific business context of a company.
On the one hand, it is essential to assess the current level of DevOps adoption within the company, the existing technical expertise, and the budget available for expansion. On the other hand, understanding the precise business needs, goals, and the regulatory environment in which the company operates is equally important. Aligning the topology with these factors is crucial for success.
Furthermore, it is vital to ensure that the selected topology is applied with the necessary flexibility to adapt to the company’s evolving requirements.
As DevOps evolves, adapting new practices to leverage emerging technologies and methodologies, so do its topologies. Patterns involved in the DevOps team topologies are already adapting to the most recent innovations, with some holding the potential to become the industry standard.
Undoubtedly, the rise of artificial intelligence is set to significantly transform DevOps processes and Agile practices in the future. By enhancing automation, AI is expected to streamline workflows and routine tasks even further. Additionally, it has strong potential for improving decision-making with real-time insights.
NoOps, an extreme form of automation adopted by some DevOps teams as discussed earlier, still has the potential to become a trend, despite its downsides. Many businesses are seeking ways to minimise operational overhead, allowing developers to focus exclusively on building applications.
The growing adoption of AI and machine learning applications also fosters the development of MLOps, which specialises in managing model lifecycles, automating ML pipelines, and improving collaboration between data scientists and engineers.
The importance of Git is likely to continue expanding, contributing to the evolution of GitOps. This approach encourages teams to treat Git as the single source of truth, helping to reduce configuration drift and enhance collaboration.
Future DevOps topologies are also expected to integrate hybrid and multi-cloud strategies more extensively, as leveraging multiple cloud providers can enhance scalability, flexibility, and resilience.
The way DevOps teams are structured is also evolving, with new team topologies emerging. Stream-aligned teams, platform teams, complicated-subsystem teams, and enabling teams to work together to improve efficiency and collaboration. At the same time, DevSecOps is making security an integral part of the development process from the outset. Rather than treating security as an afterthought, it is embedded into every stage of the process, with automated security checks ensuring compliance and reducing risks.
Choosing the right DevOps topology is critical for fostering collaboration, improving efficiency, and ensuring smooth software delivery. While proven models, recognised by Matthew Skelton and Manuel Pais, such as Dev and Ops Collaboration, SRE, and DevSecOps, enhance integration and automation, poorly implemented structures, like siloed teams and an over-reliance on automation, can lead to bottlenecks and inefficiencies.
As AI, automation, GitOps, and hybrid cloud strategies continue to shape the future of DevOps, enterprises must be prepared to adapt to these changes, regardless of how efficient their current DevOps model may be. The key to success lies in selecting a topology that aligns with business needs, technical expertise, and operational goals while allowing flexibility to evolve with emerging trends.
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