Platform engineering has emerged as a cornerstone of modern IT operations, creating standardized and reusable infrastructure to streamline workflows, automate processes and enable efficient development cycles. Traditionally, platform engineering efforts have been closely tied to DevOps, aimed at providing robust, self-service platforms for application delivery teams. However, as organizations expand their technological capabilities to include data science, observability and other IT functions, an important question arises: Should platform engineering focus exclusively on serving DevOps teams, or should it cater to the broader spectrum of IT needs?
As part of my research for the launch of PlatformEngineering.com I came upon an excellent blog over at the CNCF, by Giulio Roggero. I highly recommend reading and digesting that blog. Roggero’s vision for what might be called Platform Engineering ++ is compelling.
For this article I want to explore the evolving role of platform engineering, its foundational relationship with DevOps, and its potential to support other critical functions across an organization’s IT landscape.
The Origins: Platform Engineering and DevOps
Platform engineering originated as a response to the growing complexity of software development and delivery pipelines. Its primary goal has been to abstract away the intricacies of infrastructure management, enabling development teams to focus on building and deploying applications. These platforms provide self-service capabilities, including provisioning environments, CI/CD pipelines and monitoring tools—all designed to empower developers while aligning with DevOps principles of collaboration, automation and speed.
In this context, platform engineering serves as the backbone for implementing DevOps practices effectively. By standardizing tools and processes, platforms ensure consistency and reliability, which are critical for rapid software delivery. The close alignment with DevOps has made platform engineering indispensable for teams striving to adopt modern software delivery methodologies.
Expanding Beyond DevOps
While DevOps remains central to platform engineering, organizations increasingly recognize the need for platforms that address the requirements of other disciplines, such as:
- Data Science and Machine Learning
Data scientists often require dedicated platforms for tasks such as data processing, model training and deployment. These platforms must handle large-scale data, integrate with diverse data sources, and provide computational resources on demand. Unlike traditional DevOps needs, data science platforms emphasize experiment tracking, reproducibility and integration with AI/ML workflows.
For instance, companies like Netflix and Uber have invested in specialized platforms like Metaflow and Michelangelo, designed specifically to support data science and machine learning teams. These platforms streamline model development and deployment while ensuring compatibility with enterprise infrastructure.
- Observability and Monitoring
As software systems grow in complexity, observability has become a critical function for maintaining performance and reliability. Platforms focused on observability go beyond traditional monitoring tools to provide end-to-end visibility into system behavior, tracing, and anomaly detection.
A platform engineering approach tailored to observability could standardize tooling and provide seamless integrations, enabling teams to monitor distributed systems effectively. Observability platforms might also incorporate machine learning models for predictive analytics, further enhancing their value.
- Security and Compliance
Security and compliance teams also benefit from platform engineering. A security-focused platform might automate vulnerability scanning, enforce compliance policies, and provide centralized logging and auditing capabilities. Such platforms ensure that security practices are embedded throughout the development lifecycle, reducing the risk of breaches and non-compliance.
- IT Operations and Infrastructure Management
IT operations teams are tasked with maintaining infrastructure health, optimizing resource utilization, and managing incident response. Platforms built for IT operations could automate infrastructure provisioning, implement auto-scaling, and facilitate incident response workflows. By integrating with observability and security tools, such platforms provide a comprehensive view of the IT environment.
Challenges of Broadening Platform Engineering’s Scope
Expanding the focus of platform engineering to include multiple disciplines is not without its challenges:
- Diverse Requirements: Different teams have distinct needs. Data scientists, for example, prioritize GPU availability and experiment tracking, while observability teams require real-time data processing and visualization. Building platforms that cater to these varied requirements can be complex.
- Increased Complexity: The more functions a platform supports, the more intricate it becomes. Balancing usability with functionality is critical to ensure platforms remain accessible and effective.
- Resource Constraints: Developing platforms for multiple disciplines requires significant investment in terms of time, expertise and funding. Smaller organizations may struggle to justify such an expansion.
- Organizational Silos: Collaboration between different teams—such as DevOps, data science and observability—can be challenging. Aligning goals and priorities across disciplines is essential for successful platform engineering initiatives.
Benefits of a Unified Platform Engineering Approach
Despite these challenges, there are compelling reasons to adopt a broader approach to platform engineering:
- Increased Efficiency: A unified platform can eliminate redundancies and streamline workflows across teams. Shared resources, standardized processes, and centralized tooling reduce duplication of effort and promote efficiency.
- Improved Collaboration: Platforms that cater to multiple disciplines encourage cross-functional collaboration. For example, a data science platform integrated with DevOps pipelines enables seamless deployment of machine learning models into production.
- Scalability: A holistic approach to platform engineering enables organizations to scale their operations more effectively. By addressing the needs of diverse teams, platforms ensure that all functions can grow in tandem with the organization.
- Innovation Enablement: By providing tools and infrastructure tailored to their needs, platforms empower teams to innovate. Data scientists can experiment with new models, while observability teams can develop advanced analytics capabilities, driving overall organizational growth.
Key Considerations for Broader Platform Engineering
To successfully expand platform engineering beyond DevOps, organizations must consider the following:
- User-Centric Design: Platforms must be designed with the end-users in mind. Understanding the specific needs of each discipline is crucial to building effective platforms.
- Modular Architecture: Adopting a modular approach allows platforms to cater to diverse requirements without becoming monolithic. Teams can select and integrate only the components they need.
- Automation and Integration: Automation is essential for scalability, while seamless integration ensures compatibility with existing tools and workflows.
- Governance and Security: Expanding platform engineering increases the risk of misaligned priorities and security vulnerabilities. Establishing clear governance frameworks and incorporating robust security measures is essential.
- Continuous Feedback and Improvement: Platforms must evolve based on user feedback and emerging technologies. Regular updates and iterative improvements ensure platforms remain relevant and effective.
Conclusion
The scope of platform engineering is evolving, reflecting the diverse needs of modern IT organizations. While its origins are rooted in DevOps, the discipline has the potential to support a wide range of functions, from data science and observability to security and IT operations.
By broadening its focus, platform engineering can drive efficiency, collaboration and innovation across an organization’s technology ecosystem. However, achieving this vision requires careful planning, resource allocation and a commitment to user-centric design.
Ultimately, the decision to expand platform engineering’s focus depends on organizational goals and priorities. For some, doubling down on DevOps may be the right choice. For others, embracing a broader approach could unlock new opportunities and propel their IT capabilities to the next level.
The future of platform engineering lies in its ability to adapt and address the ever-changing needs of modern enterprises. Whether focused on DevOps or extending its reach, platform engineering will continue to play a pivotal role in shaping the way organizations build, deploy and manage technology.