We crush 5 more myths about platform engineering [Part 2] 

In a previous post, we addressed some common myths about platform engineering—explaining what it is, what it isn’t, and showing how you might already be doing some essential platform engineering tasks. 

This time, we’ll clear up five more myths, focusing on how platforms are built, what they can do, and what they can’t do. 

Myth 6: Platform engineering is an instant fix for major issues 

In a complex environment, expecting quick solutions is unrealistic. Meaningful change takes time and requires a well-thought-out plan tailored to your organization’s unique challenges and operational pace. It’s important to set realistic timelines that consider both current constraints and the steps necessary to implement effective solutions. 

Platform engineering must be tailored to your organization; there is no universal approach.  

To achieve faster results, start with a minimal viable platform (MVP) for a small group of users and gather quick feedback. Pre-made MVPs can help you get started, but it’s important not to think that buying an off-the-shelf platform will solve everything. Real progress requires investment, research, and thoughtful planning. 

This shift involves adopting platform-specific engineering practices, creating ‘golden paths’ or standardized workflows, and implementing the right tools to reduce development friction. 

Myth 7: Platform engineering is only automation, nothing more 

While automation reduces the need for human intervention in managing systems, platform engineering is much more comprehensive.  

Simple scripts might provide targeted solutions for specific issues, but they often fall short when it comes to addressing the broader, more complex needs of a system.  

By contrast, platform engineering encompasses the entire lifecycle of software development and operations—from initial creation and deployment to ongoing monitoring, scaling, and eventual teardown. 

Lifecycle management 

1. Creation and deployment: Platform engineering starts with a robust infrastructure that supports seamless code development, testing, and deployment. It employs advanced CI/CD pipelines to automate these stages, ensuring code moves from development to production effortlessly. 

2. Monitoring and maintenance: Continuous monitoring tools are integrated into the platform to track system performance and detect anomalies in real-time. These tools provide insights that help prevent issues before they escalate, maintaining system health and reliability. 

3. Scaling: Scalability is a cornerstone of platform engineering. By leveraging automation, the platform can dynamically adjust resources to meet varying demands, whether scaling up during peak times or scaling down during quieter periods. This ensures optimal resource utilization and cost-efficiency. 

4. Teardown and cleanup: Automated systems also extend to the end of the lifecycle, efficiently handling the teardown and cleanup of unused resources. This reduces clutter and minimizes the risk of security vulnerabilities associated with outdated components. 

Holistic framework 

While automation is a key component, platform engineering integrates it into a holistic framework. This framework includes not only technical tools but also best practices, governance, and standardized workflows: 

• Best practices and governance: A well-engineered platform incorporates industry best practices and ensures compliance with governance standards. This mitigates risks and enhances system integrity. 

• Standardized workflows: By establishing golden paths—standardized workflows for common tasks—platform engineering reduces variability and enhances productivity. This helps teams focus more on innovation and less on routine, repeatable tasks. 

Real-world example 

Consider a large-scale e-commerce platform. Simple automation scripts might automate tasks like updating product inventory or processing customer orders. However, platform engineering would extend this by: 

• Automating deployment: Implementing automated deployment pipelines to ensure new features and updates are rolled out seamlessly across the platform. 

• Real-time monitoring: Incorporating real-time monitoring to detect and resolve performance issues instantly, ensuring a smooth user experience even during high traffic periods. 

• Scalability: Enabling automatic scaling to handle increased demand during flash sales or promotions, and scaling down when demand subsides to save on costs. 

In essence, while platform engineering certainly incorporates automation, it does so within a comprehensive and cohesive framework designed for full-service lifecycle management.  

This holistic approach not only streamlines operations but also enhances resilience, scalability, and efficiency, making it a crucial strategy for modern enterprises. 

Myth 8: Adopting a cloud-service equals practicing platform engineering 

It’s crucial to understand that simply adopting a cloud service doesn’t equate to practicing platform engineering. This misconception can lead to poor implementation and reveals a lack of clarity about what true platform engineering entails. 

The distinction: cloud services vs. platform engineering 

Yes, you can use any cloud service as part of platform engineering, but what really matters is how you integrate these services into your overall developer experience and operations. Platform engineering aims to create a smooth and efficient environment where different services and tools work together to increase productivity and simplify processes. 

Key platform engineering practices 

To determine whether a cloud service or product aligns with your platform engineering goals, let’s revisit some foundational practices and processes: 

 Developer-centric integration: 

• Ease of access: Ensure that the cloud services are integrated in a way that makes them easily accessible to developers, without tedious configurations. 

•  Self-service tools: Equip developers with self-service capabilities, allowing them to deploy, manage, and scale applications independently. 

2. Automation and Infrastructure as Code (IaC): 

• CI/CD pipelines: Create automated pipelines for continuous integration and continuous deployment, reducing manual interventions and promoting consistent deployments. 

• Infrastructure provisioning: Implement Infrastructure as Code (IaC) to automate the provisioning and management of infrastructure, ensuring scalability and consistency. 

3. Built-in security and compliance: 

• Security integration: Integrate security measures within the development and deployment pipelines to meet compliance requirements from the outset. 

• Monitoring: Implement real-time monitoring and alert systems to quickly detect and address security threats. 

4. Observability and real-time monitoring: 

• Visibility: Ensure comprehensive monitoring of platform performance and health to provide end-to-end visibility for all stakeholders. 

• Feedback mechanisms: Establish feedback loops to continuously inform developers about system performance, facilitating ongoing improvements. 

5. Continuous improvement and iteration: 

• Regular audits: Conduct regular reviews to identify areas for improvement in tools, processes, and practices. 

• Iterative development: Foster an iterative approach to development, allowing for continuous enhancement and quick adaptation to new requirements. 

Practical example 

Consider the adoption of a cloud-based CI/CD service. In platform engineering, simply using this service isn’t enough—successful integration is key: 

• Automated workflows: Set up automated workflows that integrate the CI/CD service into your overall pipeline, maintaining consistency across environments. 

• Streamlined access: Provide developers with a unified dashboard to access the CI/CD service without navigating through multiple interfaces. 

• Integrated monitoring: Embed monitoring solutions to track performance metrics and notify developers of any issues in real-time. 

Effective platform engineering transcends the mere adoption of cloud services. It’s about how these services are incorporated into a cohesive, automated, and secure platform that elevates the developer experience and supports the entire lifecycle of application development.  

By aligning with core platform engineering practices, you can better assess whether a cloud service or product fits your platform strategy and ultimately drive more effective implementation. 

Myth 9: Platform engineering will inflate staffing costs 

Many people worry that creating a platform engineering team will raise personnel costs significantly. 

However, the goal is to shift some of your skilled DevOps staff into this new role, enabling them to use DevOps principles more efficiently with fewer resources by leveraging self-service automation and standardized workflows. 

Although a platform engineering team does need to be formed, the initial members can often be drawn from your current operations and software engineering teams. Over time, this should lead to the platform paying for itself through shared service benefits.  

This approach is similar to Google’s Site Reliability Engineering (SRE) model, which focuses on team growth at a slower rate than system growth. 

It’s a mistake to expect immediate staff reductions in operations or development when starting platform engineering. Instead, focus on retraining your existing teams. They already understand your business needs and infrastructure, and they can become even more productive by using the new platform. 

When implemented correctly, platform engineering makes workflows more efficient by: 

• Automating deployment pipelines, infrastructure, and configurations. 
• Reducing bottlenecks through a self-service model, which minimizes the need for intervention from operations teams. 
• Increasing efficiency so that teams can accomplish more with the same or even fewer resources. 

Although some initial training or hiring might be needed, moving to platform engineering brings long-term benefits by utilizing the expertise within your organization more effectively. 

Myth 10: You should apply platform engineering practices to every application 

One common misconception is that platform engineering practices should be applied universally across all applications. This is far from the truth. Effective platform engineering focuses on relieving teams of high cognitive loads by targeting specific, complex tasks and processes.  

Instead of spreading efforts thin, concentrate platform engineering initiatives on applications bogged down by infrastructure complexities and continuous operational issues. Implement a “golden path” approach to simplify development, prioritizing the abstraction of services that are both heavily used and labor-intensive.

This strategy ensures quicker realization of platform benefits and higher overall efficiency. 

Start by creating meaningful abstractions that offer tangible value. Provide sensible defaults and comprehensive guidance to ensure these abstractions are beneficial. Begin by automating high-impact, common use-cases thoroughly before broadening the scope. This method allows you to build platform confidence and capabilities incrementally. 

Gradual expansion and controlled adoption 

Initially, focus on smaller, less critical services to test and refine the platform. This gradual approach helps in building trust and smoothing out any issues early on. Document the specifics of these early implementations to provide a blueprint for wider adoption. 

It’s essential to recognize that not every service needs to be integrated into the platform immediately. Aim for significant adoption while understanding that some “stray” services may require unique, standalone approaches. These should be documented and periodically reviewed for potential future integration. 

Enhancing efficiency and innovation 

By strategically applying platform engineering practices, you reduce operational bottlenecks and free up resources. Teams become more agile, dedicating time to innovation rather than battling infrastructure woes. This targeted approach enhances the overall efficiency and productivity of your development processes. 

In summary, platform engineering isn’t a one-size-fits-all solution. Its true power lies in its ability to streamline and simplify the most challenging parts of your infrastructure. By focusing on high-impact areas first and expanding thoughtfully, you ensure a smoother transition and more effective implementation, ultimately driving sustained innovation and growth. 

Conclusion 

In this post, we debunked five more myths about platform engineering, focusing on the realities of its implementation, scope, and benefits. Contrary to common misconceptions, platform engineering is not a quick fix, mere automation, or a one-size-fits-all solution. 

Instead, it’s a comprehensive, strategic approach that requires customization, thoughtful integration of cloud services, and a focus on high-impact areas to drive significant improvements in efficiency and productivity.

By embracing platform engineering practices that suit your organization’s unique needs, you can effectively overcome operational challenges, foster innovation, and ensure sustained growth. It’s a journey that demands investment in time and resources but ultimately offers transformative benefits that are well worth the effort. 

Have you missed our latest e-book on all things platform engineering? It explains what platform engineering is, its benefits, its misconceptions, and how best to implement it.

Download it here to learn more about platform engineering and what benefits it could have for you and your company.