How is platform engineering changing?

Platform engineering has emerged as a critical discipline in modern software development, changing how organizations build, deploy, and maintain their digital infrastructure. As companies face increasingly complex technical environments, the role and practice of platform engineering continues to evolve.

The numbers reflect this shift: 55% of organizations have already implemented platform engineering, and 90% of those plan to expand it to more teams. Gartner predicts that 80% of large software engineering organizations will have platform teams by 2026.

The shift from infrastructure to developer experience

Traditional platform engineering focused heavily on infrastructure management and operational concerns. Teams spent most of their time provisioning servers, managing networks, and ensuring system reliability. Today’s platform engineering focuses on creating developer experiences that abstract away infrastructure complexity.

This shift manifests in the creation of internal developer platforms (IDPs). These platforms serve as self-service portals for development teams. Developers interact with platforms that handle provisioning, configuration, and deployment automatically, instead of submitting tickets for infrastructure resources. The result: 85% of companies using platform engineering report that their developers rely on the platform to succeed.

The platform engineering team’s primary responsibility has transformed. They no longer manage infrastructure directly. Instead, they build and maintain tools that empower developers to manage their own needs efficiently.

Real-world transformation

Consider a large financial services company’s transition to modern platform engineering. Previously, deploying a new microservice required multiple team requests. Teams waited weeks for server provisioning, database setup, network configuration, and monitoring.

With their new internal developer platform, the same team provisions complete environments in minutes. The platform encodes all requirements, best practices, and governance policies automatically. This ensures consistency while dramatically improving velocity.

The rise of product thinking in platform teams

Platform engineering teams increasingly adopt product management principles. They treat internal platforms as products and development teams as customers. This mindset shift drives platform engineers to focus on user research and iterative improvement based on feedback.

This product-oriented approach requires new skills beyond technical expertise. Platform engineers must understand user experience design and conduct developer interviews. They analyze usage metrics and make strategic decisions about feature development.

Platform teams now often include dedicated product managers. These managers work alongside engineers to ensure platform evolution aligns with developer needs and business objectives.

Measuring success differently

The emphasis on product thinking changes how platform teams measure success. Traditional metrics like uptime remain important. However, teams now prioritize developer satisfaction scores and time-to-first-deployment for new services.

These metrics reflect the platform’s actual value to the organization. They go beyond technical performance to measure real business impact.

From centralized to federated platform models

The structure of platform engineering itself is undergoing significant change. Many organizations initially created centralized platform teams responsible for all capabilities. Now there’s a growing trend toward federated models that distribute ownership while maintaining central coordination.

This shift reflects a broader realization: 55.9% of companies now operate more than one internal platform. The “one platform fits all” approach has proven inadequate for organizations with diverse technical needs.

In a federated model, different teams own specific platform capabilities aligned with their expertise. A data engineering team might own data platform components. A security team maintains identity and access management services. A core platform team provides the underlying infrastructure layer.

Federated models draw on specialized expertise while avoiding bottlenecks. No single team tries to serve all platform needs.

Governance in federated models

The federated approach requires careful governance and coordination. Organizations establish platform councils that bring together representatives from different teams. These councils ensure consistency, prevent duplication, and maintain a coherent developer experience.

Teams create shared standards for APIs, documentation, and user interfaces. Developers experience the platform as a unified whole rather than disconnected services.

Integration of AI and machine learning capabilities

Artificial intelligence and machine learning are now central to modern platform engineering. Platform teams add AI-powered features to improve developer productivity and automate routine tasks.

The relationship runs both directions: 94% of organizations identify AI as critical to the future of platform engineering, while 86% say platform engineering is essential to unlocking AI’s full business potential. Platform teams face two jobs at once: accelerate AI-driven development while providing infrastructure for AI workloads.

AI capabilities manifest throughout the platform in various ways. Intelligent code suggestions help developers write more efficient infrastructure configurations. Anomaly detection systems automatically identify and resolve performance issues before they impact users. Natural language interfaces allow conversational interaction with platform services.

AI in action

When developers create new services, AI assistants can analyze similar organizational services and suggest optimal configurations based on historical performance data. Machine learning models continuously analyze service behavior in production, automatically adjusting resources to optimize cost while maintaining performance.

If unusual patterns emerge, AI systems alert teams with probable causes and remediation steps. This proactive approach prevents issues before they impact users. Teams exploring these capabilities should consider how AI changes affect feature experimentation and rollout strategies.

The golden path philosophy and standardization

Platform engineering increasingly embraces golden paths: pre-defined workflows that guide developers toward best practices. Golden paths balance complete freedom with rigid standardization.

Golden paths provide tested routes for common tasks like deploying web services or setting up data pipelines. These paths incorporate security best practices and compliance requirements by default. Developers achieve their goals quickly using proven patterns aligned with company policies.

A mature golden path typically includes standardized CI/CD pipelines, pre-configured observability, and feature flag management for controlled rollouts. This combination lets teams ship quickly while maintaining the ability to instantly disable problematic changes without redeployment.

Modern platform engineering recognizes that golden paths cannot cover every scenario. Teams with unique requirements can deviate when necessary. The platform might require additional approvals or documentation for justified deviations.

This flexibility prevents the platform from constraining innovation. It still promotes standardization where it provides value.

From “shift left” to “shift down”

The industry is moving beyond the “shift left” mantra, which pushed security and compliance responsibilities to developers earlier in the process. The newer approach is “shift down”: embedding these controls directly into the platform itself.

Modern platforms automatically enforce policies through admission controllers, implement least-privilege access by default, and maintain audit trails for all interactions. Developers meet requirements without needing security expertise.

Platforms provide pre-approved, security-hardened base images. They automatically update dependencies to address vulnerabilities. Compliance requirements are encoded as policy-as-code, enforcing regulations across all services.

Impact on security posture

Platform-enforced security significantly reduces the burden on development teams. It improves overall security posture through consistent application of best practices. Each team no longer implements security measures independently with varying success rates.

This same principle applies to release safety. By integrating feature flags as core platform infrastructure, teams gain instant rollback capabilities without the risk and delay of redeployment. The platform handles safety; developers focus on shipping.

Common pitfalls and how to avoid them

Despite platform engineering’s momentum (the market is projected to exceed $50 billion by 2028), many initiatives struggle. Understanding common pitfalls helps teams avoid them.

Treating it as a technology problem

The most frequent mistake is focusing on tools while neglecting culture. Platform engineering requires cross-functional collaboration between development, operations, security, and business teams. Organizations that treat it as purely a technical initiative often see low adoption and siloed implementations. The solution: involve developers early, gather continuous feedback, and treat the platform as a product with real users.

Neglecting measurement

29.6% of platform teams track no success metrics at all. Without measurement, teams cannot demonstrate ROI, justify continued investment, or identify what’s working. Essential metrics include deployment frequency, lead time for changes, mean time to recovery, platform adoption rate, and developer satisfaction scores.

Underfunding the initiative

47.4% of platform engineering initiatives operate with budgets under $1 million annually. While starting small is wise, chronic underfunding leads to incomplete tooling, understaffed teams, and platforms that never reach critical mass. Building a clear business case (quantifying time savings, reduced incidents, and faster delivery) helps secure appropriate investment.

Big-bang launches

Attempting to build a comprehensive, enterprise-wide platform before launching often leads to multi-year projects that never ship. The alternative: start with a minimum viable platform addressing one or two high-value workflows. Deliver value quickly, gather feedback, and expand iteratively.

Assuming one platform serves everyone

The reality that most organizations run multiple platforms reflects genuine differences in team needs. A machine learning platform has different requirements than a microservices platform. Forcing all teams onto a single solution often drives them toward shadow IT alternatives.

Conclusion

Platform engineering has shifted from managing infrastructure to building products that developers actually want to use. The organizations seeing results share common traits: they treat platforms as products, measure what matters, and embed safety directly into the platform.

For teams starting out, the path forward is clear. Begin with a minimum viable platform addressing one high-friction workflow—often CI/CD or environment provisioning. Staff it like a product team with dedicated engineering and product management. Define success metrics from day one: deployment frequency, time-to-first-deploy for new services, and developer satisfaction. Expand based on adoption and feedback, not roadmap ambition.

For mature platform teams, the next frontier involves AI integration, federated ownership models, and deeper “shift down” of security controls. The 94% of organizations viewing AI as critical to platform engineering’s future aren’t wrong—but the fundamentals of product thinking and developer empathy remain the foundation everything else builds on.

FAQs

Is platform engineering just a rebranding of DevOps?

Platform engineering evolved from DevOps but serves a different function. DevOps is a cultural mindset emphasizing collaboration, automation, and shared responsibility between development and operations. Platform engineering operationalizes that mindset at scale by building concrete products: self-service portals, standardized pipelines, and automated guardrails.

Has platform engineering become essential for software organizations?

The data suggests yes. 85% of companies with platform engineering report their developers rely on it to succeed, and 88% of tech executives consider it crucial to meeting their objectives. It has moved from experimental to strategic: Gartner named it a top-10 technology trend for 2024 and predicts 80% of large software organizations will have platform teams by 2026.

What metrics should platform teams track?

Effective platform teams typically track metrics across three categories. Delivery performance includes deployment frequency, lead time for changes, change failure rate, and mean time to recovery (the standard DORA metrics). Platform adoption measures what percentage of teams use platform services, time-to-first-deployment for new engineers, and self-service completion rates. Developer experience captures satisfaction scores, support ticket volume, and time spent on infrastructure versus product work.

What’s the future of platform engineering?

Three trends will define the next phase. First, AI integration will move from experimental to essential. Platforms will suggest configurations, auto-tune resources, and provide natural-language interfaces for common tasks. 94% of organizations already view AI as critical to platform engineering’s future.

Second, the “shift down” philosophy will deepen. Platforms will encode security and compliance requirements as defaults: policy-as-code, automated scanning, zero-trust networking built into every deployment.

Third, platforms will become more composable. The era of monolithic, one-size-fits-all platforms is ending. Organizations will operate multiple specialized platforms (application, data, ML) with shared governance and consistent developer experiences across them.