Vultr and Platform Engineering Reveal Key Trends and Challenges in New “State of AI in Platform Engineering” Report

Vultr, the world’s largest privately-held cloud infrastructure company, today published the findings of its latest survey revealing how AI is transforming platform engineering.

The annual “State of AI in Platform Engineering” survey shows that AI adoption among platform engineering teams is now mainstream, with 75% either hosting or planning to host AI workloads and 89% leveraging AI daily for activities like code generation and documentation. Despite this strong uptake, the report identifies an “AI implementation plateau” where early enthusiasm outpaces tangible enterprise value.

To better understand the barriers to scaling AI, Vultr sponsored a companion survey involving over 120 AI-native systems professionals. The results highlight both significant advancements and critical challenges that need addressing for greater success.

Key insights include:
AI ownership remains fragmented: 40% of organizations assign AI platform responsibilities to platform engineering teams, 25% share ownership across teams, and 13% report unclear accountability.

Workload orchestration varies: More than 40% use Kubernetes extended for GPU and AI workloads, while 35% do not orchestrate AI workloads, indicating infrastructure maturity gaps.

Integration expands but pipelines lag: 58% embed AI within cloud-native apps, yet 41% have not adapted CI/CD or DevSecOps pipelines for AI. Among those that have, 28% modify pipelines for model handling, and 24% add inference service steps.

Hybrid and on-prem deployments persist: While cloud-native dominates, 16% use hybrid approaches and 9% still run GPU workloads on-premises, reflecting the need for flexible deployment.

Standardization is critical: Over half regard AI infrastructure templates and blueprints as vital for safe, scalable AI adoption.

Collaboration challenges continue: Nearly a third (31%) report limited cooperation with data science teams, and 16% report none, revealing ongoing cultural and operational barriers.

Luca Galante, a core contributor to the Platform Engineering Community, commented, “Adoption rates like this haven’t been seen since the 1990s. Yet, enterprise AI remains more experimental than strategic. Platform engineers are leading, but turning momentum into real impact requires stronger foundations.”

Kevin Cochrane, CMO of Vultr, added, “Platform engineers are becoming the central enablers of enterprise AI adoption. However, momentum alone isn’t sufficient. Clear golden paths and AI-first infrastructure that ensure workloads are safe, repeatable, and scalable are essential. That’s exactly what Vultr offers, enabling platform teams to move beyond experimentation and unlock significant global impact.”

Vultr supports platform engineers with AI-first infrastructure, including GPU-ready instances with rapid deployment, built-in global orchestration, and composable architectures designed for advanced MLOps. This foundation helps teams overcome the “AI implementation plateau” and deliver enterprise-scale AI value.