Vultr, the world's largest individual-owned cloud infrastructure company, has released the results of a new study that reveals how AI is reshaping platform engineering.
Platform Engineering Annual The state of AI in platform engineering Research shows that AI adoption is currently mainstream among platform engineers, with 75% of teams already planning to host or host AI workloads, or 89% using AI for tasks like code generation and documentation. However, the report warns of “AI-implemented plateaus” where early momentum exceeds actual corporate value.
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Although recruitment is strong, the study also reveals gaps that limit the impact of firm size. To explore these gaps more deeply, Vultr sponsored a companion survey The state of AI in platform engineering, We will actively build AI-Native systems targeting over 120 experts. The findings highlight both the challenges that must be addressed to achieve success.
The key findings are as follows:
- AI ownership is fragmented: Almost 40% of organizations assign responsibility for AI platforms to platform engineering teams, quarterly (25%) reports shared among multiple groups, with 13% reporting not clearly reporting ownership.
- Workload orchestration is uneven:Uses over 40% of Kubernetes extended to GPU and AI workloads, but 35% never coordinates AI workloads.
- The integration is expanding, but the pipeline is delayed: More than half (58%) embed AI in cloud-native applications, while 41% do not adapt AI CI/CD or DevSeCops pipelines. 28% have expanded their model processing pipelines, while 24% have added steps for inference services.
- Hybrids and on-plames continue to be relevant: Cloud-native integration is dominated, but 16% of organizations have adopted a hybrid approach, and 9% continue to run GPU workloads on-premises, reflecting the demand for flexible deployment options.
- Standardization is urgent: Over 50% of respondents consider AI infrastructure templates and blueprints Deathly or It's very important To ensure safe and scalable adoption.
- The collaboration gap lasts: Almost a third (31%) reported limited collaboration with data science teams, and 16% reported none other than highlighting ongoing cultural and operational barriers.
“Since the 1990s, this adoption rate hasn't seen such adoption rates. It's very incredible. But the reality is that most companies today use. “Platform engineers are leading the way, but we need a stronger foundation to turn momentum into a measurable impact.”
“This study reflects what we see every day. Platform engineers are quickly becoming a link pin for enterprise AI adoption,” said Kevin Cochrane, Vultr's CMO. “But momentum alone isn't enough. Teams need clear golden paths and AI-first infrastructure that make workloads safe, repeatable and scalable. This is exactly what Vultr offers, giving platform teams the foundation for moving past experiments and achieving real impacts on a global scale.”
Once platform engineers take on the role of enabling AI across the enterprise, Vultr offers an AI-first infrastructure that enables that. With GPU-enabled instances deployed in minutes, global orchestration out of the box, and configurable architecture designed for advanced MLOPS, Vultr allows platform teams to move beyond the “AI-implemented Plateau” towards enterprise-scale value.
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