Forrester defines Neocloud and NeoPaaS categories to scale AI-native infrastructure

The definition of these categories signals a shift from raw GPU procurement to operational efficiency in AI infrastructure. For streaming providers using large-scale inference for personalization or content moderation, Neocloud optimization could significantly lower the total cost of ownership for specialized compute. NeoPaaS offers a path to govern agentic AI lifecycles without the fragmentation typical of early-stage open-source stacks. This standardization is critical as enterprises move from experimental model training to production-grade deployment at scale. Watch for hyperscalers to respond by rebranding their managed Kubernetes services to match these AI-centric capability benchmarks.

The formalization of the Neocloud category arrives as enterprise GPU waste reaches critical levels. Per Cast AI’s 2026 State of Kubernetes Optimization Report, average GPU utilization across production clusters sits at just 5%, meaning 95% of provisioned capacity remains idle despite rising hardware costs. This inefficiency has been exacerbated by 'defensive over-provisioning' during the 2024-2025 chip scarcity period, leaving many organizations with expensive, underutilized assets on multi-year depreciation cycles.

Financial projections for this shift are substantial. Forrester forecasts that AI-focused neocloud providers will generate $20 billion in revenue by the end of 2026, capturing market share from traditional hyperscalers in the generative AI space. This trend is mirrored by Gartner's August 2026 data, which shows global spending on AI-optimized infrastructure-as-a-service reaching $42 billion this year as inference workloads—the operational side of AI—begin to outpace model training expenditures.

At the platform level, the focus on NeoPaaS reflects a transition toward 'agentic AI' governance. At KubeCon Europe 2026, the Kubernetes community shifted its focus from merely supporting AI to rebuilding the platform around AI conformance and upstream GPU orchestration. These industry movements suggest that the technical challenge for streaming and media firms is no longer just accessing silicon, but managing the complex lifecycle of autonomous agents that interact with enterprise data.