AI-native companies are moving rapidly from experimentation to ambition: proofs of concept (POCs) are plentiful, models are increasingly capable, and investor and customer expectations are rising. Yet the industry is facing a persistent and widening production gap. Many AI initiatives stall between successful POCs and reliable, scalable, production-grade deployments. This white paper, developed by Anicca Market Insights (AMI) and sponsored by Nebius, examines why this transition remains so elusive, and how AI-native organizations can overcome the obstacles. Our analysis identifies three primary classes of inhibitors: commercial, technical, and compliance-related, reinforced by organizational readiness challenges rooted in pre-AI operating models.
While early-stage AI innovation is often framed as a challenge of GPU scarcity, scaling to production is fundamentally a systems, economics, and governance problem. The hyperscale cloud platforms — most notably Amazon Web Services, Microsoft Azure, and Google Cloud Platform — were not designed primarily for AI-native workloads. Instead, they evolved to support retail operations, enterprise SaaS, and consumer internet services at global scale. AI workloads have been layered on top, often inheriting constraints and inefficiencies that become acute in production environments.
From a commercial standpoint, AI-native companies struggle with non-transparent total cost of ownership (TCO). Hyperscaler pricing models obscure the true cost of AI through fragmented line items, unpredictable charges, and hidden costs (such as data egress). This lack of predictability makes it difficult to forecast unit economics or justify scaling decisions to c-level executives, boards and investors.
Further compounding the issue, many organizations have already invested heavily in frontier model training, fine-tuning, or distillation pipelines tightly coupled to a single model vendor’s tooling. These sunk costs inhibit migration toward multi-model and multi-cloud strategies, even when better performance or economics are available elsewhere.
To achieve competitive pricing (often measured narrowly as cost per GPU-hour) customers are frequently forced into upfront reserved capacity commitments. These commitments reduce flexibility, amplify vendor lock-in, and shift infrastructure risk from the provider to the customer. Meanwhile, critical decisions such as instance sizing and workload optimization are left to customers without adequate predictive tooling, resulting in systematic over- or under-provisioning.
Enterprise Agreements (EAs) further exacerbate the problem by aggregating spend across unrelated workloads, making AI infrastructure decisions subservient to broader commercial negotiations rather than workload-specific performance and efficiency needs.
On the technical front, access to state-of-the-art compute is increasingly constrained. Scarce GPU capacity is often reserved for large, established enterprise customers, while AI-native firms are offered older microarchitectures (e.g., Hopper) rather than current or next-generation platforms (e.g., Blackwell, Rubin, and beyond).
Hyperscalers also actively promote proprietary accelerators and architectures (custom silicon or tightly coupled CPU/GPU stacks) that increase switching costs and reduce architectural freedom. At the same time, many AI clouds fail to deliver true full-stack solutions that integrate compute, networking, orchestration, and storage in a way that materially reduces TCO and operational complexity.
These challenges are not accidental. Hyperscaler platforms remain optimized for their original design constraints, not for the deterministic performance, ultra-low latency networking, and workload-specific scaling that modern AI production demands.
Beyond cost and performance, compliance and governance gaps represent a critical brake on production adoption. AI-native companies often lack clear guidance on security, regulatory alignment, and governance frameworks suitable for production AI systems.
Industry-wide governance baselines — broadly accepted by customers, vendors, and regulators — are still emerging. In their absence, organizations hesitate to commit mission-critical workloads to production. Security concerns are especially acute: many teams struggle to define or contain the blast radius of AI failures such as model drift, data leakage, or emergent behavior, making risk management and accountability unclear.
Finally, scaling AI exposes organizational readiness gaps. Skills shortages, legacy operating models, and decision-making structures designed for pre-AI IT environments slow adoption. AI requires tighter coupling between infrastructure, product, data science, and governance teams — yet many organizations remain siloed, reinforcing friction at precisely the moment scale demands cohesion.
Nebius Cloud was architected specifically to address the structural reasons AI-native companies fail to scale beyond proof-of-concept. Rather than adapting legacy cloud models to AI workloads, Nebius was designed from the ground up for production AI economics, performance determinism, and governance clarity. Its approach maps directly to the commercial, technical, and compliance challenges outlined above.
Nebius addresses the industry’s lack of cost transparency by collapsing AI infrastructure economics into a small number of clearly attributable cost drivers. Customers gain direct visibility into compute, networking, and storage consumption without hidden line items such as punitive data egress fees or cross-service dependencies.
This transparency enables:
In contrast to hyperscaler pricing models that obscure true cost through fragmented services and pricing tiers, Nebius enables customers to understand what they are paying for, why, and how it scales.
Nebius breaks the hyperscaler model that ties competitive GPU pricing to long-term reserved capacity commitments. Instead, pricing is aligned to actual workload characteristics, enabling customers to scale up or down without assuming infrastructure risk on behalf of the provider.
This is particularly critical for AI-native companies whose demand curves are:
By removing rigid reservation constructs, Nebius allows AI initiatives to scale economically as product-market fit emerges, not before.
Nebius assumes responsibility for infrastructure efficiency rather than pushing that burden onto customers. Through AI-native platform design and workload-aware configuration, Nebius reduces the need for customers to manually select instance “t-shirt sizes” without adequate predictive tooling.
The result is:
Nebius prioritizes access to current and next-generation GPU architectures rather than relegating AI-native customers to older silicon. This ensures that performance gains from architectural advances — such as improved memory bandwidth, interconnect speed, and power efficiency — translate directly into lower training time and inference cost.
Unlike hyperscalers that reserve cutting-edge capacity for select enterprise customers or internal workloads, Nebius aligns its roadmap with the needs of AI-native builders.
Nebius does not push customers toward proprietary accelerators or CPU architectures designed primarily to reinforce ecosystem lock-in. Instead, it focuses on best-of-breed, industry-standard GPUs combined with high-performance networking, allowing customers to optimize for performance rather than vendor strategy.
This neutrality ensures that architectural decisions remain aligned with model performance and scalability requirements — not procurement constraints.
Nebius delivers a cohesive AI stack—from compute and networking through orchestration and data pipelines—designed to minimize friction and maximize efficiency at scale. This reduces operational overhead and eliminates the hidden inefficiencies that arise when AI workloads are assembled from loosely integrated cloud services.
Crucially, Nebius infrastructure is not constrained by legacy cloud design goals. Hyperscaler platforms were built to optimize for:
Nebius provides the structural building blocks needed for AI governance in production environments, even as industry-wide standards continue to mature. By offering clearer workload boundaries, infrastructure isolation, and observability, Nebius enables customers to define governance policies that are operationally enforceable, not merely aspirational.
This clarity allows AI-native companies to commit production workloads with greater confidence, despite evolving regulatory landscapes.
One of the most significant inhibitors to scaling AI is uncertainty around failure modes — such as model drift, data leakage, or unintended behavior — and their potential impact. Nebius infrastructure supports clearer isolation and monitoring, enabling organizations to:
This reduces organizational hesitation to scale AI systems that are already delivering value at the POC stage.
Finally, Nebius indirectly addresses organizational inhibitors by simplifying the infrastructure layer. By reducing complexity, unpredictability, and opaque decision-making at the platform level, Nebius allows AI-native organizations to focus scarce talent on model innovation, product differentiation, and governance maturity—rather than cloud mechanics.
Taken together, Nebius shifts AI scaling from a high-risk, opaque endeavor into a controlled, economically rational, and technically predictable process. By aligning infrastructure design with AI-native realities rather than legacy cloud constraints, Nebius enables organizations to move decisively from POC to production.
In the concluding section of this paper, we explore how this foundation is operationalized through Nebius Token Factory, enabling inference at scale with predictable economics and production-grade confidence.
In the second section of this paper, we outline how Nebius customers can address these challenges through transparent economics, AI-native infrastructure design, and production-ready governance foundations. Nebius’ approach is purpose-built for AI workloads, emphasizing predictable TCO, access to leading-edge compute, and architectural choices aligned with the realities of modern model training and inference.
As AI-native companies move from experimentation to execution, the ability to scale inference reliably and economically becomes a defining competitive advantage. We conclude by inviting organizations to explore how Nebius Token Factory enables inference at scale, helping bridge the gap from POC to production with confidence, clarity, and control.