Neoclouds: What They Are and How They Cut ERP Cloud Costs

Cloud cost pressure has been building for years, but AI is now pushing it directly into core enterprise systems and budgets. As organizations embed AI into ERP processes such as planning, forecasting, procurement, and supply chain execution, cloud spend is rising alongside demand for compute-intensive workloads. This is creating renewed interest in a newer class of provider: neocloud providers.

Neocloud providers are specialized cloud providers built primarily for AI workloads, especially those requiring high-performance GPU infrastructure. Unlike hyperscalers, which support a broad range of enterprise applications, neocloud providers focus on delivering the infrastructure needed to run AI efficiently. 

If ERP is the business control tower, neocloud providers are the specialist lane for the AI workloads that are putting the biggest strain on cloud budgets. For ERP leaders, this distinction is becoming increasingly relevant as AI moves from experimentation into operational systems.

Why AI Is Reshaping Cloud Cost Models for ERP

Cloud cost volatility is no longer confined to IT budgets. Recent research shows global cloud infrastructure spending reached $110.9 billion in Q4 2025, up 29% year over year, while 88% of CFOs say cloud spend is rising. As AI becomes embedded into ERP systems, it is pushing up compute demand across finance, operations, and executive decision-making, making spending harder to predict and control.

This shift is especially visible as organizations move AI from pilot to production. In one survey, AI and machine learning workloads accounted for 22% of cloud costs, and three-quarters of CFOs said cloud forecasts swing by 5% to 10% of revenue each month. 

That volatility is why cloud is no longer just infrastructure supporting ERP; it is becoming a core operating cost inside ERP-led transformation programs. 

How Neocloud Providers Fit the ERP Stack

Neocloud providers are designed to address exactly the type of workloads that are expanding within ERP environments. They typically provide GPU-heavy compute, high-throughput networking, and storage optimized for AI training and inference, rather than general-purpose enterprise services.

According to Synergy Research Group, the neocloud market surpassed $25 billion in 2025 and could approach $400 billion by 2031.

Leading providers include CoreWeave, Crusoe, Core Scientific, Lambda, Nebius, and Nscale. For ERP teams, that signals a fast-expanding infrastructure tier built specifically to decouple AI workloads from core transactional systems. 

Instead of running all AI processing within hyperscaler environments that also host ERP systems, enterprises can selectively run compute-intensive AI workloads on neocloud infrastructure while keeping ERP systems stable and controlled.

This separation introduces a more modular architecture. ERP systems continue to manage transactions and core data, while AI workloads run on infrastructure purpose-built for AI performance and cost efficiency. This approach aligns with emerging clean-core strategies, where intelligence and extensions are handled outside the core ERP environment.

The Cost and Capacity Argument

The economic case for neocloud providers becomes clearer when viewed through an ERP lens. AI workloads tied to ERP processes, such as predictive analytics or real-time optimization, can significantly increase infrastructure costs when run on general-purpose cloud environments.

A 2025 report by the Uptime Institute said neocloud providers can offer up to roughly two‑thirds less cost access to GPU compute than hyperscalers and more predictable pricing models for these workloads. 

This can help organizations manage the cost of scaling AI across ERP processes without disproportionately increasing overall cloud spend.

Equally important is access to capacity. As demand for GPUs continues to rise, delays in securing compute can slow down ERP-related AI initiatives across the enterprise, including those tied to forecasting, planning, and process automation.

Surveys of AI practitioners show that more than 80% of organizations have delayed AI initiatives because of limited GPU access, often through hyperscaler-led channels. 

For ERP teams, that means AI-driven demand planning, intelligent finance modules, or supply-chain-optimization capabilities can be pushed out by several months if GPU capacity is constrained on the platforms they rely on.

Neocloud providers are often structured to provide faster access to GPU resources, which can accelerate deployment timelines for AI-enabled ERP capabilities.

For CFOs and CIOs, this introduces a new lever for cost optimization. Instead of reducing AI ambition, organizations can optimize where and how AI workloads run within the broader ERP architecture.

Where Neoclouds Fit in Enterprise Architecture

Neoclouds are not replacing hyperscalers or enterprise cloud platforms. Instead, they are becoming part of a multi-cloud strategy that is increasingly workload-specific.

In a typical ERP architecture, hyperscalers may continue to host core ERP systems, data platforms, and integration layers. Neocloud providers, meanwhile, can support AI model training, large-scale inference, and compute-heavy analytics that feed into ERP processes.

This creates a layered architecture:

• Core ERP systems manage transactions and master data.
• Data platforms aggregate and prepare enterprise data.
• Neocloud providers handle AI processing and model execution.
• Outputs are reintegrated into ERP workflows for execution.

This kind of split already exists in practice and is already reflected in emerging providers. For example, new entrants such as Antimatter are positioning themselves as vertically integrated neocloud providers built specifically for AI inference, designed to deliver AI-heavy workloads faster and at lower cost than traditional hyperscalers.

Within this architecture, the role of neocloud providers is starting to break along a few distinct ERP-linked AI patterns. The point is not simply that they offer GPU capacity, but that they are differentiating around the specific operational, cost, and governance demands that enterprise AI introduces into ERP environments.

• Real-time AI inference is emerging as one of the clearest use cases, helping ERP systems support faster decisions in areas such as demand forecasting, pricing updates, and conversational interfaces by relying on high-speed GPU infrastructure.
• Sustainable compute is becoming a differentiator as well, with providers such as Crusoe positioning energy-first infrastructure for AI-heavy ERP workloads where power cost and carbon impact are moving into board-level discussions. 
• Sovereign AI infrastructure is becoming more important where ERP-related AI workloads intersect with data residency, compliance, and regional governance requirements. Providers such as Nebius, which has expanded its European AI cloud footprint and announced new AI factory capacity in Finland, illustrate how neocloud providers are aligning to demand for more regionally controlled AI infrastructure.

Such an approach allows enterprises to balance performance, cost, and control while maintaining alignment with ERP governance and compliance requirements.