How can SMBs reduce cloud costs for AI?

Executive summary

Rapid artificial intelligence (AI) adoption has exposed the flaws in traditional cloud infrastructure and its business models. Poorly optimized systems cannot adapt to accelerating demand, leading to overprovisioning, excessive egress fees, and highly unpredictable costs. Almost all (94%) IT leaders say they are struggling with cloud costs as a result.¹

This white paper explores how companies can reduce cloud costs while embracing AI. Rather than cutting AI budgets or stalling pilot projects, we show how smart leaders optimize their spend to deliver reliable, scalable cloud solutions without unexpected spikes, vendor lock-in, or wasted compute.

Readers can expect to learn:

• Why AI adoption is crucial for small and medium-sized businesses (SMBs)
• What drives cloud cost inflation and which aspects of AI exacerbate the problem
• How to balance AI workflows with cost optimization for maximum value

The AI adoption dilemma

Why SMBs cannot afford to fall behind

The race to integrate AI will define the next five years for most businesses. IBM recently surveyed a range of business executives and found that:

For many of these respondents, the survival of their company rests almost entirely upon AI-driven gains. The technology has become their only viable path forward. Executives don’t just expect AI to make operations more efficient; they expect it to become a central strategic pillar, with projections that two-thirds of AI spend will be focused on product, service, and business model innovation by 2030.³

The potential for breakaway competitive advantage is clear: if AI can start driving faster, more successful innovation, it will be nearly impossible for rival firms to catch up. But the flip side of that equation is that almost every leader is racing against the clock — and many believe their professional future hangs in the balance.

One survey found that 79% of CEOs expect to lose their job within two years if they fail to deliver AI-driven business gains.⁴ That sentiment is based in reality: more than half of survey respondents state that their board should resign if the company loses market share to competitors due to inadequate AI strategy.⁵

That pressure presents leaders with a dilemma: slow adoption puts their business and personal futures under threat, but accelerated AI implementation risks spiraling costs that might render the program unviable before it delivers significant enough returns.

The (cloud) costs of AI adoption

While AI is often sold to radically increase efficiency, most companies’ bottom lines tell a different story. Workers save time; operations get streamlined; but the engine powering these improvements — namely, cloud computing — is becoming less efficient for many businesses.

Almost 30% of companies say their cloud costs rose by 25% or more last year,⁶ primarily driven by AI-related projects. Such increases might be tolerable if they were just temporary growing pains; short-term pricing spikes could be offset by rocketing productivity and profitability driven by the AI outputs. The reality is these cost increases will persist until companies start to rethink their cloud infrastructure, with the potential to stall AI adoption at the exact moment when leaders face the greatest pressure to accelerate adoption.

Recent research shows that AI projects are impacting cloud costs in two ways:

1. Higher compute requirements

AI workloads increase cloud costs through both training and inference:

• Training large models requires sustained access to high-performance compute clusters, often composed of hundreds to thousands of GPUs or TPUs operating in parallel for days or weeks. These specialized accelerators are priced much higher than standard CPUs, and distributed training increases networking, storage I/O, and orchestration costs.
• Inference — the stage where a pre-trained AI model analyzes new data and generates novel outputs — drives up costs through operational scale and latency. Models of substantial size must be kept resident in memory to meet low-latency SLAs, and serving millions of requests concurrently requires excess capacity and effective load balancing. Equally, the most inference workloads ultimately return results to users or systems outside the cloud network, which most providers charge outbound data transfer fees.

The impact on overall cloud spend is clear: 41% of IT budgets are now directed toward scaling cloud capabilities, largely to support AI workloads.¹⁰ Cloud compute is the second largest expense at many IT and SaaS companies, trailing only payroll and other employee-related spending.¹¹

2. Increased cost fluctuation

AI-related cloud spend is harder to predict than standard compute:

• Model training introduces irregular, high-magnitude spending spikes driven by experimental cycles, retraining schedules, and architectural changes; a single training run can consume weeks of GPU time, and small changes in model design or data can materially alter cost.
• Inference costs scale directly with user adoption and usage patterns, often in real time, making them sensitive to product launches, seasonality, and unexpected demand surges.
• Research and experimentation, such as hyperparameter sweeps, A/B tests, and parallel model variants, generate “noise” in cloud usage that is difficult to attribute to stable workloads or planned initiatives.¹²

The net result is not just higher overall costs, but more unexpected or hidden costs. Monthly cloud spend varies wildly; gross margins are repeatedly hit by surprise cloud bills; and AI starts to look less like the future of business and more like a financial liability. While occasional price spikes might be acceptable, the total inability to forecast costs makes executives wary of AI.

Those concerns are now leaking into the boardroom: 43% of chief information officers (CIOs) say their CEOs and directors have concerns about their company’s cloud spend.¹⁶ And one study found that over one-quarter of companies are reducing AI budgets due to increased cloud spending.¹⁷

But can leaders really afford to rein in AI adoption?

The danger of stalled AI adoption

We’ve discussed AI’s potential to power breakaway growth, but the flip side is its potential to render companies obsolete. When one company achieves sudden exponential gains in efficiency and scale, competitors may be wiped out before they can respond.

Slow or stalled AI adoption is therefore a serious risk. The problem is that mitigating that risk may prove a financial luxury many small and medium-sized businesses (SMBs) cannot afford based on current cloud spending trends.

Businesses with at least $500 million in annual revenue are already adopting AI more quickly than smaller organizations¹⁸; these are companies that might find cloud costs painful in the short term but have the necessary scale and resources to offset them against long-term projected gains.

Smaller businesses can’t match those resources or compete on scale. Instead, they must compete through better cloud strategy. The answer is not to swallow spiraling cloud costs, but to bring them back under control. And they can do that by identifying the real root of the problem.

Cloud cost challenge

For many organizations, the problem with AI-driven cloud costs stems from poorly optimized infrastructure.

Over one-third of mid-market companies feel they “rushed” into complex cloud migrations in recent years¹⁹; the pressure to reap business benefits — such as cost savings and increased efficiency — led Cloud Architects, IT Directors, and Heads of Infrastructure to move their business to the cloud without sufficient preparation.²⁰

Many now admit to “cutting corners” and feel their infrastructure does not meet performance requirements.²¹ Complex AI workflows expose these weaknesses, as teams often lack the flexibility or visibility required to avoid heavy overprovisioning; simultaneously, reliance on hyperscalers leaves them vulnerable to recent egress and ingress data transfer charges.

These challenges are also seen across larger organizations. One survey of Global 2000 enterprises found that nearly all senior executives are having second thoughts about their cloud service provider, with one-quarter having terminated a hyperscaler contract in the last 12 months.²²

Inflexible or poorly optimized infrastructure leaves organizations at a range of risks, including:

1. Vendor lock-in

Poorly optimized cloud infrastructure often embeds proprietary services and provider-specific AI tooling that limit portability. As AI workloads scale and GPU pricing diverges across regions and vendors, this lock-in removes the ability to shift workloads to lower-cost options, forcing organizations to absorb higher rates and capacity premiums.

2. Unexpected fee hikes

Vendor lock-in leaves organizations vulnerable to cost creep. Without protection against sudden egress and ingress fee spikes, organizations may find themselves with a heavily inflated bill due to AI inference. When AI workloads rely on inefficient data movement, always-on capacity, or tightly coupled managed services, even modest increases in GPU, networking, or egress pricing can hit margins hard.

Lack of visibility

Without clear workload isolation, tagging, and usage telemetry, AI costs become opaque. Training, inference, and experimentation blend together, obscuring which models or teams drive spend and preventing targeted optimization. This allows unnecessary costs to persist and scale unchecked.

However, all of these challenges are addressable with the right cloud infrastructure.

How SMBs make cloud costs work with rapid AI adoption

Standard cloud infrastructures and hyperscaler-managed services were developed before AI became mission-critical for SMBs. Rather than reimaging their businesses, IT leaders are left to work around legacy impediments, which leads to slower, less impactful AI implementations.

Instead, look for a cloud vendor that gives teams the freedom to develop, train, and deploy AI with open tools, predictable costs, and infrastructure that’s built for the future.

1. Maximize cloud flexibility

Leverage a solution built on open standards, including OpenStack and Kubernetes, for optimal portability and freedom from vendor lock-in.

• Enable achievable workload portability across clouds and on-premises environments
• Accelerate experimentation and iteration with flexible support for multiple AI frameworks
• Integrate AI pipelines easily with third-party tools and hybrid infrastructures
• Optimize costs and performance by moving workloads to the most efficient resources
• Preserve strategic control by avoiding vendor lock-in and proprietary constraints

2. Reduce and predict cloud spend

Choose a platform that eliminates ingress and egress charges and offers pay-as-you-go pricing to make cloud costs reliable:

• Deploy machine learning and generative AI models while maintaining predictable spend
• Protect margins and maximize the ROI of early AI programs to win buy-in for wider implementation
• Optimize performance without creating extra financial risk or incurring hidden costs

3. Scale and tailored cloud usage

Your business will change, so consider a vendor like OVHcloud that offers hundreds of variations of servers, enabling you to tailor compute and storage to fit your exact needs — and scale on demand:

• Minimize over-and-under provisioning by matching compute and storage exactly to workload needs
• Leverage 450,000 servers in 46 data centers across four continents
• Benefit from always-on DDoS mitigation, included at no extra cost

The result is a platform built to unlock your competitive advantage, not limit it.

About OVHcloud

OVHcloud US is a subsidiary of OVHcloud, a global player and Europe’s leading cloud provider operating more than 450,000 servers within 46 data centers across four continents. For more than 20 years, the company has relied on an integrated model that provides complete control of its value chain, from the design of its servers to the construction and management of its data centers, including the orchestration of its fiber-optic network. This unique approach allows it to independently cover all the uses of its 1.6 million customers in more than 140 countries. OVHcloud now offers latest-generation solutions combining performance, price predictability, and total sovereignty over their data to support their growth in complete freedom.