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How transparency and outcome pricing are democratizing enterprise AI: The end of hidden AI costs

How transparency and outcome pricing are democratizing enterprise AI: The end of hidden AI costs

How transparency and outcome pricing are democratizing enterprise AI: The end of hidden AI costs – Image: Xpert.Digital

The AI Cost Trap: How to Uncover Hidden Expenses and Save Your Budget

## Faster than Moore's Law: The dramatic price drop in AI is now changing everything ### Numbers by results: How a new pricing model is revolutionizing the AI world ### FinOps for AI: Put an end to uncontrolled costs – how to optimize correctly ### AI for everyone: Why artificial intelligence is now affordable for your company ### Are your AI costs out of control? The truth behind GPU prices and cloud bills ###

What is the current state of FinOps for GenAI?

The explosive proliferation of generative artificial intelligence has made FinOps for GenAI a critical discipline in companies. While traditional cloud workloads have relatively predictable cost structures, AI applications introduce a whole new dimension of cost complexity. The main reasons for rising AI costs lie in the nature of the technology itself: Generative AI is computationally intensive, and costs increase exponentially with the amount of data processed.

A key consideration is the additional resource consumption of AI models. Running and querying data requires large amounts of computing resources in the cloud, resulting in significantly higher cloud costs. Furthermore, training AI models is extremely resource-intensive and costly due to the increased computing power and storage requirements. Finally, AI applications perform frequent data transfers between edge devices and cloud providers, which incurs additional data transfer costs.

The challenge is exacerbated by the experimental nature of AI projects. Companies often experiment with different use cases, which can lead to over-provisioning resources and, consequently, unnecessary expenditures. Due to the dynamic nature of AI models being trained and deployed, resource consumption is difficult to predict and control.

Why are GPU spending and AI costs so difficult to understand?

The lack of transparency around GPU spending and AI costs poses one of the biggest challenges for enterprises. High demand and rising GPU costs often force companies to build costly multi-cloud architectures. A patchwork of solutions from different vendors impairs transparency and hampers innovation.

The lack of cost transparency is particularly evident when using different GPU types and cloud providers. Companies are faced with the challenge of choosing between on-premises GPU investments and cloud-based GPU services. GPU resources are available locally as a shared pool on demand, avoiding the costs of dedicated, but only intermittently used, specialized hardware. However, this creates new complexities in cost allocation and control.

A key problem lies in the unpredictability of variable costs in AI applications. Nearly every AI application is built on foundation models, which incur significant variable costs that scale with model usage. Every API call and every processed token contributes to these costs, representing a fundamental change in the underlying cost structure.

How are the model expenditure costs actually developing?

One of the most remarkable developments in the AI industry is the dramatic decline in model output costs. OpenAI CEO Sam Altman reports that the cost of using a given level of AI decreases tenfold approximately every 12 months. This trend is significantly stronger than the famous Moore's Law, which predicts a doubling every 18 months.

The cost reduction is clearly reflected in the price development of OpenAI models. From GPT-4 to GPT-4o, the price per token fell by approximately 150 times between early 2023 and mid-2024. This development makes AI technologies increasingly accessible to smaller companies and a wide variety of use cases.

Several factors are driving this continuous cost reduction. Competition between model developers and inference providers is creating significant pricing pressure. Open-source models from Meta and others are now approaching GPT-4 performance, further fueling competition. Furthermore, hardware innovations such as specialized chips and ASICs are continuously improving, reducing the cost of inference.

What does workload optimization mean in the AI context?

Workload optimization for AI applications requires a holistic approach that goes beyond traditional cloud optimization. AI workloads can vary dramatically in their compute intensity and storage requirements, making an uninformed approach risky and potentially leading to significant prediction errors and resource waste.

Optimizing compute resources is at the heart of AI cost optimization. Computational costs are typically the largest expense in GenAI operations. Proper sizing of GPUs, TPUs, and CPUs is crucial: choosing the lightest accelerator that still meets latency and accuracy SLOs is key. Each step to a higher silicon class increases hourly costs by 2-10 times, without guaranteeing a better user experience.

GPU utilization strategies play a central role in cost optimization. Unused watt-hours are the silent killer of GenAI budgets. Multi-tenancy and elastic clusters transform parked capacity into throughput. Pooling and MIG slicing allow A100/H100 GPUs to be partitioned and namespace quotas to be enforced, typically resulting in a jump in utilization from 25 to 60 percent.

How does outcome-based pricing work in practice?

Outcome-based pricing models represent a fundamental shift in the way companies think about monetizing AI technologies. Instead of paying for access to the software or its use, customers pay for tangible results – such as successfully resolved sales or support conversations.

These pricing models create a direct financial alignment between AI providers and their customers. When a provider only benefits when its solution delivers measurable results, both parties share the same definition of success. According to McKinsey research, companies that use outcome-based technology pricing models report 27 percent higher satisfaction with provider relationships and a 31 percent better return on investment compared to traditional pricing arrangements.

AI plays a crucial role in enabling outcome-based pricing models. The technology provides the predictive analytics, automation, and real-time insights necessary to implement such models. AI systems can track and measure performance and ensure that promised results are actually achieved.

What role does transparency play in AI cost optimization?

Transparency is the foundation of any effective AI cost optimization strategy. Without clear visibility into resource utilization, companies can neither understand the true costs of their AI projects nor make informed optimization decisions. The need for transparency is reinforced by the experimental nature of AI development and the unpredictability of resource requirements.

A key element of transparency is granular cost tracking. Companies need detailed insights into costs per model, per use case, and per business unit. This requires specialized monitoring tools that go beyond traditional cloud cost management and can capture AI-specific metrics such as token consumption, inference costs, and training effort.

Implementing cost transparency encompasses several key areas. These include tracking API usage and token consumption for cloud-based AI services, monitoring GPU utilization and energy consumption for on-premises solutions, and allocating costs to specific projects and teams. Modern tools offer visual dashboards that highlight cost-saving opportunities and help teams make data-driven decisions.

 

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Outcome pricing: The new era of digital business models

How can companies identify hidden AI costs?

Hidden AI costs are one of the biggest challenges for companies implementing artificial intelligence. Zachary Hanif of Twilio identifies two main categories of hidden AI costs: technical and operational. Technically, AI differs fundamentally from traditional software because an AI model represents the state of the world at a specific point in time and is trained with data that becomes less relevant over time.

While traditional software can manage with occasional updates, AI requires continuous maintenance. Every AI investment requires a clear maintenance and monitoring plan with defined retraining intervals, measurable metrics for performance evaluation, and defined thresholds for adjustments. Operationally, many companies lack clear goals and measurable results for their AI projects, as well as defined governance and shared infrastructure.

Identifying hidden costs requires a systematic approach. Companies should first identify all direct and indirect costs associated with implementing and operating AI solutions. These include software licenses, implementation costs, integration costs, employee training costs, data preparation and cleansing, and ongoing maintenance and support costs.

What are the challenges in measuring the ROI of AI investments?

Measuring the return on investment (ROI) of AI investments presents unique challenges that go beyond traditional IT investments. While the basic ROI formula remains the – (return – investment cost) / investment cost × 100 percent – the components of AI projects are more complex to define and measure.

A key challenge lies in quantifying the benefits of AI. While direct cost savings from automation are relatively easy to measure, the indirect benefits of AI are more difficult to capture. These include improved decision quality, increased customer satisfaction, faster time to market, and increased innovation. While these qualitative improvements have significant business value, they are difficult to translate into monetary terms.

The time component presents another challenge. AI projects often have long-term effects that extend over several years. For example, a company invests €50,000 in an AI-powered customer service system, saving €72,000 annually in personnel costs. This results in an ROI of 44 percent and pays for itself in about eight months. However, the cost-benefit ratio can change over time due to model drift, changing business requirements, or technological developments.

How is the democratization of enterprise AI developing?

The democratization of enterprise AI is taking place on several levels and is being driven primarily by the dramatic reduction in the cost of AI technologies. The continuous tenfold reduction in model costs annually is making advanced AI capabilities accessible to a wider range of companies. This development enables small and medium-sized enterprises to implement AI solutions that were previously reserved only for large corporations.

A key driver of democratization is the availability of user-friendly AI tools and platforms. AI tools for small businesses have become increasingly affordable and user-friendly, designed to address specific needs without requiring a team of data scientists. This development enables small teams to achieve enterprise-level results, from handling customer inquiries to optimizing marketing campaigns.

The impact of this democratization is significant. Studies show that small and medium-sized enterprises can increase their productivity by up to 133 percent through the targeted use of AI, with an average increase of 27 percent. Companies that already use AI technologies benefit particularly in areas such as human resources management and resource planning.

What is the importance of sustainable AI investments?

Sustainable AI investments are becoming increasingly important as companies must consider both the environmental impact and the long-term economic viability of their AI initiatives. The energy consumption of AI applications has become enormous – the training of GPT-3 is estimated to have generated over 550 tons of CO₂, comparable to the annual CO₂ emissions of over 100 cars. By 2030, the energy demand of data centers in Europe is expected to rise to 150 terawatt hours, approximately five percent of total European electricity consumption.

At the same time, AI offers significant opportunities for sustainable solutions. AI can significantly reduce energy consumption in factories, put buildings on a carbon-saving course, reduce food waste, or minimize the use of fertilizers in agriculture. This dual nature of AI – being both part of the problem and part of the solution – requires a thoughtful approach to AI investments.

Sustainable AI investment strategies encompass several dimensions. First, the development of energy-efficient AI models using techniques such as model compression, quantization, and distillation. Second, the use of renewable energy sources for training and operating AI systems. Third, the implementation of Green AI Principles, which serve as guidance for all AI development and implementation.

How does outcome pricing influence business models?

Outcome-based pricing is revolutionizing traditional business models by redefining the risk-reward distribution between providers and customers. AI is driving a shift away from static, seat-based pricing models to dynamic, outcome-based pricing structures. In this model, providers are paid only when they deliver value, aligning incentives for companies and customers.

The transformation is evident in three key areas. First, software is becoming labor: AI is transforming what were once purely service businesses into scalable software offerings. Traditional services that require human labor – such as customer support, sales, marketing, or back-office financial administration – can now be automated and packaged as software products.

Second, the number of user seats is no longer the atomic unit of software. If AI can take over a large portion of customer support, for example, companies will need significantly fewer human support agents and, consequently, fewer software licenses. This forces software companies to fundamentally rethink their pricing models and align them with the results they deliver rather than the number of people accessing their software.

What role do measurable ROI metrics play?

Measurable ROI metrics form the backbone of successful AI investment strategies and enable companies to quantify the true value of their AI initiatives. Defining specific key performance indicators (KPIs) is crucial for a precise ROI calculation. Important KPIs include the cost per unit before and after AI implementation, with a significant reduction in costs being a strong indicator of a positive ROI.

Time savings through automated processes can be directly factored into the ROI, as the time saved can be monetized. Reducing error rates and improving quality also have an indirect impact on ROI, as they increase customer satisfaction and strengthen long-term customer loyalty. Additionally, the extent to which employees use AI solutions and the impact this has on their productivity should be measured.

A practical example illustrates the ROI calculation: A company invests €100,000 in an AI solution for its sales contact center. After one year, the lead-to-sales conversion rate increases by five percent, resulting in additional revenue of €150,000. Sales staff efficiency increases by ten percent, corresponding to personnel cost savings of €30,000. The cost per qualified lead decreases by 20 percent, resulting in marketing savings of €20,000. The total benefit is €200,000, resulting in an ROI of 100 percent.

 

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FinOps 2.0: Strategies for managing AI costs

How can companies develop a FinOps strategy for AI?

Developing an effective FinOps strategy for AI requires a structured, eight-step approach that incorporates both traditional cloud FinOps principles and AI-specific challenges. The first step is to establish a strong foundation by forming an interdisciplinary team across finance, technology, business, and product functions. This team must work closely together to understand and manage the unique aspects of AI workloads.

The second step focuses on implementing comprehensive visibility and monitoring systems. AI workloads require specialized monitoring that goes beyond traditional cloud metrics and includes AI-specific metrics such as token consumption, model performance, and inference costs. This granular visibility enables companies to identify cost drivers and identify optimization opportunities.

The third step is implementing cost allocation and accountability. AI projects must be assigned to clearly defined business units and teams to ensure financial accountability. The fourth step involves establishing budgets and spending controls, including implementing spending limits, quotas, and anomaly detection to avoid unexpected cost increases.

What impact does cost reduction have on new business models?

The dramatic reduction in the cost of AI technologies – by a factor of ten annually – is opening the door to entirely new business models and use cases that were previously not economically viable. Sam Altman of OpenAI sees this development as having the potential for an economic transformation similar to the introduction of the transistor – a major scientific discovery that scales well and penetrates almost every sector of the economy.

Cost reduction enables companies to integrate AI capabilities into areas where they were previously too expensive. Lower prices lead to significantly increased usage, creating a virtuous circle: Higher usage justifies further investment in the technology, leading to even lower costs. This dynamic democratizes access to advanced AI capabilities and enables smaller companies to compete with larger rivals.

Altman predicts that the prices of many goods will fall dramatically as AI reduces the cost of intelligence and labor. At the same time, however, luxury goods and some limited resources, such as land, could rise even more dramatically in price. This polarization creates new market dynamics and business opportunities that companies can exploit strategically.

What does the future of AI cost optimization look like?

The future of AI cost optimization is shaped by several converging trends. AI-driven cloud cost management can already reduce expenses by up to 30 percent and enables real-time insights and efficient resource allocation. This development will further accelerate with the integration of machine learning into cost optimization tools.

A key trend is the development of smarter purchasing recommendations and cost transparency tools. AWS and other cloud providers are continuously improving their cost management tools to provide better insights and recommendations. For example, AWS's recommendation tool identifies optimal purchasing options based on historical consumption, facilitating proactive planning of cost-saving strategies.

The future also sees greater standardization of AI cost metrics. The development of FOCUS (FinOps Open Cost and Usage Specification) 1.0 enables companies to export cost and usage data in a uniform format. This significantly facilitates the analysis of cloud spending and the identification of optimization opportunities.

What role does technological evolution play in reducing costs?

The continuous evolution of the underlying technologies plays a central role in the dramatic cost reduction in the AI industry. Significant hardware innovation is driving costs down, with specialized chips and ASICs like Amazon's Inferentia and new players like Groq. While these solutions are still in development, they are already demonstrating dramatic improvements in both price and speed.

Amazon reports that its Inferentia instances deliver up to 2.3 times higher throughput and up to 70 percent lower cost per inference than comparable Amazon EC2 options. At the same time, efficiency on the software side is continuously improving. As inference workloads scale and more talent is employed in AI, GPUs are utilized more effectively, resulting in economies of scale and lower inference costs through software optimizations.

A particularly important aspect is the rise of smaller, but more intelligent models. Meta's Llama 3 8B model performs essentially the same as its Llama 2 70B model, released a year earlier. Within a year, a model with nearly a tenth of the parameter size and the same performance was created. Techniques such as distillation and quantization make it possible to create increasingly capable, compact models.

How does democratization affect the competitive landscape?

The democratization of AI technologies is fundamentally changing the competitive landscape and creating new opportunities for companies of all sizes. The continuous cost reduction of AI models enables smaller companies to utilize technologies that were previously only available to large corporations with substantial IT budgets. This development is leveling the playing field, where innovative ideas and implementation are becoming more important than pure financial resources.

The effects are already measurable: Small and medium-sized enterprises can increase their productivity by up to 133 percent through targeted use of AI. These productivity gains enable smaller companies to compete with larger competitors in areas where they have traditionally been disadvantaged. AI-powered automation takes over routine tasks and frees up valuable time for strategic initiatives.

Democratization is also leading to a fragmentation of the AI services market. Whereas a few large providers once dominated the market, numerous specialized solutions for specific industries and use cases are now emerging. This diversification creates more choice for companies and drives innovation through competition. At the same time, new challenges arise in integrating different AI tools and ensuring interoperability.

What strategic recommendations arise for companies?

Several strategic imperatives arise for companies seeking to benefit from the AI cost revolution. First, companies should develop a comprehensive FinOps strategy for AI that goes beyond traditional cloud cost management. This requires specialized teams, tools, and processes that address the unique characteristics of AI workloads.

Second, companies should establish transparency as a core principle of their AI investments. Without clear visibility into costs, performance, and business value, informed decisions cannot be made. This requires investments in monitoring tools, dashboards, and reporting systems that can capture and display AI-specific metrics.

Third, companies should favor outcome-based approaches when evaluating and procuring AI solutions. Instead of paying for technology features, they should evaluate and compensate providers based on measurable business outcomes. This creates better incentive alignment and reduces the risk of AI investments.

Fourth, companies should consider the long-term sustainability of their AI investments. This includes both ecological sustainability through energy-efficient models and green data centers, as well as economic sustainability through continuous optimization and adaptation to changing cost structures.

Fifth, companies should view the democratization of AI as a strategic opportunity. Smaller companies can now implement AI capabilities that were once prohibitively expensive, while larger companies can expand their AI initiatives into new areas and use cases. This development requires a reassessment of competitive strategies and the identification of new opportunities for differentiation and value creation.

 

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