GenAI Sticker Shock Sends CIOs in Search of AI Ops Solutions
By Carsten Krause, August 7th, 2024
The New Financial Reality of Generative AI
Generative AI has ushered in a transformative era in technology, offering unprecedented capabilities in content creation, language processing, and data analysis. However, alongside the immense potential and excitement, a new financial reality is emerging that many CIOs did not fully anticipate.
As organizations delve into generative AI projects, they are beginning to receive the initial bills for their experimentation and implementation efforts. These costs often exceed expectations, creating a financial strain that requires immediate and strategic attention. The early phase of generative AI adoption is reminiscent of the initial cloud computing boom, where enthusiasm and rapid deployment sometimes led to unchecked spending.
The Burden of Compute-Intensive Workloads
Generative AI, particularly large language models (LLMs) and other advanced AI systems, demands significant computational power. Training and fine-tuning these models require extensive use of GPUs (Graphics Processing Units) or TPUs (Tensor Processing Units), which are both expensive and energy-intensive. The reliance on specialized hardware for these compute-intensive workloads contributes substantially to the overall cost.
The Cost of Innovation Leveraging AI Technologies
James Greenfield, Vice President of AWS Commerce Platform, highlighted the parallels between the current generative AI spending and the early days of cloud computing at the FinOps X conference in San Diego in June. “We’re getting back into this frenetic spend mode that we saw in the early days of cloud,” Greenfield noted. “There’s going to be this day of reckoning a few years down the line where we’re going to need to reign some of those costs back in” (CIO).
Echoing this sentiment, J.R. Storment, Executive Director of the FinOps Foundation, compared the situation to the initial rush to adopt cloud technologies without proper cost control mechanisms. “It’s very reminiscent of the early days of cloud when it was ‘weapons-free’ on spending with everyone trying to implement cloud — and now genAI — everywhere but with little to no cost control or governance” (CIO).
Navigating the Pricing Landscape For AI and Generative AI
The pricing landscape for generative AI is complex, with significant premiums on workloads due to the necessary infrastructure primarily supplied by Nvidia. According to IDC’s “Generative AI Pricing Models: A Strategic Buying Guide,” the interdependencies across the tech stack add to the complexity. Nvidia’s dominance in the GPU market further complicates matters, as CIOs are forced to seek alternatives while awaiting more competitive options (IDC).
Many are turning to AI-specific service providers and cloud offerings from Nvidia, AWS, and Google to host generative AI workloads. These providers, often referred to as AI hyperscalers, offer GPU-as-a-service (GPUaaS), allowing enterprises to purchase GPU power on demand. This on-demand approach helps limit spending and reduces the upfront costs of purchasing processors, enabling organizations to scale their compute power based on workload requirements (IDC).
The AI Ops Service Providers
AI hyperscalers like CoreWeave, Equinix, Digital Realty, and Paperspace, alongside cloud giants Microsoft, Google, and AWS, provide GPUaaS, offering various configurations and cost-saving options. These services enable CIOs to optimize generative AI hardware costs while maintaining the necessary processing power for innovation. Tom Richer, CEO of CloudBench, emphasized the benefits of this approach: “By understanding their options and leveraging GPU-as-a-service, CIOs can optimize genAI hardware costs and maintain processing power for innovation” (CIO).
Implementing Cost Control Measures
To address the high costs associated with generative AI, CIOs are adopting strategic cost management practices. This includes the implementation of FinOps principles specifically tailored for AI workloads (AI FinOps). AI FinOps focuses on achieving cost visibility, implementing performance metrics, and optimizing resource allocation.
J.R. Storment, Executive Director of the FinOps Foundation, stressed the importance of applying these principles to AI expenditures. “We’re seeing the costs of AI spiraling for many and feeding a wave of interest in how to do ‘FinOps for AI’ by applying cost visibility principles already prevalent in FinOps for other cloud costs” (CIO).
Brian Shield, CTO of the Boston Red Sox and Fenway Sports Management, advocates for selective deployment and thoughtful evaluation of generative AI solutions to maximize return on investment (ROI). “I have proposed paying genAI vendors on a per-use-case basis,” Shield explained. “If the tool performs well, that is, production-worthy, I’ll pay you X. For solutions with less than 90% accuracy, if there are still viable use cases, I’ll pay you Y” (CIO).
Innovation in the AI Marketplace
As the landscape of generative AI continues to evolve, the marketplace is witnessing a surge of innovations aimed at addressing the unique challenges and opportunities presented by this transformative technology. These innovations span across hardware, software, and service offerings, each contributing to the optimization of AI operations and the management of associated costs.
Advances in GPU Technology
At the heart of generative AI lies the GPU, a critical component for processing the complex computations required by advanced AI models. The evolution of GPU technology is pivotal in meeting the demands of real-time inference and training. Companies like Nvidia are continuously developing more efficient and powerful GPUs that enhance AI capabilities while striving to reduce costs.
John Marcante, US CIO in Residence at Deloitte and former Global CIO at Vanguard, emphasized the rapid advancements in GPU technology: “The heart of generative AI lies in GPUs. These chips are evolving rapidly to meet the demands of real-time inference and training. As we delve deeper into this innovation cycle, expect GPUs to become even more efficient, capable, and specialized for AI workloads” (CIO).
Emergence of AI-Specific Service Providers
The rise of AI-specific service providers, often referred to as AI hyperscalers, is another significant development in the marketplace. These providers offer GPU-as-a-Service (GPUaaS), enabling organizations to access high-performance computing resources on demand. This model allows enterprises to avoid the substantial upfront costs associated with purchasing GPUs, instead opting for a scalable and flexible solution that aligns with their computational needs.
Examples of AI hyperscalers include:
- CoreWeave: Offers cloud GPU infrastructure for various AI workloads. https://www.coreweave.com
- Equinix: Provides digital infrastructure services, including cloud-based GPU solutions. https://www.equinix.com
- Digital Realty: Specializes in data center solutions with support for AI and machine learning applications. https://www.digitalrealty.com
- Paperspace: Delivers cloud GPUs and AI infrastructure to support development and deployment of AI models. https://www.paperspace.com
Integration of AI into Cloud Platforms
Cloud service providers like AWS, Google, and Microsoft are integrating AI capabilities into their platforms, offering tailored solutions to meet the specific needs of AI workloads. AWS, for instance, has introduced Trainium and Inferentia chips designed to optimize AI training and inference. Google’s Tensor Processing Units (TPUs) provide specialized hardware for AI applications, enabling high-performance computing at scale.
These cloud-based AI solutions offer several advantages, including cost efficiency, scalability, and ease of integration with existing cloud services. This integration supports the seamless deployment and management of AI applications, helping organizations leverage advanced AI capabilities without significant infrastructure investments.
Development of AI-Specific Software and Frameworks
Software innovations are also playing a crucial role in enhancing the efficiency and effectiveness of generative AI. AI-specific software frameworks and tools are being developed to streamline the training, deployment, and management of AI models. These frameworks are designed to optimize resource utilization, improve performance, and reduce costs.
Open-source AI frameworks like TensorFlow, PyTorch, and Apache MXNet provide robust platforms for developing and deploying AI models. These frameworks offer flexibility and customization, allowing organizations to tailor AI solutions to their specific needs while minimizing licensing costs.
Adoption of Domain-Specific AI Models
Another notable trend is the adoption of smaller, domain-specific AI models. These models are tailored to perform specific tasks within particular industries, offering a more efficient and cost-effective alternative to large, general-purpose AI models. By focusing on specific applications, organizations can reduce the computational requirements and associated costs, while achieving high accuracy and performance.
Chris Bedi, Chief Customer Officer at ServiceNow, highlighted the importance of domain-specific models: “Having domain-specific models helps keep our costs under control, which then we’re able to pass along that benefit to our customers” (ServiceNow).
Sustainable Practices and Energy Efficiency
As the environmental impact of AI operations becomes a growing concern, the marketplace is seeing a push towards sustainable practices and energy-efficient solutions. Cloud providers are focusing on developing energy-efficient data centers and leveraging renewable energy sources to power AI workloads. These efforts not only reduce the carbon footprint of AI operations but also help organizations manage energy costs effectively.
Bryan Muehlberger, CTO of Schumacher Homes, pointed out the significance of addressing energy consumption: “AI is compute-intensive and it’s impacting data centers globally. Unless we solve our energy problems nationally, this will eventually become a much bigger problem and the costs will be passed down to the companies using the services” (Schumacher Homes).
Strategic Partnerships and Collaborations
Building strategic partnerships with AI service providers, hardware vendors, and other stakeholders is essential for organizations looking to optimize their AI investments. Collaborations enable companies to access cutting-edge technology, negotiate better pricing, and receive tailored support for their AI initiatives. These partnerships foster innovation and drive the development of more efficient and cost-effective AI solutions.
For instance, Nvidia’s partnership with various cloud providers allows them to offer integrated solutions that combine Nvidia’s GPU technology with the cloud infrastructure of providers like AWS, Google, and Microsoft. These integrated solutions provide enterprises with the flexibility to deploy AI workloads efficiently and cost-effectively.
AI FinOps vs. Traditional FinOps
The practice of FinOps (Financial Operations) involves managing cloud costs effectively, ensuring that businesses get the most value out of their cloud spending through visibility, governance, and optimization. AI FinOps, on the other hand, is specifically focused on the financial management of AI workloads, which present unique challenges due to their intensive computational requirements and reliance on specialized hardware like GPUs.
| Aspect | FinOps | AI FinOps |
|---|---|---|
| Scope | General cloud cost management | Specific to AI workloads |
| Tools | Cloud cost management platforms | GPUaaS, AI-specific cost management tools |
| Challenges | Over-provisioning, lack of cost visibility | High GPU costs, energy consumption |
| Optimization | Rightsizing, reserved instances, spot instances | GPU-as-a-service, domain-specific models |
| Governance | Policies, tagging, cost allocation | Performance-based pricing, selective deployment |
Leveraging Open-Source and Domain-Specific Models
To further reduce costs, many CIOs are turning to open-source models like OpenAI and LLaMA, which offer transparency, customization, and lower running costs. Bern Elliott, a distinguished analyst at Gartner, highlighted the benefits of open-source models. “Open source is one way CIOs can definitely keep the costs low,” Elliott said. “For many enterprises, that’s where the cost is. If the cost of running it is low, the margins become better” (Gartner).
Smaller, domain-specific models are also being used to keep generative AI costs under control. Chris Bedi, Chief Customer Officer at ServiceNow, emphasized the importance of tailored solutions: “Having domain-specific models helps keep our costs under control, which then we’re able to pass along that benefit to our customers” (ServiceNow).
Energy Consumption: A Critical Factor
Another significant cost consideration is the massive energy consumption of generative AI applications. Bryan Muehlberger, CTO of Schumacher Homes, pointed out the impact of AI on data centers globally. “AI is compute-intensive and it’s impacting data centers globally,” Muehlberger said. “Unless we solve our energy problems nationally, this will eventually become a much bigger problem and the costs will be passed down to the companies using the services” (Schumacher Homes).
As organizations continue to explore sustainable practices, utilizing cloud-based GPUs can offer a more energy-efficient solution compared to on-premise hardware. Richer noted that while cloud providers are focusing on sustainability, CIOs must carefully evaluate the trade-offs between cost, performance, and data security when choosing a GPUaaS solution.
Comparison of Measures to Monitor, Control, and Optimize AI Spend
| Measure | Description |
|---|---|
| Cost Visibility | Implement detailed cost monitoring and reporting tools. |
| Performance Metrics | Evaluate AI tools based on output quality and accuracy. |
| Selective Deployment | Deploy AI solutions in key business areas to maximize ROI. |
| Negotiated Pricing | Pay vendors based on performance metrics. |
| GPUaaS | Use GPU-as-a-service to reduce upfront hardware costs. |
| Open-Source Models | Leverage open-source AI models to minimize licensing costs. |
| Domain-Specific Models | Use tailored models for specific tasks to improve efficiency. |
| Energy-Efficient Solutions | Opt for cloud-based GPUs to lower energy consumption and costs. |
Vendors and Solutions for AI FinOps
| Vendor | Solution | URL |
|---|---|---|
| Nvidia | GPU-as-a-Service | https://www.nvidia.com |
| CoreWeave | Cloud GPU Infrastructure | https://www.coreweave.com |
| Equinix | Digital Infrastructure Services | https://www.equinix.com |
| Digital Realty | Data Center Solutions | https://www.digitalrealty.com |
| Paperspace | Cloud GPUs and AI Infrastructure | https://www.paperspace.com |
| AWS | AWS Trainium and Infertia | https://aws.amazon.com |
| Tensor Processing Units (TPUs) | https://cloud.google.com/tpu | |
| IBM | AI and Cloud Solutions | https://www.ibm.com/cloud |
| Oracle | Cloud Infrastructure | https://www.oracle.com/cloud |
| Dell | Project Helix AI Solutions | https://www.delltechnologies.com |
| HPE | GreenLake AI Services | https://www.hpe.com/us/en/greenlake.html |
| RunPod | GPU-as-a-Service | https://www.runpod.io |
| CloudBench | Cloud Cost Management | https://www.cloudbench.io |
| Red Hat | Open-Source AI Solutions | https://www.redhat.com |
Additional Expert Insights:
- CIO Dimension: “GenAI challenges for CIOs include data privacy, transparency, and the limited availability of AI skills. Many organizations are turning to reskilling and upskilling the existing workforce to address these gaps” (CIO Dimension).
- EY: “While many CIOs see the growth potential for GenAI, the most common use cases are IT automation and enhancing the value of their organizations” (EY).
- Intelligent CIO: “The future of GenAI lies in focusing on improvingthe quality of AI solutions rather than achieving artificial general intelligence (AGI)” (Intelligent CIO).
- IT World Canada: “CIOs are advised to strategically integrate GenAI capabilities into business processes, focusing on augmenting business operations and right-sizing AI governance” (IT World Canada).
- CIO AXIS: “GenAI investments are expected to double in 2024, driven by aggressive infrastructure development and widespread adoption across various business activities” (CIO AXIS).
The high costs associated with generative AI are prompting CIOs to explore a range of technologies and methods to manage expenses effectively. By leveraging GPU-as-a-service, open-source models, and domain-specific solutions, IT leaders can optimize their generative AI investments. As innovation continues, the market will likely offer more efficient and specialized GPUs, further aiding in cost control. However, energy consumption remains a critical factor, requiring careful consideration and sustainable practices.
The CDO TIMES Bottom Line
Generative AI promises to revolutionize industries by enhancing capabilities in content creation, language processing, and data analysis. However, the financial implications of implementing and maintaining generative AI systems are substantial and require strategic management. Here’s a summary of the critical points and actionable insights for CIOs navigating the financial landscape of generative AI:
Strategic Cost Management is Crucial
CIOs must adopt a strategic approach to manage the high costs associated with generative AI. Implementing FinOps principles specifically tailored for AI workloads (AI FinOps) can help achieve cost visibility, optimize resource allocation, and maintain financial health. By focusing on cost control measures such as performance metrics, selective deployment, and negotiated pricing, organizations can maximize their return on investment.
Leveraging Cost-Effective Solutions
The reliance on expensive GPUs and other specialized hardware is a significant cost driver for generative AI. Leveraging GPU-as-a-Service (GPUaaS) can reduce upfront costs and allow for scalable, on-demand access to computing power. This approach helps organizations align their spending with actual workload requirements, ensuring efficient use of resources.
Open-Source and Domain-Specific Models
Open-source AI models like OpenAI and LLaMA provide transparency, customization, and lower running costs, making them a cost-effective alternative for many organizations. Additionally, using smaller, domain-specific models tailored to specific tasks can help control costs and improve efficiency.
Energy Consumption and Sustainability
The massive energy consumption of generative AI applications is another critical factor that CIOs must consider. Opting for cloud-based GPUs can offer a more energy-efficient solution compared to on-premise hardware. Organizations should also explore sustainable practices to manage the environmental impact of their AI operations.
Continuous Innovation and Market Adaptation
The generative AI landscape is rapidly evolving, with ongoing innovations in GPU technology and AI capabilities. CIOs must stay informed about the latest developments and be prepared to adapt their strategies accordingly. This includes exploring new AI service providers, platforms, and technologies that can offer improved performance and cost efficiency.
Strategic Partnerships and Vendor Management
Building strategic partnerships with AI service providers and vendors can help organizations negotiate better pricing and access to cutting-edge technology. CIOs should consider performance-based pricing models and maintain an ongoing dialogue with vendors to ensure they receive the best value for their investments.
Actionable Insights for CIOs
- Implement AI FinOps Principles: Adopt financial operations strategies specifically for AI to manage costs effectively.
- Leverage GPUaaS: Use GPU-as-a-Service to reduce upfront hardware costs and scale computing power based on workload requirements.
- Explore Open-Source Models: Utilize open-source AI models to lower licensing costs and improve transparency.
- Focus on Energy Efficiency: Choose cloud-based GPUs and sustainable practices to reduce energy consumption and environmental impact.
- Stay Informed on Market Trends: Keep up with the latest innovations in AI technology to adapt strategies and optimize performance.
- Build Strategic Partnerships: Engage with AI service providers and vendors to negotiate better pricing and access to advanced technology.
Generative AI presents significant opportunities for business transformation, but managing the associated costs is essential for realizing its full potential. By implementing strategic cost management practices, leveraging cost-effective solutions, and staying informed about market developments, CIOs can navigate the financial challenges of generative AI and drive innovation within their organizations.
For more insights and to become a paid subscriber, visit https://www.cdotimes.com/sign-up/.
For the full IDC reports referenced, visit IDC’s “Generative AI Pricing Models: A Strategic Buying Guide” and IDC Market Glance: Generative AI Foundation Models.
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