How much does AI Cost?

As advanced SAP consultants, it is necessary to understand the financial implications of integrating AI into both existing and new SAP environments.

This guide from IgniteSAP aims to provide an overview of the costs associated with SAP AI, so helping to accurately plan and manage these expenses for your organizations or clients. It should be noted that this is based on SAP’s current pricing for AI services, but this may change over time.

While this article focuses on the costs rather than the benefits to the business of AI, it is important to not lose sight of the advantages of AI. In this article we are highlighting some important considerations, but wholeheartedly encourage the adoption of AI in SAP systems.

Understanding SAP AI Offerings

SAP's AI portfolio includes products and services designed to improve business operations. Key offerings include; SAP AI Core and AI Foundation, which provide infrastructure for machine learning, conversational AI, and robotic process automation. SAP Data Intelligence allows for data orchestration, integrating and transforming data from multiple sources to facilitate AI model deployment. SAP AI Business Services automate specific processes, such as invoice matching and customer retention, while SAP Analytics Cloud offers predictive analytics and data visualization to support informed decision-making.

Other notable tools include: SAP Conversational AI for chatbots, SAP Predictive Analytics for data modeling, and SAP Intelligent RPA for automating repetitive tasks. SAP HANA Cloud supports real-time analytics and AI model integration. SAP SuccessFactors leverages AI for HR processes, and the SAP CX AI Toolkit enhances customer experience management. Other features include the Generative AI Hub, Sales Order Auto Completion, Multi Model Management in ISLM, and Joule for AI-driven code generation. SAP Datasphere integrates data and AI platforms, and Predictive Accounting in SAP S/4HANA aids financial planning.

Licensing and Subscription Fees

Many of SAP's AI services are offered on a subscription basis. For instance, SAP Analytics Cloud (SAC) starts at approximately $25 per user per month, with costs increasing for added features and larger deployments.

Similarly, SAP Conversational AI is priced at around $1,500 per month, depending on the number of chatbots and user interactions. These subscription fees provide access to the software and necessary updates but may vary based on the scale and complexity of implementation.

Usage-Based Pricing

SAP employs usage-based pricing for many of its AI services, particularly those involving compute and storage resources. For example, SAP HANA Cloud charges based on the number of compute hours used, data storage volume, and network data transfer. The base rate for compute services is approximately $0.95 per hour, but this can vary with further charges for storage and data transfer. The SAP AI Pricing Estimator Tool (which we will discuss later) can help calculate these costs by inputting specific parameters and usage patterns.

Additional Features and Advanced Modules

Accessing advanced features or extra modules typically incurs extra costs. For example, the SAP AI Business Services suite includes various tools like SAP Cash Application and SAP Service Ticket Intelligence, each with its own pricing model. Costs for these services are calculated based on the number of inference calls made and the models deployed. Custom features and integrations might also require higher licensing fees.

Data Storage and Backup Costs

AI models often require significant amounts of data for training and operation. Over time, this leads to increased data storage needs, which can become a substantial expense. Regular backups are also necessary to ensure data integrity and availability. Backup storage is typically estimated as a percentage of the total data size, often around 14 times the data volume, to accommodate multiple backups and versioning.

Compute Resources

As AI applications scale, the demand for compute resources, including CPUs, GPUs, and memory, increases. High-performance computing (HPC) resources, which are often necessary for training advanced AI models, are considerably more expensive than standard computing resources. This can lead to unexpectedly high operational costs if not planned for appropriately.

Model Maintenance and Retraining

AI models require periodic updates and retraining to maintain accuracy and relevance. This involves additional compute resources and potentially incurs extra costs for data management and model deployment. Regular performance monitoring is also essential to ensure that models are functioning correctly, which can add to the overall expense.

Integration and Customization

Integrating AI solutions with existing SAP systems often involves significant customization and development work. These integration efforts can be time-consuming and costly, especially if third-party services or more SAP modules are required. Professional services for customization and ongoing optimization also contribute to higher costs.

Support and Training

Ongoing technical support and maintenance are necessary to keep AI systems running smoothly. Premium support packages, which provide faster response times and more comprehensive service, can be particularly costly. Training employees to use new AI tools and systems effectively also requires investment in both time and resources.

Security and Compliance

Implementing robust security measures to protect AI data and models from cyber threats is crucial and can be expensive. Ensuring compliance with data privacy regulations, such as GDPR, involves increased administrative and technical efforts, adding to the overall cost.

Long-Term and Cumulative Costs

SAP customers may encounter long-term and cumulative expenses associated with AI integrations. These costs build up over time and can significantly impact the total cost of ownership for AI solutions within SAP systems.

Model Maintenance and Updates

AI models require ongoing maintenance to remain effective. This includes regular updates to the model algorithms and retraining with new data to ensure accuracy and relevance. The process of retraining models necessitates substantial compute resources, particularly for complex models, which can lead to increased costs over time. Maintaining and updating models also involves continuous monitoring and fine-tuning, adding to the overall operational expenses.

Support and Training

Providing continuous support for AI systems is essential to address technical issues promptly and maintain system reliability. Premium support packages, which offer comprehensive service and faster response times, come at a higher cost. Training employees to use AI tools effectively and keeping them updated with the latest advancements in AI technology also require ongoing investment. This includes not only initial training but also periodic refresher courses and advanced training sessions as new features and capabilities are introduced.

Security and Compliance

Ensuring good security measures to protect AI systems from cyber threats and data breaches is critical. Implementing advanced security protocols, conducting regular security audits, and complying with data privacy regulations such as GDPR incur added costs. These measures are necessary to safeguard sensitive data and maintain customer trust, but they add to the long-term financial burden.

Integration and Customization

As AI solutions evolve, further customization and integration efforts may be required to align them with changing business needs. This includes modifying existing integrations, developing new interfaces, and ensuring seamless operation across different systems. Customization projects often involve significant development work and can be costly, especially if they require external expertise or more software licenses.

Performance Optimization

Optimizing the performance of AI models and systems to ensure they operate efficiently and effectively is an ongoing process. This involves fine-tuning algorithms, adjusting resource allocations, and implementing performance-enhancing techniques. Regular performance optimization helps in maintaining system efficiency but adds to the cumulative costs over time.

Unexpected Downtime and Performance Issues

Unplanned downtime or performance issues with AI systems can lead to significant financial losses due to reduced productivity and potential revenue loss. Ensuring high availability and reliability of AI systems requires investment in redundancy measures, backup systems, and rapid incident response capabilities. These precautions are essential to minimize downtime and mitigate its impact on business operations.

SAP AI Pricing Estimator Tool

The SAP AI Pricing Estimator Tool, accessible via the SAP Discovery Center, provides a user-friendly interface for estimating costs associated with various AI services. It allows users to input detailed parameters related to their specific use case, helping to generate precise cost estimates.

Step-by-Step Guide on Using the Tool

Access the Tool

Navigate to the SAP Discovery Center.

Log in with your SAP credentials and locate the Pricing Estimator Tool under the Control Center section.

Creating a New Estimate

Click on the “+ Create New Estimate” tile in the “Estimates” menu.

Select the relevant AI services you plan to use, such as SAP HANA Cloud, SAP Data Intelligence, or specific AI Business Services.

Adding and Customizing Services

Add the services to your estimate by selecting them from the available options.

Customize each service by specifying details such as the number of users, data volume, compute requirements, and expected usage patterns.

For partial usage of services across multiple projects, adjust the percentage of usage accordingly to ensure accurate cost allocation.

Estimating Storage and Backup Costs

Input the total data storage requirements, including primary storage and backup needs.

The tool estimates backup storage as a percentage of the total data size, typically around 20% of the data scanned or updated.

Estimating Compute Costs

Specify the number of virtual CPUs (vCPUs) and the expected compute activities, such as data import, update, and scan operations.

The tool calculates compute costs based on the number of API calls generated by these activities.

Network Data Transfer Costs

Estimate the network data transfer costs by entering the total amount of data expected to be read from the system each month.

The tool provides an estimate based on the specified data transfer volumes, ensuring comprehensive cost calculations.

Finalizing and Sharing the Estimate

Review the estimate to ensure all parameters are correctly entered.

Click “Customize Estimate” to adjust details like the license model (CPEA or Pay-as-you-Go) and the estimation period.

Once finalized, share the estimate with stakeholders using the sharing options available in the tool.

Using the SAP AI Pricing Estimator Tool allows SAP consultants to create precise and detailed cost estimates, facilitating better financial planning and ensuring that all potential costs are accounted for. This proactive approach helps in managing budgets effectively and avoiding unexpected expenses during AI implementation projects.

Conducting a Thorough Cost-Benefit Analysis

A cost-benefit analysis helps in evaluating the potential benefits of AI integration against the associated costs. This analysis should consider both tangible and intangible benefits, such as improved operational efficiency, better decision-making capabilities, and increased customer satisfaction.

List all potential benefits of the AI integration, including cost savings from automation, increased revenue from improved decision-making, and operational efficiencies.

Use the SAP AI Pricing Estimator Tool to calculate all direct costs, including licensing, compute resources, storage, and data transfer fees. Factor in implicit costs such as training, maintenance, and integration efforts.

Compare the estimated costs against the expected benefits to determine the overall value proposition. This comparison helps in making informed decisions about whether to proceed with the AI integration.

Planning for Scalability

As AI applications grow, so do their resource requirements. Planning for scalability ensures that your systems can handle increased workloads without unexpected cost surges.

Invest in scalable infrastructure that can grow with your AI needs. Cloud-based solutions like SAP HANA Cloud offer flexible scalability options that can be adjusted based on demand.

Periodically assess the performance and resource usage of your AI systems. This assessment helps in identifying when to scale up or down based on current and projected workloads.

Include scalability in your financial planning. Allocate budget for potential increases in compute resources, storage, and other infrastructure needs as your AI applications expand.

Continuous Monitoring and Adjustment

Regular monitoring of AI usage and costs is essential to avoid unexpected expenses and ensure optimal resource utilization.

Continuously track the usage of AI resources using tools provided by SAP. Monitor metrics such as compute hours, data storage, and network data transfer to understand cost drivers.

Look for opportunities to optimize costs by adjusting resource allocations, consolidating workloads, and implementing efficiency measures. Regular performance reviews can identify areas where costs can be reduced without compromising functionality.

Based on the monitoring results, adjust budgets and financial plans to reflect actual usage patterns and costs. This proactive approach helps in maintaining financial control and avoiding budget overruns.

Engaging with SAP Partners

Working with SAP partners and consultants provides access to expertise and resources that can facilitate smoother and more cost-effective AI integration.

SAP partners can provide valuable guidance on best practices for AI integration, helping to avoid common pitfalls and ensuring successful implementations.

Partners often have access to greater resources and tools that can aid in cost management and optimization. Leveraging these resources can refine the efficiency of your AI projects.

SAP partners can offer tailored solutions that meet the specific needs of your organization, ensuring that AI implementations are aligned with business objectives and budget constraints.

Final Recommendations

Integrating AI into SAP systems presents both opportunities and challenges. Advanced SAP consultants must be equipped with a comprehensive understanding of the explicit, implicit, and cumulative costs associated with these technologies to provide accurate financial guidance and ensure successful implementations.

To ensure that AI integrations in SAP systems are both successful and financially sustainable, SAP consultants should:

Utilize SAP’s Estimator Tools: These tools provide detailed and customized cost estimates, helping to plan and manage budgets effectively.

Plan for Hidden and Long-Term Costs: Recognize and account for all potential costs, including those that may not be immediately obvious. This comprehensive approach helps in avoiding budget overruns and ensuring financial stability.

Engage with Experts: Collaborate with SAP partners and consultants to leverage their expertise and resources. This collaboration can facilitate smoother implementations and optimize costs.

Monitor and Optimize: Regularly track AI usage and costs, and continuously seek opportunities for optimization. This proactive approach ensures efficient resource utilization and cost control.

While the cost of implementing and successfully maintaining effective AI in SAP systems is something which requires extremely careful assessment, it should be noted that AI can provide organizations with very substantial savings in expenditure over time, and this should be factored into any exploration of financing AI initiatives.

By following the strategies outlined in this guide, SAP consultants can provide valuable financial insights and support successful AI implementations within SAP environments, benefiting their organizations or clients through a more efficient system that supports each business’s strategic goals.

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