Research for this report was conducted using various AI services, including Perplexity for language modeling and ChatGPT for conversational AI, and was edited with the help of Grammarly. ©TrustBolt.ai All rights reserved. March 2025

Executive Summary

If you haven't heard, the adoption of generative AI is not just a trend but a growing force that is expected to continue to accelerate over the next one to two years. Enterprises are increasingly learning to integrate this latest technology into their automation, decision-making, and content generation.

Many, if not most, organizations are rapidly experimenting with AI across various business functions. The need for a well-structured and thoughtful cost-management strategy is becoming increasingly apparent, providing a solid foundation for decision-making and instilling confidence in further innovation.  

Here, we identify five common AI cost drivers and approaches to manage these costs effectively.

Understanding Cost Drivers

To effectively control costs, enterprises must first understand the five key drivers associated with the total cost of ownership of third-party generative AI platforms. This knowledge will empower you to make informed decisions and implement cost-effective strategies, ensuring you are prepared for the challenges ahead. 

1. Subscription and Licensing Fees – The amount that 3rd party AI vendors invoice you each period.  Costs often vary based on several factors, including.
   1. Subscription Model—i.e., Usage-based or User Based
   2. Volumes—i.e., companies with larger contracts and commitments can often negotiate tiered pricing structures based on expected volumes.
   3. Prompt/Response Complexity—The more complicated the prompt and the more in-depth the resulting answer, the higher the costs.
   4. Model Complexity—Providers are constantly delivering new and improved models at a rapid pace of development. With these new models come increased capabilities and often higher prices.
2. Data Storage and Processing – Additional expenses arise from storing, securing, and managing AI-generated content.
3. Compliance and Security Investments – Necessary to meet regulatory standards, protect intellectual property, and safeguard user privacy.
4. Infrastructure and Integration Costs – Expenses related to integrating AI solutions with existing enterprise systems and workflows.
5. Scalability and Performance Optimization – Scaling AI usage across business units may introduce additional computational costs and performance considerations.

Cost Management Strategies

After understanding the drivers behind the cost of adapting AI, enterprises can develop strategies to use these capabilities effectively and efficiently and ultimately control costs.

Apply AI Thoughtfully 

• Define clear AI use cases aligned with strategic goals to ensure targeted, cost-effective deployment.
• Prioritize AI investments in high-value, high-volume areas. Examples include customer service automation, fraud detection, market research, and real-time business intelligence.
• Establish a governance and monitoring program to assess the long-term cost-effectiveness and sustainability of AI use is essential to promoting responsible deployment.
• Promote transparency in AI-driven decision-making processes, to build trust among employees, stakeholders, and customers, fostering a culture of openness and understanding.
• Implement role-based access controls to limit AI usage to essential personnel and business units based on approved use cases.
• Prioritize AI selectively for high-impact, value-generating tasks such as automating customer service, enhancing predictive analytics, and streamlining content generation.
• Develop guidelines for AI utilization to avoid over-reliance on expensive cloud-based generative AI models for non-essential tasks.
• Avoid using AI where it is not needed.  Apply AI to problems that it can uniquely solve. For instance, use AI for complex data analysis, but for routine administrative tasks, traditional solutions may suffice.  Eliminate first, then automate!

Negotiate Enterprise Agreements

• Engage in volume-based pricing discussions with AI vendors to secure more favorable contracts.
• Opt for enterprise-tier solutions that offer predictable and scalable pricing models.
• Explore hybrid approaches, such as integrating open-source AI models for non-critical tasks to reduce reliance on high-cost proprietary platforms.
• Conduct periodic contract reviews to ensure cost-effective licensing and avoid underutilized service subscriptions.

Monitor and Control AI Expenditure

• Establish a governance framework for AI usage, budget allocation, and cost tracking.
• Deploy AI usage dashboards to monitor real-time expenses, detect inefficiencies, and identify cost-saving opportunities.
• Set spending thresholds, enforce approval workflows for high-cost AI functions, and implement automated alerts for API calls that exceed budgeted limits.
• Provide transparency of costs back to individuals, teams, and functions leveraging AI services.
• Conduct periodic cost audits to assess AI efficiency and align spending with business goals.
• Leverage lower-cost models for more straightforward, lower-value activities.
• Deploy internally hosted solutions for general en-mass rollouts.

Optimize AI Usage Through Fine-Tuning and Multi-Model Implementations

• Train AI models on proprietary company data to improve relevance, accuracy, and efficiency while reducing excessive API usage.
• For cost-sensitive applications, use a mix of third-party AI and internally hosted models, balancing performance with affordability.
• Deploy smaller, fine-tuned AI models for domain-specific tasks instead of relying solely on general-purpose AI systems.
• Evaluate the cost-benefit ratio of on-demand AI services versus pre-trained models for recurring enterprise use cases.
• Leverage prompt-catching mechanisms for the frequency used or similar prompts and responses to reduce redundant queries.
• Introduce prompt batching to group multiple queries into a single request especially where multiple responses relate to the large input data sets.
• Invest in training and prompt engineering to ensure efficient query structuring to minimize redundant calls.

Address Compliance and Security Efficiently

• Adopt AI solutions with built-in compliance and security features to minimize regulatory overhead.
• Store and process sensitive within your company boundaries, while leveraging external AI selectively for less sensitive operations.
  Implement privacy-preserving AI techniques such as differential privacy, encryption, and secure multi-party computation to mitigate security risks.
• Conduct AI risk assessments regularly to ensure cost savings do not override data security and compliance obligations.

Conclusion

By implementing structured cost-management strategies, enterprises can mitigate excessive AI expenses while harnessing the full potential of generative AI platforms. Through usage optimization, governance frameworks, and strategic investments in AI efficiency, businesses can maximize ROI while maintaining financial control over AI adoption.

To learn more about how TrustBolt can help you deploy AI with confidence, visit us at https://www.trustbolt.ai/learn.