As artificial intelligence (AI) continues to revolutionize industries and drive innovation, organizations face the critical challenge of effectively planning and estimating the costs associated with deploying AI solutions. This paper serves as a comprehensive guide, illuminating the intricate financial considerations accompanying various AI deployment models. By delving into the nuances of third-party vendor commercial closed-source services, third-party hosted open-source models, and do-it-yourself approaches on public cloud providers’ AI-centric services and systems, these learnings equip readers with the knowledge necessary to make informed decisions. Moreover, it underscores the paramount importance of aligning AI investments with tangible business value, emphasizing the intricate interplay between cost, accuracy, and performance.
Prerequisites
This paper contains beginner to advanced-level topics and approaches. Readers should have some introductory background knowledge of text-based AI large language model (LLM) services and the available deployment methods in use or planned by their enterprise today. The report follows on from a series on how to forecast AI services costs in the cloud, as guided by the FinOps Foundation.
Cloud AI Deployments and Pricing Models
1. Third-Party Vendor Commercial Closed-Source Services
Examples: Chatbots, image generation, speech recognition, and fraud detection solutions by companies like OpenAI, Google, and Microsoft.
Pros:
- Rapid deployment.
- High-quality, reliable models.
- Robust customer support.
Cons:
- Limited customization.
- Pricing risks due to proprietary technology.
- Privacy and bias concerns.
2. Third-Party Hosted Open-Source Models
Examples: NLP for sentiment analysis, predictive maintenance, and autonomous vehicle AI systems on platforms like Anyscale, Replicate, Groq, or Hugging Face.
Pros:
- Greater control and flexibility.
- Compliance with privacy and security standards.
- Community support.
Cons:
- Higher technical expertise required.
- Privacy and security default standards may not be as robust.
- Longer time to results and iterating new ideas.
3. DIY on Cloud Providers AI-Centric Services/Systems
Examples: Text chatbots, code copilots, recommendation systems, and medical image interpretation on AWS Sagemaker, GCP Vertex AI, or Azure AI.
Pros:
- Full control over models and data.
- Customizable cost management.
- Integration with broader cloud ecosystems.
Cons:
- Significant expertise required.
- Longer development and deployment times.
Cost Drivers
- Training Costs: While open-source models can be more cost-effective, training them for specific tasks can be resource-intensive. Training costs can range from 5-15% of the total cost of a model throughout its lifecycle.
- Production Inference and Serving Client Requests: Primary costs include computing, database, storage, monitoring, and network layers.
- Customization and Maintenance: Ongoing maintenance and customization add to the total cost of ownership.
- Infrastructure Costs: Expenses associated with the computational resources (CPUs/GPUs) required for training and inference.
- Operational Costs: Including data storage, network usage, and labor costs associated with developing, training, and maintaining models.
AI Cost Planning Strategies
1. Business Summary for AI Cost Planning
Quality of an AI product, process, or workload lies with product managers, architects, and workload engineers. Merely measuring the consumption of AI tokens does not reflect the actual tasks performed or the value delivered to customers.
2. AI Cost Forecasting and Planning Framework
Establish a robust AI cloud cost forecasting and planning framework to manage overall AI-specific services expenditure and optimize resource allocation.
3. Rollup Spend Forecast
A high-level rollup spend forecast offers finance and senior leaders a holistic view of incremental AI services costs, facilitating strategic decision-making and budget allocation.
AI Costing Methods, Approaches, and Examples
Key Factors for Accurate Cost Forecasting:
- Understanding cost drivers and AI pricing trends.
- Mastering capacity planning.
- Adapting to frequent innovations impacting deployment usage/costs.
- Aligning financial outlays with business value.
- Driving efficiency within cloud AI service operations as part of broader programs.
Cost Drivers for Various AI Systems
- Computing Resources: AI workloads often require significant computational power.
- Data Storage and Transfer: High costs for storing and transferring large amounts of data.
- AI Model Training: Training AI models can be time-consuming and computationally intensive.
- AI Model Serving: Deploying AI models in a production environment can be costly.
- AI Model Monitoring and Maintenance: Ongoing monitoring and maintenance are required.
- Cloud Provider Fees: Fees for using infrastructure and services from cloud providers.
Cost Estimators
Third-Party Vendor Closed System Cost Estimators
- Current vendor pricing.
- Platform model types related to the current vendor.
- Sample prompt and scenario areas for user experience.
DIY Cloud Provider AI-Specific SKUs and Services Cost Estimators
- Tokens with a model and cost inputs.
- Updated prompts for better cost estimation.
- Decision-making between dedicated instances vs per-token billing.
- Quantization to reduce computational requirements and costs.
- Fine-tuning for specific tasks to improve model performance.
Benchmarking and Capacity Planning
Benchmarking should be done periodically to ensure capacity is performing as expected. Capacity planning considerations include redundancy, utilization metrics, and vendor capabilities.
Indicators of Success:
- AI shared services and company-wide level cloud service forecasting is within 90%+ accuracy.
- Standard approaches produce consistently accurate spend plans for workloads.
- Reduced bill shock from AI poor planning and reactionary tasks.
- Understanding cloud provider levers when scaling larger capacity needs.
By adopting these comprehensive strategies and tools, organizations can navigate the complexities of AI cost estimation and planning, ensuring their AI investments deliver long-term value and align with strategic objectives.
How VE3 Adds Value
At VE3, we specialize in guiding organizations through the complexities of AI cost estimation and planning. Our comprehensive suite of services includes:
- AI Strategy Development: Crafting tailored AI strategies that align with business objectives and leverage the latest technological advancements.
- Cost-Benefit Analyses: Conducting detailed feasibility studies and cost-benefit analyses to ensure the viability of AI projects.
- Model Development and Testing: Building and rigorously evaluating AI models to meet the highest standards of accuracy, efficiency, and ethical integrity.
- Performance Optimization: Implementing robust measurement frameworks to track the impact of AI initiatives and continuously optimize their performance.
By integrating the best practices and insights from the FinOps Foundation, we help organizations achieve accurate AI cost forecasting and efficient resource allocation. Our expertise ensures that your AI investments are not only strategically aligned but also deliver tangible business value.
For more information on how VE3 can assist with your AI projects, please visit our website or contact us directly. For more tech insights visit us !
