Optimizing Cost for Generative AI with AWS

This post was made better through reviews from Shane Robbins, Ross Richards, and Spencer Hedrick. 

If you or your organizations are in the midst of exploring generative AI technologies, it’s important for you to be aware of the investment that comes with these advanced applications. While you are aiming at the expected return on your generative AI investment, such as, operational efficiency, increased productivity, or improved customer satisfaction, you should also have a good understanding of levers you can use to drive cost savings and enhanced efficiency. To guide you through this exciting journey, we will publish a series of blog posts filled with practical tips to help AI practitioners and FinOps leaders understand how to optimize the costs associated with your generative AI adoption with AWS.

Flexible implementation and pricing options across the AWS Generative AI stack

As you leverage generative AI technologies to enhance your business applications, you’ll find a wide range of implementation options across the AWS Generative AI stack. We see our customers typically following three common implementation approaches.

• For organizations with advanced ML expertise, requiring maximum control and flexibility, you can train and deploy custom models using our infrastructure. HAQM Elastic Compute Cloud (HAQM EC2) provides the highest level of control but requires you to manage your own ML infrastructure and frameworks, while HAQM SageMaker AI offers a fully managed service that handles the infrastructure heavy-lifting while still maintaining flexibility for custom model development. SageMaker JumpStart provides pre-built solutions and models that can be fine-tuned, offering a middle ground between fully custom development and using pre-trained models.
• For organizations who are early in the ML journey, seeking a balance between customization and convenience, you can access leading pre-trained AI models through HAQM Bedrock from providers, such as Anthropic, AI21 Labs, HAQM, Meta, and the Deepseek R1 model.
• For quick implementation with minimal setup, you can deploy ready-to-use applications with HAQM Q, AWS’s generative AI powered assistant that helps make your organizational data more accessible, write code, generate content, and answer questions.

Each implementation approach comes with flexible pricing options designed for cost optimization. If you’re leveraging our infrastructure to train and deploy custom models, you can take advantage of Compute Savings Plans and Machine Learning Savings Plans for steady workloads, or Spot instances for fault-tolerant training tasks. When accessing models via HAQM Bedrock, you can choose between pay-per-token on-demand pricing, reserve capacity through provisioned throughput, or use batch inference for bulk processing. If you’re using HAQM Q to transform the software development and business processes in your organization, you can choose between HAQM Q Business, which offers two subscription models (Business Lite and Business Pro), or HAQM Q Developer, which provides both Free Tier and Pro Tier.

Cloud Financial Management Strategies for generative AI success

If pandemic was a pivotal moment for many of you to reevaluate the cost of your cloud applications, the post-pandemic recovery alongside the surge of interest in generative AI will require you to scrutinize your technology investment. If you haven’t already set clear Cloud Financial Management (CFM) strategies for your organization, now it’s time for you to take a look at your cloud investment and implement essential CFM practices in place.

1. Look before you leap: Estimate your generative AI project cost by converting your business needs into technical configurations. You can estimate the cost for a standalone project or add/modify resource changes associated with your generative AI project to your existing cloud workloads and simulate an entire bill run using AWS Pricing Calculator. The bill estimate feature in the Pricing Calculator will take into consideration of your discount terms for more accuracy. When it comes to the actual generative AI applications, we advise you to begin with proven patterns, such as, Retrieval-Augmented Generation (RAG), text-to-SQL queries. These patterns typically have well-established documentations and cost structure, therefore are easier for you to plan and manage cost.

2. Keep your finger on the pulse: Setting up budgetary limit to individual business entities with alerts that will notify you when cost or usage exceeds the limit with AWS Budgets. You can create an all-up cost budget for the month or a budget to track cost/usage associated with specific services with dimensions, such as, service, tag, Cost Categories. Pay attention to the root cause analysis for each cost anomaly detected with AWS Cost Anomaly Detection. Detailed analysis at the AWS Region, account, service, and usage type can help you quickly identify and address the source of a potential cost overrun.

3. Know the whole picture: When analyzing your generative AI investment, you should include the Total Cost of Ownership, including initial development (e.g., data preparation, model selection and customization), ongoing operation (e.g., computing, storage, energy), and management expense (e.g., education, monitoring). You can use AWS Cost Explorer and Data Exports to access cost and usage incurred by your adoption of AWS services for your generative AI project. It is however equally important to keep track of all the other expenses. Once you have the full scope of your investment, you are advised to allocate these cost to the responsible entities, so users have the right visibility and are accountable for their spend. KPI targets that associate cloud investment with business outcomes (e.g., cost per text summary, cost per image generated), or performance boundary (e.g., response time) are great ways to assess the effect of your investment and motivate for the right behaviors.

4. The more you know, the better you do: Leveraging the agility and scalability of cloud to develop and scale your Generative AI applications, and strategizing your purchase options with all the discounts that are available to you. Your generative AI workloads can benefit from GPU-based instances. AWS Compute Optimizer provides you with rightsizing recommendations by monitoring several metrics, including GPU utilization. In order for Compute Optimizer to collect your GPU metrics, you need to install the HAQM CloudWatch agent with NVIDIA driver, see Collect NVIDIA GPU metrics. HAQM SageMaker AI provides you with a comprehensive platform for developing, training, and deploying generative AI models. If you have consistent needs for HAQM SageMaker and accelerated computing instances, you can consider leveraging Machine Learning Savings Plans, or SageMaker Savings Plans if you primarily use SageMaker AI. Use Savings Plans Purchase Analyzer to estimate the cost impact of your hourly commitment amount for your SageMaker Savings Plans.

If you’re looking to development essential CFM skills, you can take our free digital training courses at your convenience. The AI practitioner training course can help you get more familiar with AI/ML technologies on AWS.

Navigating tradeoffs: balance cost optimization with performance for your generative AI applications

In addition to the CFM strategies listed above, there are many other cost optimization tactics you can consider for your generative AI workloads. These tactics not only provide significant cost saving benefits, but also improve the overall performance for your applications. Prompt caching: store and cache frequently used prompts and their responses to improve response time and reduce redundant API calls. Model distillation: train smaller models to focus on specific use cases for lower inference latency and reduce overall compute and memory utilization. Batch processing: group and process multiple requests in a single batch for better GPU utilization and higher throughout.

However, there are tradeoffs when you implement these cost optimization tactics. How you want to balance resource efficiency with application reliability, response time with the quality and depth of your outputs. As you design and refine your applications and user experience, you can experiment with different techniques and find the best hybrid approach that maximizes the accuracy and minimizes latency, ultimately enhancing your customer experience.

Coming up

To assist you in discovering and implementing cost optimization strategies while adopting our various generative AI service, we will publish the following blog posts that delve into key areas to consider when utilizing specific AI services. We will add links to these blog posts once they become available.

• Optimizing cost for developing custom AI models with HAQM EC2 and SageMaker AI (learn more)
• Optimizing cost for using foundational models with HAQM Bedrock
• Optimizing cost for deploying HAQM Q
• Optimizing cost for Generative AI supporting infrastructure