Implementing GenAI Cost Optimization strategies

 

Implementing GenAI Cost Optimization strategies

As organizations increasingly adopt generative AI capabilities into their operational workflows, managing and optimizing inference costs becomes as crucial as traditional cloud cost management. 

P95 latency per dollar spent is an important metric for evaluating the latency-cost tradeoffs for GenAI applications. P95 latency (the 95th percentile of response times) per dollar spent, provides a direct measure of the value received in terms of performance relative to cost for GenAI applications.

This blog explores practical approaches to balance cost considerations with performance requirements to maintain P95 latency per dollar when deploying GenAI Solutions.

Developing token efficiency systems while maintaining effectiveness.

• Develop token efficiency systems by using token estimation and tracking, context window optimization, response size controls, prompt compression, context pruning, and response limiting to reduce foundation model costs while maintaining effectiveness. 
• Implement the token counting capabilities to accurately estimate and track token usage before making API calls, allowing for better cost prediction and optimization. Using model-specific tokenizers is an effective approach for accurately estimating token counts. Different foundation models use different tokenization algorithms, so using a specific tokenizer for your chosen model ensures that your token estimates closely match how the model will tokenize your input, enabling more precise cost estimation and context window management. 

Create cost-effective model selection frameworks. 

·       It is critical to understand and create cost-effective model selection frameworks by using cost capability tradeoff evaluation, tiered foundation model usage based on query complexity and response quality.  One such example is right sizing the model/s selection and implementing tiered model usage based on query complexity.  By implementing logic so that simple queries route to smaller, less expensive models that can adequately handle straightforward requests, medium complexity queries direct to mid-tier models that balance cost and capability, and complex queries are served by the most powerful but expensive models, costs can be optimized.

Develop high-performing GenAI systems that maximize resource utilization and throughput for workloads.

• Develop high-performance foundation model systems by using batching strategies, capacity planning, utilization monitoring, auto-scaling configurations, and provisioned throughput optimization to maximize resource utilization and throughput for GenAI workloads. 
• For workloads that don't require real-time inference responses, batch processing can reduce foundation model costs while maintaining output quality. For example, pre-generating product descriptions in nightly batch jobs rather than generating them on-demand.

Create intelligent caching systems to reduce costs and improve response times.

• Create intelligent caching systems such as semantic caching, result fingerprinting, edge caching, deterministic request hashing, and prompt caching to improve response times and avoid unnecessary foundation model invocations.

You may also try other techniques such as implementing recursive summarization techniques to compress long documents while preserving key information before submitting inputs to foundation models, using prompt templates  that prioritize the most relevant information at the beginning to ensure critical content is processed even with truncation, using context pruning algorithms to compress prompts while maintaining their effectiveness, and using response size control mechanisms that limit output token generation while maintaining answer quality.

Cost optimization is an ongoing process that should evolve with your GenAI application's needs and usage patterns. By regularly monitoring model performance and inference costs against established metrics, and by keeping a tab on future model releases and pricing changes -  you can optimize costs while maintaining the effectiveness of your GenAI investments.