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.
