GenAI spend often grows invisibly through token usage, redundant generations, and unexamined model choices. Without cost-focused instrumentation and guardrails, teams struggle to scale responsibly.
To win, your GenAI solutions must be cost-aware by design, with clear visibility, forecasting, and controls over spend drivers.
When GenAI cost optimization is reactive, expenses escalate quickly:
- Opaque cost drivers: Token and API usage accumulate without clear attribution to features or workflows.
- Inefficient generation: Redundant prompts and unnecessary generations inflate spend with little added value.
- Unplanned scaling: Usage grows faster than expected, creating budget surprises and forced tradeoffs.
These issues lead to budget overruns, constrained experimentation, and stalled GenAI adoption.
In this hands-on workshop, your team systematically analyzes, reduces, and governs GenAI costs using practical optimization techniques.
- Analyze token and API cost contributors across workflows and use cases.
- Identify and reduce redundant generations and prompts.
- Test lower-cost model alternatives while measuring quality impact.
- Forecast usage patterns to anticipate scaling costs.
- Set guardrails and controls to manage ongoing GenAI spend.
- Analyzing Token and API Cost Contributors
- Reducing Redundant Generations and Prompts
- Testing Low-Cost Model Alternatives
- Forecasting Usage and Scaling Costs
- Setting Guardrails on GenAI Spend
- Gain clear visibility into where GenAI costs are coming from.
- Reduce unnecessary spend without degrading output quality.
- Confidently evaluate lower-cost model options.
- Anticipate how usage growth will affect future budgets.
- Establish guardrails that keep GenAI spend predictable and controlled.
Who Should Attend:
Solution Essentials
Facilitated workshop (in-person or virtual)
4 hours
Intermediate
Cost analysis artifacts, usage data, model comparisons, and forecasting exercises