Automation Cost Optimization Review (Retention)

Automation Cost Optimization Review (Retention)

1. 01

   Inventory all active workflows and their monthly execution volumes

   Pull usage logs from the automation platform to identify which workflows run most frequently and which are idle. This establishes the baseline for cost and value analysis.

2. 02

   Flag workflows with repeated prompt structures or static context blocks

   Look for system prompts or context blocks that appear in more than 20% of calls. These are prime candidates for prompt caching, which can cut inference costs by up to 90% on repeated inputs.

3. 03

   Assess whether any tasks can be deferred and batched

   Identify workflows where outputs are not needed in real time, such as nightly report generation. Batching these tasks can reduce costs by up to 50% on deferrable operations.

4. 04

   Review model selection for each workflow against current pricing tiers

   Compare the cost of the current model with newer, cheaper tiers. For example, OpenAI's GPT-5.6 family includes Luna, priced 80% below the flagship Sol, which may be sufficient for routine tasks.

5. 05

   Evaluate routing layers or open-weight models for routine tasks

   Consider tools like Fireworks Nexus that automatically route routine coding tasks to open-weight models, reducing per-request costs without sacrificing quality for simple operations.

6. 06

   Check for resilience features like retries and caching in LLM calls

   Ensure workflows have fault-tolerance defaults such as retries with exponential backoff and caching to avoid silent failures and redundant API calls, which can inflate costs.

7. 07

   Document cost savings and present a revised monthly estimate to the client

   Quantify the projected savings from caching, batching, and model changes. Present this as a revised cost model, reinforcing the agency's value in ongoing optimization.