Workflow Automation Cost Optimization Review (Retention)

Workflow Automation Cost Optimization Review (Retention)

1. 01

   Inventory all active client workflows and their monthly execution volumes

   Pull usage data from the automation platform for each workflow, noting trigger frequency, task counts, and any AI or LLM steps that incur per-call costs.

2. 02

   Identify repeated prompt structures and enable prompt caching

   For workflows using LLM steps, check if the same system prompt or context block appears in more than 20% of calls. Enabling caching on those inputs can cut inference costs by up to 90% on repeated inputs.

3. 03

   Route routine tasks to lower-cost models or open-weight alternatives

   For non-critical steps like data extraction or classification, consider switching to cheaper model tiers or open-weight models. OpenAI's GPT-5.6 Luna, for instance, is priced 80% below the flagship Sol, and routing layers like Fireworks Nexus can automate this shift.

4. 04

   Review error and retry patterns for silent failures

   Check logs for failed runs, timeouts, and rate-limit errors. Implement retries with exponential backoff and circuit breaking where missing, as these can prevent costly manual rework and data gaps.

5. 05

   Consolidate overlapping workflows and remove redundant steps

   Look for automations that duplicate each other or have steps that no longer serve a purpose. Streamlining reduces execution time and platform usage, which can lower subscription costs.

6. 06

   Estimate monthly savings and present a cost optimization report to the client

   Quantify the reduction in inference costs, task executions, and manual hours saved. Present the findings as part of a quarterly business review to demonstrate ongoing value and justify the retainer.