Cut OpenAI API credit usage in automation workflows

What usually drives OpenAI credit spikes in automations

• AI runs on every event, even when only a small % of events need AI output.
• Multiple prompts per event (e.g., summary + classification + follow-up), where some can be consolidated.
• Legacy branches that still execute (scoring modules, abandoned “agent notes,” old experiments).
• No caching, so you re-summarize the same text across steps or retries.
• No measurement loop, so you can’t tell if changes helped or hurt.

Step 1: Inventory every AI call (make a single source of truth)

• Workflow name + step name (or function name)
• Trigger type (what causes it to run)
• Inputs (prompt + key variables)
• Output usage (where the response is stored / who consumes it)
• Frequency (calls/day or calls/week)
• Cost proxy (tokens, credits, or estimated $/day)

Step 2: Remove dead branches and unused outputs first

• Old scoring modules that were built for an earlier version of the system
• “Agent notes” or extra detail fields that aren’t used downstream
• Duplicate summarizers (summary generated twice in two different steps)
• Classification steps whose result is never used for routing, tagging, or follow-up

Step 3: Gate AI calls with simple thresholds (don’t run AI on every event)

• Minimum text length before summarizing
• Only run follow-up generation when a call is marked “needs follow-up”
• Only run classification when a required field is missing
• Skip when the transcript is empty, low quality, or below confidence

Step 4: Consolidate prompts (reduce 3–4 calls down to 1–2)

• A summary prompt
• A sentiment / flags prompt
• A follow-up email prompt

1. Identify prompts that share the same input text (transcript, notes, etc.).
2. Merge outputs into one response shape (e.g., JSON with fields: summary, key_flags, follow_up_draft).
3. Validate quality with 20–50 real examples before rolling out.

Step 5: Cache results and stop re-processing the same content

• Cache the transcript summary keyed by transcript ID + model + prompt version
• Cache classification results for the same text
• Reuse the same summary for multiple downstream steps (don’t summarize again)

Step 6: Track “credits/day” before and after each change

• Baseline: average credits/day for the last 7–14 days
• After each change: track credits/day for at least 3–7 days
• Keep a changelog of what was modified and when

Step 7: Prioritize the biggest multipliers first

• A workflow that runs on every call/event
• Follow-ups that run multiple prompts per call

1. Remove unused modules (fastest win, lowest risk)
2. Add gating thresholds
3. Consolidate prompts
4. Add caching
5. Revisit model choice only after you’ve reduced call count

Common pitfalls (and how to avoid them)

• Breaking downstream dependencies: Turn off steps behind a flag first; verify nothing depends on the output.
• Consolidation regressions: Test merged prompts on real examples; keep the old approach available as a fallback.
• Over-gating: Don’t gate away AI where it’s genuinely required; start with conservative thresholds.
• No ownership: Assign one person to own the AI call inventory and cost review.

A quick “audit worksheet” you can copy into your ops doc

• Workflow:
• Step:
• Purpose:
• Trigger frequency (per day):
• Runs on every event? (Y/N):
• Output used? (Where?):
• Can gate? (Condition):
• Can merge with another call? (Which?):
• Can cache? (Key):
• Status (keep / modify / remove):

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