Your forecast is in
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Action plan
No optimizations apply to this build — it's already efficient for the workload you described. Below, the levers PitCrew checked and why each one wasn't a fit.
Considered, didn’t apply
PitCrew checks every lever — model fit, prompt caching, batch lanes, prompt trimming. Here’s why the rest didn’t make the cut on this build.
- Prompt cachingYour system prompt is 55 tokens; caching needs ≥1,024 tokens to amortize the cache-write cost.
- Trim system promptNo redundancy detected — your 55-token prompt is already tight.
- Switch to a cheaper modelNo candidate beats Opus 4.7 by more than $1.00/mo on your inputs — your model choice is already efficient.
- Batch APIThis is a real-time agent (0% async traffic). No work to route to a batch lane.
Alternative models
Same quality tier, your wizard inputs. No caching or batch applied — every row is a directly-comparable raw monthly cost. Click Try as default to re-render this report with that model as the new baseline.
| Model | Input $/Mtok | Output $/Mtok | Context | Monthly cost | vs default | Open in audit |
|---|---|---|---|---|---|---|
openaiGPT-5.4 general purpose | $3 | $10 | — | $29/mo | $-38/mo | Try as default → |
xaiGrok 4.1 real-time dataX integration | $3 | $15 | — | $40/mo | $-27/mo | Try as default → |
anthropicOpus 4.7 Defaultcomplex reasoningcodeagentic | $5 | $25 | — | $67/mo | — | Try as default → |
anthropicOpus 4.6 complex reasoningcodeagentic | $5 | $25 | — | $67/mo | +$0/mo | Try as default → |
openaio3 deep reasoning | $10 | $40 | — | $114/mo | +$47/mo | Try as default → |
What we assumed
These are the inputs we used. If anything looks off, re-run the audit with better numbers.
- Call volume is your guess — typical pre-deploy estimates land within ±50% of actual.
- Conversation length is a coarse bucket — actual tokens vary by ±40% per call.
Real-bill expectation
PitCrew forecasts steady-state inference cost — the dollars the LLM provider bills for the deterministic, no-extras workload your wizard described. Real production bills are typically 1.2-1.5× higher because the steady-state model excludes:
- Dev / eval loops (often 10-30% of total spend)
- Retries, error recovery, idempotency replays
- Background batch jobs (summaries, classification of past data)
- A/B traffic on alternate models
- Embeddings + fine-tunes that ride alongside the agent
| Scenario | Steady-state (PitCrew) | Expected real bill |
|---|---|---|
| Default build | $67/mo | $80–$100/mo |
| PitCrew plan | $67/mo | $80–$100/mo |
The 20-50% multiplier comes from public engineering postmortems and the validation cases in docs/accuracy-validation.md. If your team has tight eval loops and minimal retry traffic, target the low end.
How sensitive is this forecast?
Pre-deploy estimates are guesses. Here’s how the savings shift if the volume or conversation length you guessed turns out to be off.
Run another audit
for a different build
Tweak inputs, swap the model, see how the forecast moves.
New audit