Your forecast is in
Slack bot answering HR benefits questions for an 800-person company. People mostly ask about health insurance, 401k, and PTO. Escalates legal/medical questions to a human.
How PitCrew gets you to $2/mo
Each recommendation below is one change you make at design time, with the dollars it shaves and the running total saved before you ship.
Action plan
The full reasoning behind each recommendation — copy into your build doc.
V3.2 Chat from deepseek runs the same workload at lower cost (budget tier, one tier below). Spec lists it as good for: general purpose. Verify quality on a sample of your traffic before fully switching.
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 62 tokens; caching needs ≥1,024 tokens to amortize the cache-write cost.
- Trim system promptNo redundancy detected — your 62-token prompt is already tight.
- 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 |
|---|---|---|---|---|---|---|
deepseekV4 codingreasoning | $0.30 | $0.50 | — | $2/mo | $-50/mo | Try as default → |
deepseekR1 complex reasoning | $0.55 | $2 | — | $8/mo | $-44/mo | Try as default → |
mistralLarge 2 multilingualreasoning | $2 | $6 | — | $23/mo | $-29/mo | Try as default → |
googleGemini 2.5 Pro long contextmultimodal | $1 | $10 | — | $33/mo | $-19/mo | Try as default → |
openaiGPT-4o multimodal | $3 | $10 | — | $36/mo | $-16/mo | Try as default → |
openaiGPT-5.2 balanced | $2 | $14 | — | $46/mo | $-6/mo | Try as default → |
anthropicSonnet 4.6 Defaultgeneral purposebalanced | $3 | $15 | — | $52/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 | $52/mo | $62–$78/mo |
| PitCrew plan | $2/mo | $2–$3/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