Your forecast is in
IDE-integrated agent that reviews diffs before code review. Reads the changed files, runs static analysis tools, summarizes risks, and suggests fixes. About 80 PRs per day across the engineering org.
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 |
|---|---|---|---|---|---|---|
elevenlabsConversational voiceconversationalexpressive | $0 | $0 | — | $0/mo | $-67/mo | Try as default → |
openaitext-embedding-3-large embeddingsemantic-searchhigh-quality | $0.13 | $0 | — | $0.49/mo | $-66/mo | Try as default → |
voyagevoyage-3-large embeddinghigh-qualityretrieval | $0.18 | $0 | — | $0.67/mo | $-66/mo | Try as default → |
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 → |
openaiGPT Realtime voicereal-timeinteractive | $5 | $20 | — | $57/mo | $-10/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.
What’s not included
PitCrew forecasts steady-state AI API spend — the dollars the LLM / embedding provider bills for the deterministic workload your wizard described. A production bill carries two kinds of cost on top that PitCrew doesn’t model:
1. Inference overhead — proportional (20–50% on top of steady-state)
- 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) | With inference overhead |
|---|---|---|
| Default build | $67/mo | $80–$100/mo |
| PitCrew plan | $67/mo | $80–$100/mo |
2. Hosting & infra — flat (workload-dependent, typically $10–80/mo)
- Cloud hosting (Vercel / Render / Fly / AWS / etc.)
- Database (Supabase / Postgres / Mongo / etc.)
- Managed vector DB or search — Pinecone, Weaviate, OpenSearch typically $25–100/mo (if not already entered in Step 5)
- CDN, scraping APIs, telephony minutes, transport (Twilio, LiveKit, Zyte, etc.)
- Vendor SaaS margin if going through a wrapper (Cursor, Vapi, Evee, etc.) instead of direct API
The 20-50% inference 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. The hosting/infra range is highly workload-dependent — small RAG bots may spend nothing extra, voice agents add telephony costs on top.
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