hoangsonww Cost Breakdown
Detailed cost analysis from the Agent Monitor's pricing engine.
Input
The user provides: $ARGUMENTS
This may be: "today", "this week", "last 30 days", a session ID, or "budget $50/week".
Data Sources
| Endpoint | Returns |
|---|---|
GET /api/pricing |
{ pricing: [{ model_pattern, display_name, input_per_mtok, output_per_mtok, cache_read_per_mtok, cache_write_per_mtok }] } |
GET /api/pricing/cost |
Total cost: { total_cost, breakdown: [{ model, input_tokens, output_tokens, cache_read_tokens, cache_write_tokens, cost, matched_rule }] } |
GET /api/pricing/cost/{sessionId} |
Per-session cost with same breakdown shape |
GET /api/sessions?limit=200 |
Sessions list — each includes inline cost field (bulk pricing) |
GET /api/analytics |
Token totals (total_input, total_output, total_cache_read, total_cache_write — baselines pre-summed), daily trends |
How costs are calculated
The pricing engine matches model names against model_pattern using SQL LIKE (e.g. claude-sonnet-4-5% matches claude-sonnet-4-5-20250514). Longest pattern wins for specificity. Cost per model:
cost = (input_tokens / 1M) × input_per_mtok
+ (output_tokens / 1M) × output_per_mtok
+ (cache_read_tokens / 1M) × cache_read_per_mtok
+ (cache_write_tokens / 1M) × cache_write_per_mtok
Token counts are effective totals = current + baseline (baselines preserve pre-compaction tokens that would otherwise be lost when the transcript JSONL is rewritten).
Default pricing tiers (seeded on first run)
| Family | Input $/Mtok | Output $/Mtok | Cache Read $/Mtok | Cache Write $/Mtok |
|---|---|---|---|---|
| Opus 4.5/4.6 | $5 | $25 | $0.50 | $6.25 |
| Sonnet 4/4.5/4.6 | $3 | $15 | $0.30 | $3.75 |
| Haiku 4.5 | $1 | $5 | $0.10 | $1.25 |
Report Sections
1. Cost by Model
Table from /api/pricing/cost breakdown — each model with 4 token counts + cost. Highlight which pricing rule matched.
2. Cost by Session (Top 10 Most Expensive)
From sessions list with inline cost — sort descending. Show session name, model, duration, cost.
3. Daily Cost Trend
Cross-reference daily_sessions with per-session costs to compute daily spend. Show 7/30-day trend with direction arrows.
4. Token Efficiency Analysis
- Cache hit rate:
total_cache_read / (total_cache_read + total_input) × 100— higher = more efficient - Compaction baseline recovery: Tokens preserved via baseline columns (tokens not lost to compaction)
- Output/input ratio: Balanced ratio indicates good prompt efficiency
5. Cost Optimization Opportunities
- Sessions where cache_write >> cache_read (poor cache reuse)
- Expensive models used for simple tasks (check subagent_type vs model)
- Sessions with many compactions (context overflow = wasted tokens)
Output
Structured Markdown with tables. Currency as USD to 4 decimal places. Include total and per-model subtotals.