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.