ai-cost-audit

name	ai-cost-audit
description	Use before launching any LLM feature or when monthly API costs are growing unexpectedly. Requires token count measurement, call volume analysis, and cost projection at 10× scale. Blocks "it's cheap enough now" completions.

AI Cost Audit

The Law

EVERY LLM FEATURE HAS A COST TRAJECTORY. DISCOVER IT BEFORE 10× SCALE DISCOVERS YOU.
"It's cheap enough now" is a claim about current volume, not future volume.
"The API has reasonable pricing" is not a projection.
Token counts + call volume + cost at 10× scale IS a cost audit.

When to Use

Trigger:

• Before launching any LLM feature (pre-launch projection)
• When monthly API bill increased > 20% with no obvious cause
• Before scaling a feature to a new user segment
• Before committing to a model or provider for a high-volume use case

When NOT to Use

• Internal one-off scripts or developer tools with < 50 calls/day — cost is negligible; write the call, move on
• Features still in prototype where the call structure will change significantly before launch — audit after the design stabilises
• When total monthly API cost is guaranteed < $50 regardless of 10× scale — skip the audit, check the bill quarterly

The Process

Step 1 — Count Tokens Precisely

Do not estimate. Count:

import tiktoken

enc = tiktoken.get_encoding("cl100k_base")  # cl100k for GPT/Claude

def count_tokens(text: str) -> int:
    return len(enc.encode(text))

# Measure each segment separately
print("System prompt:", count_tokens(system_prompt))
print("Avg context:", count_tokens(avg_context_sample))
print("Avg user message:", count_tokens(avg_user_message_sample))
print("Avg output:", count_tokens(avg_output_sample))

Get real samples from logs or representative test data — not the "hello world" example.

Step 2 — Measure Call Volume

Calls per user session: N
Sessions per day: M
Background/batch calls per day: K
Retry rate: R% (from logs or estimate)
Total calls per day: (N × M) + K × (1 + R/100)

Step 3 — Calculate Current Cost

COST_PER_1K_INPUT  = 0.003   # $/1k tokens — replace with actual model pricing
COST_PER_1K_OUTPUT = 0.015

def cost_per_call(input_tokens, output_tokens):
    return (input_tokens / 1000 * COST_PER_1K_INPUT
          + output_tokens / 1000 * COST_PER_1K_OUTPUT)

daily_cost    = cost_per_call(avg_input, avg_output) * calls_per_day
monthly_cost  = daily_cost * 30

Step 4 — Project at 10× Scale

Scale is never gradual — launches cause spikes. Always project at 10×:

Scale	Calls/day	Monthly cost	Verdict
Current (1×)	N	$X	Baseline
10×	10N	$10X	Must be under budget
100×	100N	$100X	Directional awareness

If 10× monthly cost exceeds your budget threshold, optimise before scaling.

Step 5 — Identify Top Cost Drivers

Rank by token contribution:

1. Context injection (RAG chunks): 3,200 tokens avg — 68% of input cost
2. System prompt: 800 tokens — 17% of input cost
3. User message: 200 tokens — 4% of input cost
4. Output: 400 tokens — 11% of total cost

Optimise the top driver first. The others compound on the same multiplier.

Step 6 — Apply Reduction Levers

Lever	Typical Saving	Apply When
Prompt caching	50–90% on cached input	System prompt > 500 tokens and stable
Reduce top_k in RAG	20–50% input token reduction	Context injection is top cost driver
Downgrade model tier	3–10× cost reduction	Quality meets threshold at lower tier (see model-benchmarking)
Semantic caching	30–70% call reduction	High repeat query rate
Pipeline splitting	5–10× cost reduction	Frontier model doing pre-processing tasks
Output cap	10–30% output cost	max_tokens not set or set too high

Step 7 — Document

Store in cost-audit/<feature>/<date>.md:

## AI Cost Audit — <feature> — <date>

Token breakdown (averages from N samples):
  System prompt: X tokens
  Context: Y tokens
  User message: Z tokens
  Output: W tokens
  Total per call: T tokens

Call volume: N calls/day (M sessions × P calls + K background)
Retry rate: R%

Cost at current volume: $X/month
Cost at 10× volume: $10X/month

Top driver: <context injection — Y tokens, Z%>

Reductions applied:
  1. <lever> — estimated saving: X%
  2. <lever> — estimated saving: Y%

Post-optimization projection: $X'/month at 10×

Rationalization Red Flags

These thoughts mean no cost audit was done — stop:

• "It's cheap enough now" — at what volume? with what growth rate?
• "We'll deal with costs when they're a problem" — you'll deal with them at 3am after the billing alert fires
• "The model pricing is reasonable" — "reasonable" has no units; calculate the monthly figure
• "It's not a consumer product so usage will be low" — internal tools get the most surprising usage
• "The context is only a few paragraphs" — a few paragraphs is 400–800 tokens; multiply by call volume

Completion Statement Format

When ai-cost-audit is satisfied, state it like this:

Cost audit complete.
Avg input: N tokens (system: A, context: B, user: C)
Avg output: M tokens
Calls/day: D (retry rate: R%)

Current cost: $X/month
Cost at 10× scale: $Y/month (budget threshold: $Z/month ✓/⚠️)

Top cost driver: <driver> (N% of input cost)
Reductions planned: <levers> — projected saving: X%
Post-reduction projection at 10×: $Y'/month

Token samples from: <N representative calls from logs / test data — not estimated>
Stored: cost-audit/<feature>/<date>.md ✓

Why This Matters

LLM API costs are invisible until they aren't. A feature that costs $200/month at launch costs $20,000/month after a growth event. The audit takes two hours. The surprise does not.