Token Billing That Finance Teams Can Trust

The finance team's problem with LLM billing

Finance teams need predictable, auditable costs. LLM billing is the opposite: it varies by request, depends on token counts most engineers do not track, and arrives as a monthly USD total that is impossible to attribute to specific features or teams.

The result is that AI spend gets treated as an undifferentiated "cloud cost" — visible only at month end, impossible to forecast, and impossible to chargeback to the right budget owner.

From tokens to invoicing

The reliable pattern is request-level accounting. Store prompt tokens, completion tokens, model ID, provider, forex rate, and final INR cost for each request.

This gives your finance and ops teams a deterministic audit trail without reverse engineering totals from partial logs.

What to log per request

At minimum, capture these fields on every LLM API call:

| Field | Why it matters | |-------|---------------| | request_id | Correlates logs across systems | | model | Cost per token varies by model | | prompt_tokens | Input cost component | | completion_tokens | Output cost component | | cost_usd | Provider raw cost | | forex_rate | Explains INR conversion | | cost_inr | Final chargeable amount | | user_id / api_key_id | Chargeback attribution | | timestamp | Date-range filtering |

AICredits logs all of these for every request automatically. Export to CSV from the Usage tab for month-end reconciliation.

Avoiding common mistakes

Do not aggregate costs too early. Keep source events immutable and compute summaries downstream.

Also separate retry attempt cost from successful response cost to avoid underreporting spend. A failed request that was retried three times consumed real tokens — those costs should appear in your ledger even if the final response was unsuccessful.

Making it explainable

The typical finance question is: "Why did AI spend go up 30% this month?" To answer this confidently, you need to be able to show:

1. Which features or API keys drove the increase
2. Which models were used (did we accidentally send more traffic to expensive models?)
3. Whether it was volume growth or cost-per-request growth

Request-level logs with model and key attribution make this answerable in minutes rather than hours.