AI agents 2026 loop, retry, and explore dead ends — each step billed per token. This forensics page estimates your invoice from usage patterns, flags the waste, and shows where caps and budgets stop the bleed.
AI agents loop: they call models repeatedly, retry on failure, and explore dead ends — each step billed per token. A single runaway agent can burn through hundreds of dollars overnight while you sleep. Spending forensics estimates your invoice from usage patterns, flags the waste (runaway loops, oversized models, redundant calls), and shows where caps and budgets would have stopped the bleed.
Agent spending forensics is the practice of reconstructing where an AI agent's API budget went — which calls, models, and loops consumed the spend — so you can cap costs before the next invoice.
This free AI API cost calculator breaks down your AI API invoice into what actually burned the tokens — input vs output, your cache-hit estimate, and the top three waste flags. Paste the numbers from your invoice or usage summary.
Estimate: all results are derived from the numbers you type in — we never invent provider pricing. Your invoice figures are the only source of truth; this tool is a guidance aid, not an official billing audit.
Token count drives the bill — and output tokens are typically priced several times higher than input tokens by most providers. (Estimate.) An AI API cost calculator shows whether input or output dominates your spend; agents that "think out loud" usually show a heavy output skew, which is the first waste flag this tool checks.
The usual wins: enable prompt caching, use the cheapest model that passes your quality bar, trim oversized context windows, cap output length, add retry backoff so failures don't burn tokens twice, and batch calls instead of one-at-a-time requests. Fix the waste flags this tool reports first — they are the highest-leverage items.
Prompt caching lets providers reuse the processing of repeated prompt prefixes — like your system prompt — at a discounted input rate instead of charging full price every call. This tool estimates your cache-hit tokens from the cache percentage you enter; if that percentage is under 10%, the missing cache is flagged as your first cost fix. (Estimate.)
If the workload isn't time-sensitive, yes. Providers' batch queues are typically advertised at roughly half the standard rate, with results delivered hours later instead of seconds — good for summarization, enrichment, and evaluation runs; bad for real-time chat. (Estimate — check your provider's current batch pricing.)
No. Every number on this page is an estimate derived from the numbers you type in. We never invent provider pricing — your own invoice figures are the only source of truth, and this tool is a guidance aid, not an official billing audit.
Start with your invoice or usage summary: total input tokens, total output tokens, total cost, and the billing period. An AI API cost calculator then splits spend into input vs output, estimates cache-hit tokens, and flags waste patterns like over-generation. Fix the flagged waste first — it is the highest-leverage saving.
What is the biggest factor affecting LLM API costs?
Token count drives the bill — and output tokens are typically priced several times higher than input tokens by most providers. (Estimate.) An AI API cost calculator shows whether input or output dominates your spend; agents that "think out loud" usually show a heavy output skew, which is the first waste flag this tool checks.
How do I reduce my LLM API bill?
The usual wins: enable prompt caching, use the cheapest model that passes your quality bar, trim oversized context windows, cap output length, add retry backoff so failures don't burn tokens twice, and batch calls instead of one-at-a-time requests. Fix the waste flags this tool reports first — they are the highest-leverage items.
What is prompt caching and how much does it save?
Prompt caching lets providers reuse the processing of repeated prompt prefixes — like your system prompt — at a discounted input rate instead of charging full price every call. This tool estimates your cache-hit tokens from the cache percentage you enter; if that percentage is under 10%, the missing cache is flagged as your first cost fix. (Estimate.)
Should I use the batch API to cut AI API costs?
If the workload isn't time-sensitive, yes. Providers' batch queues are typically advertised at roughly half the standard rate, with results delivered hours later instead of seconds — good for summarization, enrichment, and evaluation runs; bad for real-time chat. (Estimate — check your provider's current batch pricing.)
Is this an official audit of my provider invoice?
No. Every number on this page is an estimate derived from the numbers you type in. We never invent provider pricing — your own invoice figures are the only source of truth, and this tool is a guidance aid, not an official billing audit.