How many tokens are in a typical API call?

A simple question might be 50-100 tokens. A system prompt can add 200-1000 tokens. A conversation with history can easily exceed 2,000 tokens. Every token costs money on every call.

What Are AI Token Costs and How Do They Work?

AI language models charge per token. A token is roughly 4 characters or 0.75 words. Understanding token costs is essential for budgeting AI applications.

How Token Pricing Works

Most LLM providers charge separately for input (prompt) tokens and output (completion) tokens. Input tokens are typically cheaper. For example, GPT-4 charges $30/M input and $60/M output tokens.

Hidden Cost Sources

Frequently Asked Questions

Why this matters

Autonomous AI agents are useful precisely because they can act without a human in the loop on every decision. The moment an agent can initiate a transaction — a paid API call, a purchase, a charge, a provisioning step — it can also overspend. The question is not whether to give agents spending power, but how to bound that power so a bug, a loop, or an adversarial input cannot drain a budget.

sipi.bot is a spend firewall for autonomous AI agents. It sits between your agent code and your payment methods, evaluating every transaction against your rules in under 5 milliseconds and returning one of three structured decisions: approve, block, or flag. Per-transaction limits, daily ceilings, velocity caps, merchant allowlists, and human-in-the-loop escalation are all enforced before a dollar moves. Pricing starts at $99 per month.

How to apply this

Identify every place your agent can cause money to move. Include indirect paths (tool calls that wrap paid APIs, functions that provision metered resources).
Put a policy check in front of each one. The check should be the only path between spend intent and the actual transaction.
Define limits that match the agent's job: per-transaction cap, daily ceiling, velocity cap, merchant allowlist.
Log every decision with agent id, merchant, amount, intent, and policy version. The log is your audit trail and your debugging tool.
Set up real-time alerts on flagged and blocked transactions. A blocked transaction is information — use it to improve the agent or the policy.
Review the audit log weekly during early deployment, then monthly once the patterns stabilize.

Common mistakes

Setting limits too high because you are worried about interrupting the agent. Start low and raise based on what you observe. A blocked transaction is a teaching signal; an unblocked overspend is a bill.

Skipping the audit log. Without a tamper-evident record, you cannot debug blocked transactions, cannot prove compliance, and cannot answer the question 'what did the agent spend last week?'

Trusting the LLM provider's monthly cap. Provider caps are account-level and monthly. They will not stop a loop that runs for six hours on a Saturday and they will not cap a non-LLM transaction.

Measuring success

Track three numbers: blocked-transaction rate (should be low — under 1% — and stable), flagged-transaction rate (should be near zero in steady state), and total spend per agent per day (should match your policy ceiling, never exceed it). If blocked rate spikes, your policy is too tight or your agent is misbehaving; investigate before loosening.