Cap API Spending for Agents on a Rented Mac

API-backed agents are cheap to start and expensive to leave unsupervised. A single retry loop can burn hundreds of dollars of token requests before anyone notices. The reliable fix is three layers that back each other up: a provider-side spend cap, a client-side per-job ceiling checked after every response, and a launchd watchdog that unloads the agent when daily spend crosses a line.

Pick a Figment plan that matches the agent

Most API-orchestration agents are network-bound, not compute-bound. The Figment virtual Intel macOS VM at $29/month (2 vCPU, 8 GiB RAM, 80 GiB disk) is enough to run a Python or Node agent loop continuously against an LLM API. The VM runs macOS on Intel; it is a real macOS environment that supports Intel-compatible toolchains (including Xcode 26 or earlier), not an SSH jump to another machine. Access is through the browser console, and outbound HTTPS to your LLM provider works normally.

Choose an M-series Mac from $95/month when the agent needs to execute ARM-only binaries, call Vision or Core ML, or hold a local model in RAM. Specific chip, RAM and disk at that entry price are confirmed in the quote — ask before you commit. The Managed Build Mac at $249/month adds one M-series Mac with CI setup, backups, monitoring, and 30 minutes of monthly support, which is useful when the agent runs unattended overnight and you want hardware watched by someone else. Physical plans currently collect requests until supply is confirmed, so start the conversation early via /mac-hosting/start.

Set the provider-side spend cap first

Every major LLM provider exposes a cap you can set before writing any agent code. Set it before you deploy, not after the first incident.

Enforcement behaviour differs: prepaid balances stop at zero, metered plans may lag by a short window as usage events roll up. Read the current enforcement wording on your provider's pricing or limits page and treat the cap as a backstop, not the first line of defence. The client-side counter below is what keeps a single run from eating the entire month.

A per-job token counter

Accumulate estimated cost during the run and raise an exception when the ceiling is crossed. The example below is unexecuted illustrative code; wire your model's current per-million token prices into cost_per_m_in and cost_per_m_out from the provider's pricing page.

# budget_guard.py
PER_JOB_LIMIT_USD = 0.05   # stop this run at five cents

class BudgetGuard:
    def __init__(self, ceiling: float):
        self.ceiling = ceiling
        self.spent = 0.0

    def charge(self, input_tokens: int, output_tokens: int,
               cost_per_m_in: float, cost_per_m_out: float) -> None:
        cost = (input_tokens / 1_000_000) * cost_per_m_in \
             + (output_tokens / 1_000_000) * cost_per_m_out
        self.spent += cost
        if self.spent >= self.ceiling:
            raise RuntimeError(
                f"Per-job budget exceeded: ${self.spent:.5f} >= ${self.ceiling}"
            )

    def log_summary(self) -> str:
        return f"spent={self.spent:.5f}"

Call guard.charge(...) after every API response using the token counts the provider returns in its usage field. When the ceiling is hit the agent exits non-zero rather than retrying. Write guard.log_summary() to the log file so the watchdog can parse it. If your provider returns a cost value directly in the response payload, prefer that over a client-side calculation.

Monthly budget worksheet with stop conditions

Fill in the table once with your model's published per-token prices. Run one sample job first to measure realistic token counts; a ceiling built on guessed averages is the main way these setups fail in production.

Worked example. Suppose a call averages 2 000 input tokens at $3/M and 500 output tokens at $15/M: (2000/1M × 3) + (500/1M × 15) = 0.006 + 0.0075 = $0.0135 per call. Max 3 calls per job gives a per-job ceiling of $0.0405 — round up to $0.05 to leave room for a retry. One run per hour = 24 runs/day × $0.0405 = $0.972 daily, or $29.16 for a 30-day month — about the cost of the $29 VM itself, which is a useful sanity check on the agent's unit economics.

Stop conditions, act on the first one triggered:

1. BudgetGuard raises RuntimeError → exit 1, do not retry inside the same run.
2. Provider returns a billing/rate error (HTTP 429 or a documented spend-limit response) → log it, back off, exit 1, do not loop.
3. Parsed daily spend ≥ 90% of daily ceiling → pause remaining runs for the day.
4. Month-to-date spend ≥ 80% of monthly ceiling → unload the launchd agent and alert.

Schedule the agent and watchdog with launchd

Create two plists in ~/Library/LaunchAgents/. Apple documents the full key reference in Creating Launchd Jobs.

Agent job (com.yourname.agent.plist):

<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN"
  "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
<plist version="1.0"><dict>
  <key>Label</key><string>com.yourname.agent</string>
  <key>ProgramArguments</key>
  <array>
    <string>/usr/bin/python3</string>
    <string>/Users/agent/jobs/run_agent.py</string>
  </array>
  <key>StartInterval</key><integer>3600</integer>
  <key>StandardOutPath</key><string>/Users/agent/logs/agent.log</string>
  <key>StandardErrorPath</key><string>/Users/agent/logs/agent-err.log</string>
</dict></plist>

Daily watchdog (com.yourname.budget-watchdog.plist) parses the log and unloads the agent if daily spend crosses the line. Replace 0.97 with your daily ceiling:

#!/bin/bash
# budget_watchdog.sh
set -euo pipefail
DAILY_CEILING=0.97
LOG=/Users/agent/logs/agent.log
PLIST="$HOME/Library/LaunchAgents/com.yourname.agent.plist"

spent=$(grep "$(date +%Y-%m-%d)" "$LOG" \
  | grep -oE 'spent=[0-9.]+' \
  | cut -d= -f2 \
  | awk '{s+=$1} END{print (s>0)?s:0}')

if awk "BEGIN{exit !($spent >= $DAILY_CEILING)}"; then
  launchctl unload "$PLIST" 2>/dev/null || true
  echo "$(date -u +%FT%TZ) agent paused spent=${spent} ceiling=${DAILY_CEILING}" \
    >> /Users/agent/logs/watchdog.log
fi

Schedule the watchdog with its own plist using StartCalendarInterval once per day, or a short StartInterval for finer granularity. Load both with launchctl load <plist>. For a notification when the watchdog fires, wrap the final echo with a call to the macOS Shortcuts command line tool: shortcuts run "Alert Agent Paused", where the Shortcut sends you a Message or Mail.

Resume and tune after a trip

When the watchdog has paused the agent:

1. Read watchdog.log and agent-err.log to see which stop condition fired.
2. Reconcile the number against the provider's own usage dashboard — client-side estimates can drift from billed cost.
3. Adjust PER_JOB_LIMIT_USD, max calls, or StartInterval in the worksheet, not in ad hoc edits to the script.
4. Reload: launchctl load ~/Library/LaunchAgents/com.yourname.agent.plist.

The $29 Figment VM hosts this setup comfortably; move to an M-series or Managed Build plan when you need ARM execution, more RAM, or someone else watching the box. Start at /mac-hosting or request a configuration at /mac-hosting/start.

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