llm-spend

Track and limit LLM API spending in real-time. Drop-in wrapper for OpenAI, Anthropic, Google, and any OpenAI-compatible API. Know exactly where your money goes. Set budgets. Get alerts before you go broke.

The Pain

You're burning $50/day on LLM APIs and have no idea which feature, model, or prompt is responsible. You find out when the invoice arrives.

Install

pip install llm-spend

Quick Start

Wrap your OpenAI client

from llm_spend import track
import openai

client = openai.OpenAI()
tracked = track(client, budget=50.0)  # $50 budget

# Use exactly like normal - spending is tracked automatically
response = tracked.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Hello"}]
)

# Check spending anytime
print(tracked.spending.report())
# Total: $0.42 | gpt-4o: $0.38 (12 calls) | gpt-3.5-turbo: $0.04 (45 calls)

Set budgets with alerts

tracked = track(client,
    budget=100.0,           # Hard limit - raises BudgetExceeded at $100
    warn_at=0.8,            # Callback at 80%
    on_warn=lambda s: print(f"⚠️ {s.total_cost:.2f}/{s.budget}"),
    on_budget=lambda s: notify_slack(s),  # Custom handler
    reset="daily",          # Reset budget daily/weekly/monthly
)

Track by label (know WHERE money goes)

response = tracked.chat.completions.create(
    model="gpt-4o",
    messages=messages,
    extra_headers={"X-LLM-Spend-Label": "summarizer"},  # tag this call
)

# Later:
for label, cost in tracked.spending.by_label().items():
    print(f"  {label}: ${cost:.2f}")
# summarizer: $12.50
# classifier: $3.20
# chatbot: $45.00

CLI dashboard

# Show live spending from log file
llm-spend report --file spending.jsonl

# Set environment budget (works with any wrapped client)
export LLM_SPEND_BUDGET=50
export LLM_SPEND_WARN=0.8
export LLM_SPEND_RESET=daily

Cost Database

Built-in pricing for 50+ models (updated regularly):

Custom model costs:

tracked = track(client, custom_costs={
    "my-fine-tuned-model": (0.005, 0.015),  # (input_per_1k, output_per_1k)
})

API Reference

# Core
tracked = track(client, budget=None, warn_at=0.8, reset=None)
tracked.spending.total_cost        # float - total $ spent
tracked.spending.by_model()        # dict[str, float]
tracked.spending.by_label()        # dict[str, float]
tracked.spending.call_count         # int
tracked.spending.report()          # str - formatted report
tracked.spending.reset()           # manually reset counters
tracked.spending.to_jsonl(path)    # export log

# Exceptions
from llm_spend import BudgetExceeded

Features

• Zero config — wrap client, get tracking
• Accurate pricing — 50+ models with per-token costs
• Budget enforcement — hard limits that prevent overspend
• Labels — attribute costs to features/teams/users
• CLI — terminal dashboard and reports
• Async support — works with async OpenAI client
• No dependencies — pure Python, no external packages
• Thread-safe — safe for concurrent use

License

MIT