llmspend 0.1.0

Know where your AI money goes. Track LLM API costs per feature, per model, per user.

LLMSpend

Know where your AI money goes.

Track LLM API costs per feature, per model, per user. 2 lines of code. Zero config.

Install

pip install llmspend

Quick Start

import anthropic
from llmspend import monitor

# Wrap your client — that's it
client = monitor.wrap(anthropic.Anthropic(), project="my-app")

# Use it exactly as before
response = client.messages.create(
    model="claude-haiku-4-5-20251001",
    max_tokens=500,
    messages=[{"role": "user", "content": "Hello"}]
)
# Cost, tokens, and latency are now tracked automatically

Tag by Feature

response = client.messages.create(
    model="claude-sonnet-4-6",
    max_tokens=1000,
    messages=[{"role": "user", "content": query}],
    llmspend={"feature": "chatbot", "user_id": "u_123"}
)

View Your Costs

# Last 24 hours, grouped by model
llmspend stats

# Last 7 days, grouped by feature
llmspend stats --last 7d --by feature

# Most expensive calls
llmspend top

# Export as JSON
llmspend export

  LLMSpend — Last 7d
  ──────────────────────────────────────────────────
  Total: $12.4320 across 2,847 calls

  Group                      Calls       Cost    Avg ms
  ───────────────────────── ────── ────────── ────────
  claude-sonnet-4-6            312   $8.9400     1240ms
  claude-haiku-4-5             1893   $2.1200      430ms
  gpt-4o-mini                  642   $1.3720      380ms

Works with OpenAI too

import openai
from llmspend import monitor

client = monitor.wrap(openai.OpenAI(), project="my-app")
# All chat.completions.create calls are now tracked

What Gets Tracked

Per API call:

• Provider, model, timestamp
• Input/output tokens
• Cost in USD
• Latency in ms
• Your custom tags (feature, user_id)

What is never tracked:

• Prompt content
• Response content
• API keys

Self-Hosted Dashboard

Coming soon — React dashboard for visualizing costs locally.

Hosted Version

Coming soon at llmspend.dev — team dashboards, alerts, cost forecasting.

License

MIT