ai-cost-guard

Budget enforcement and cost tracking for LLM applications.

Stop runaway API spend from bugs, prompt injection, or retry loops — before it hits your credit card.

from ai_cost_guard import CostGuard

guard = CostGuard(weekly_budget_usd=5.00)

@guard.protect(model="anthropic/claude-haiku-4-5-20251001")
def call_claude(prompt: str):
    return client.messages.create(...)   # blocked if budget exceeded

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Why this exists

When you build with LLMs, three things will eventually go wrong:

1. A bug creates an infinite retry loop — you wake up to a $300 bill.
2. A prompt injection attack causes your app to make thousands of unexpected calls.
3. A junior dev accidentally calls GPT-4o instead of GPT-4o-mini in a tight loop.

ai-cost-guard is a hard stop. It tracks every LLM call, accumulates cost, and raises BudgetExceededError before the next call goes through.

Zero runtime dependencies. Pure Python stdlib. Works with any LLM provider.

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Install

pip install ai-cost-guard

Or from source:

git clone https://github.com/yourusername/ai-cost-guard
cd ai-cost-guard
pip install -e ".[dev]"

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Quick Start

Decorator (simplest)

from ai_cost_guard import CostGuard
import anthropic

client = anthropic.Anthropic()
guard = CostGuard(weekly_budget_usd=5.00)

@guard.protect(model="anthropic/claude-haiku-4-5-20251001", purpose="summarizer")
def summarize(text: str):
    return client.messages.create(
        model="claude-haiku-4-5-20251001",
        max_tokens=256,
        messages=[{"role": "user", "content": f"Summarize: {text}"}],
    )

Manual check + record

guard.check_budget("openai/gpt-4o", estimated_input=500, estimated_output=200)

response = openai_client.chat.completions.create(...)

guard.record(
    model="openai/gpt-4o",
    input_tokens=response.usage.prompt_tokens,
    output_tokens=response.usage.completion_tokens,
)

Dry-run mode (test without real calls)

guard = CostGuard(weekly_budget_usd=5.00, dry_run=True)
# All calls raise BudgetExceededError("DRY RUN") — safe to use in CI

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CLI

# Show current spend vs budget
ai-cost-guard status

# List all calls this period
ai-cost-guard calls

# List all registered models with pricing
ai-cost-guard models

# Check if a model call would be allowed given a budget
ai-cost-guard check anthropic/claude-sonnet-4-6 5.00

# Reset the tracker
ai-cost-guard reset

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Supported Providers

Provider	Models
Anthropic	claude-haiku-4-5, claude-sonnet-4-6, claude-opus-4-6
OpenAI	gpt-4o, gpt-4o-mini, gpt-3.5-turbo
Google	gemini-1.5-flash, gemini-1.5-pro
Ollama (local)	qwen2.5:7b, llama3.2:3b, mistral:7b (always $0.00)

Adding a new model:

from ai_cost_guard import PROVIDERS

PROVIDERS["myprovider/mymodel"] = {
    "input":  1.00 / 1_000_000,   # per token
    "output": 4.00 / 1_000_000,
}

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Security properties

• Hard budget cap — raises BudgetExceededError before the call, not after.
• No network calls — all data stored locally in ~/.ai-cost-guard/cost_log.json.
• Atomic writes — cost log uses temp-file + rename to prevent corruption.
• Zero dependencies — nothing to supply-chain attack.
• Audit trail — every call logged with timestamp, model, tokens, and purpose.

See SECURITY.md for full security policy.

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How it compares

Tool	Hard budget stop	Multi-provider	Zero deps	Local storage
ai-cost-guard	✅	✅	✅	✅
LangChain callbacks	❌ (observe only)	✅	❌	❌
OpenAI usage limits	✅	❌	N/A	❌
Manual tracking	❌	depends	✅	depends

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Running tests

pip install -e ".[dev]"
pytest tests/ -v

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Contributing

PRs welcome. Please:

• Keep zero runtime dependencies.
• Add tests for new providers.
• Update pricing when providers change rates.

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License

MIT — free to use, modify, and distribute.