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LLM Cost Optimizer

$29

Token usage tracking, model routing for cost/quality tradeoffs, caching strategies, batch processing, and budget alerting.

📁 29 files
MarkdownYAMLPythonLLMOpenAI

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📁 File Structure 29 files

llm-cost-optimizer/ ├── LICENSE ├── README.md ├── configs/ │ └── pricing.yaml ├── free-sample.zip ├── guide/ │ ├── 01-getting-started.md │ ├── 02-core-features.md │ └── 03-advanced-features.md ├── index.html ├── requirements.txt ├── src/ │ ├── __init__.py │ ├── __pycache__/ │ │ ├── __init__.cpython-312.pyc │ │ ├── budget_alert.cpython-312.pyc │ │ ├── cache.cpython-312.pyc │ │ ├── model_router.cpython-312.pyc │ │ ├── pricing.cpython-312.pyc │ │ ├── report_generator.cpython-312.pyc │ │ ├── token_tracker.cpython-312.pyc │ │ └── tokenizer_estimate.cpython-312.pyc │ ├── budget_alert.py │ ├── cache.py │ ├── model_router.py │ ├── pricing.py │ ├── report_generator.py │ ├── token_tracker.py │ └── tokenizer_estimate.py └── tests/ ├── __pycache__/ │ ├── conftest.cpython-312.pyc │ └── test_optimizer.cpython-312.pyc ├── conftest.py └── test_optimizer.py

📖 Documentation Preview README excerpt

LLM Cost Optimizer

Track, analyse, and reduce LLM API spending with token accounting, intelligent model routing, response caching, budget alerts, and cost reporting.

Most teams discover they're spending 3-5x more than necessary on LLM APIs. This toolkit gives you per-request cost tracking, a model router that picks the cheapest model for each quality tier, exact-match response caching (typically 20-40% savings), budget alerting, and a report generator that produces HTML dashboards and Markdown summaries — all with a stdlib-only token estimator so you can project costs before making any API calls.

What You Get

  • Token usage tracker with per-request logging, model breakdowns, and JSONL persistence
  • Pricing table for OpenAI and Anthropic models with YAML-driven updates (no code changes needed)
  • Model router that classifies prompts by complexity and selects the cheapest qualifying model
  • Response cache with SHA-256 exact-match, TTL expiration, LRU eviction, and JSON persistence
  • Budget monitor with daily/weekly/monthly thresholds, percentage-based warnings, and custom alert callbacks
  • Token estimator (stdlib-only) with content-type detection for English prose, code, technical, and multilingual text
  • Report generator producing both Markdown tables and a styled HTML cost dashboard
  • Comprehensive test suite covering pricing, tracking, routing, caching, budgeting, and estimation

File Tree


llm-cost-optimizer/
├── README.md
├── LICENSE
├── requirements.txt
├── src/
│   ├── __init__.py                  # Package init
│   ├── token_tracker.py             # UsageEvent dataclass + TokenTracker
│   ├── pricing.py                   # ModelPricing + PricingTable (YAML/defaults)
│   ├── model_router.py              # Heuristic tier classifier + cost-aware routing
│   ├── cache.py                     # ResponseCache with TTL + LRU + persistence
│   ├── budget_alert.py              # BudgetMonitor with AlertLevel enum
│   ├── report_generator.py          # Markdown + HTML dashboard reports
│   └── tokenizer_estimate.py        # Stdlib-only token estimation
├── configs/
│   └── pricing.yaml                 # Per-model pricing (USD per 1K tokens)
└── tests/
    ├── conftest.py                  # Path setup
    └── test_optimizer.py            # Full test suite

Quick Start

1. Install Dependencies


pip install -r requirements.txt

Only PyYAML is required (for pricing config). The core modules work without it using built-in defaults.

2. Track API Calls


from src.pricing import PricingTable
from src.token_tracker import TokenTracker

pricing = PricingTable("configs/pricing.yaml")
tracker = TokenTracker(log_path="usage.jsonl", pricing=pricing)

*... continues with setup instructions, usage examples, and more.*

📄 Code Sample .py preview

src/budget_alert.py """ Budget Alerting — LLM Cost Optimizer ======================================= Monitor spending against configurable budget thresholds and trigger alerts when limits are approached or exceeded. By Datanest Digital — support@datanest.dev """ from __future__ import annotations import logging from dataclasses import dataclass, field from datetime import datetime, timezone from enum import Enum from typing import Any, Callable, Dict, List, Optional logger = logging.getLogger(__name__) class AlertLevel(Enum): INFO = "info" WARNING = "warning" CRITICAL = "critical" @dataclass class BudgetAlert: """A triggered budget alert. Attributes: level: Severity of the alert. message: Human-readable alert message. current_spend: Current spending amount in USD. budget_limit: The budget limit that was breached. timestamp: When the alert was triggered. """ level: AlertLevel = AlertLevel.INFO message: str = "" current_spend: float = 0.0 budget_limit: float = 0.0 timestamp: str = field( default_factory=lambda: datetime.now(timezone.utc).isoformat() ) class BudgetMonitor: """Monitor LLM spending against budget thresholds. Set daily, weekly, and monthly budgets. The monitor checks spending after each recorded event and triggers callbacks when # ... 85 more lines ...
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