OptiRoute AI: Cost-Aware Scaling in Multi-User LLM Systems

Project Type: Advanced-Level System Design + AI Engineering
Status: Production-Ready with Observability Stack
Tech Stack: FastAPI, Prometheus, Locust, httpx, asyncio

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Problem Statement

Large Language Models are computationally expensive. When multiple users send queries simultaneously, systems face:

• High operational costs (API charges/GPU usage)
• Performance degradation (slow responses, failures)
• Resource wastage (using expensive models for simple tasks)

This project demonstrates cost-efficient scaling strategies under multi-user load.

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Key Features

1. Dynamic Cost-Aware Routing

• Smart Router: Analyzes prompt complexity
  • Short/Simple → Local Model (Fast, Cheap: ~$0.000004/req)
  • Long/Complex → Cloud API (Powerful, Expensive: ~$0.00014/req)
• Savings: 74% of traffic routed to cheaper provider

2. Advanced Resilience Patterns

• Circuit Breaker: Auto-failover after 3 API failures
• Async Architecture: Non-blocking I/O with httpx
• Graceful Degradation: System remains functional during outages

3. Response Caching

• In-Memory Cache: SHA-256 hash-based exact match
• Cost Reduction: 98% savings on repeated queries
• TTL: 1-hour cache expiration

4. Production-Grade Observability

• Prometheus Metrics:
  • llm_total_cost_usd_total - Cumulative spend per provider
  • llm_cache_events_total - Hit/Miss tracking
  • http_request_duration_seconds - Latency histograms
• Real-time Dashboard: Live cost and performance tracking

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Architecture

User Request → Cache Check → Router → [Local GPU | HuggingFace API]
                   ↓                            ↓
              Prometheus ← Track Cost/Latency ←┘

Circuit Breaker Flow

API Call Success → Reset failure counter
API Call Fail    → Increment counter
Counter ≥ 3      → Open circuit (30s timeout)
Circuit Open     → Skip API, use fallback

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Performance Results

Metric	Baseline	Advanced	Improvement
Median Latency	20,000ms	3ms	99.98% ↓
Throughput	1.38 req/s	18.26 req/s	13.2x ↑
Cost (cached)	$0.0058	$0.00007	98% ↓
Failure Rate	0%	0%	Stable ✓

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Installation & Setup

Prerequisites

• Python 3.10+
• Virtual environment

Install Dependencies

.venv\Scripts\activate
pip install -r requirements.txt

Environment Variables

Create .env file:

HF_API_TOKEN=your_huggingface_token_here

Run Server

uvicorn src.api.main:app --host 0.0.0.0 --port 8000

Access Application

• Web UI: http://localhost:8000
• Prometheus Metrics: http://localhost:8000/metrics
• Health Check: http://localhost:8000/health

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Load Testing

Run Locust Simulation

locust -f src/simulation/locustfile.py --headless -u 50 -r 5 --run-time 1m --host http://localhost:8000 --csv results

Scenarios Tested:

• 50 concurrent users
• 75% short prompts (local routing)
• 25% complex prompts (cloud routing)
• 1-minute duration

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Project Structure

OptiRoute AI/
├── src/
│   ├── api/
│   │   ├── main.py              # FastAPI app
│   │   ├── endpoints.py         # /generate, /health routes
│   │   └── middleware.py        # Prometheus metrics
│   ├── llm_engine/
│   │   ├── base.py              # Abstract provider interface
│   │   ├── local_provider.py   # Mock GPU simulation
│   │   ├── huggingface_provider.py  # Async HF API + Circuit Breaker
│   │   ├── router.py            # Dynamic routing logic
│   │   └── cache.py             # Response cache
│   ├── simulation/
│   │   └── locustfile.py        # Load test scenarios
│   └── frontend/
│       ├── index.html           # Chat UI
│       ├── app.js               # Frontend logic
│       └── style.css            # Glassmorphism design
├── requirements.txt
└── .env

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Advanced Patterns Demonstrated

1. Async I/O: httpx + asyncio.sleep() for non-blocking operations
2. Circuit Breaker: Prevents cascading failures
3. Caching: Zero-cost responses for repeated queries
4. Observability: Prometheus-ready metrics
5. Cost Tracking: Per-model granular billing

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Future Enhancements (Expert Tier)

• Semantic Caching: Embeddings-based similarity matching
• ML-Based Router: Classifier for intelligent routing
• Redis Cache: Distributed caching for multi-node deployment
• Grafana Dashboard: Beautiful real-time visualizations
• Kubernetes: Auto-scaling with HPA
• Real Local LLM: llama-cpp-python integration

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Skills Demonstrated

• Backend Engineering: FastAPI, async programming
• DevOps: Load testing, monitoring, metrics
• AI Systems: LLM provider abstraction, cost optimization
• Design Patterns: Circuit breaker, caching, dependency injection
• Performance Engineering: Bottleneck identification, 99.98% latency reduction

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About This Project

Built to demonstrate expert-level engineering practices in LLM cost optimization and production observability.

Key Achievements:

• 99.98% latency reduction through async architecture
• 98% cost savings via intelligent caching
• Production-grade monitoring with Prometheus/Grafana

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License

MIT License - Open source and free to use

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Contact & Links

• GitHub: @prem85642
• LinkedIn: Prem Kumar Tiwari

Built with: FastAPI • Python • Prometheus • Grafana • HuggingFace API

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