Stop paying premium rates for every AI request. NeuralOps automatically routes each prompt to the cheapest model capable of answering it — saving up to 85% on LLM costs without sacrificing quality.
🏆 2nd Runner Up — ₹5000 Prize — Built in 36 hours at hackathon
Use NeuralOps · Features · Demo · Quick Start · Pricing · Architecture · API Reference · Roadmap
Change one line. Save 80%+ automatically.
Python:
from openai import OpenAI
# Before (expensive):
# client = OpenAI(api_key="sk-...")
# After (80% cheaper):
client = OpenAI(
api_key="not-required",
base_url="https://nueralops-production.up.railway.app/v1"
)
response = client.chat.completions.create(
model="auto", # NeuralOps decides the best model
messages=[{"role": "user", "content": "Your prompt here"}]
)
print(response.choices[0].message.content)
print(f"Saved: {response._neuralops['savings_percentage']}%")JavaScript:
import OpenAI from 'openai';
const client = new OpenAI({
apiKey: 'not-required',
baseURL: 'https://nueralops-production.up.railway.app/v1'
});
const response = await client.chat.completions.create({
model: 'auto',
messages: [{ role: 'user', content: 'Your prompt here' }]
});
console.log(response.choices[0].message.content);cURL:
curl https://nueralops-production.up.railway.app/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "auto",
"messages": [{"role": "user", "content": "What is machine learning?"}]
}'LangChain:
from langchain_openai import ChatOpenAI
llm = ChatOpenAI(
openai_api_base="https://nueralops-production.up.railway.app/v1",
openai_api_key="not-required",
model_name="auto"
)
result = llm.invoke("Explain neural networks")
print(result.content)Every company using AI APIs today pays a flat premium rate for every single request — regardless of whether that request needs frontier intelligence or could be answered by a model 15x cheaper.
A user asks: "What is the capital of France?"
| Without NeuralOps | With NeuralOps |
|---|---|
| Routes to premium model → $0.000018 | Routes to Llama 3.1 8B → $0.0000012 |
| 93% of cost wasted | 93% saved automatically |
This waste compounds at scale:
| Monthly AI Spend | Wasted (est. 70%) | Annual Waste |
|---|---|---|
| $5,000 | $3,500 | $42,000 |
| $50,000 | $35,000 | $420,000 |
| $500,000 | $350,000 | $4,200,000 |
NeuralOps fixes this — automatically, with zero changes to your existing code.
Every prompt is classified as SIMPLE, MEDIUM, or COMPLEX using a fast LLM classifier, then routed to the optimal cost-tier model. No configuration needed.
Send the same prompt to all 3 models simultaneously. See responses, latency, cost, and composite scores side-by-side. NeuralOps picks the winner using complexity-aware scoring.
Live WebSocket-powered stats — total requests, cumulative savings, routing distribution, and per-request activity feed. Zero page refreshes.
Interactive node-graph showing how requests flow from classifier → router → model tier, with live health indicators per node.
Deep analytics on request history: complexity distribution, per-model savings breakdown, NeuralOps Intelligence Report with automatic recommendations, and activity timeline.
Input your monthly AI spend — get projected savings, annual ROI, payback period, and NeuralOps fee. Pure math, no API calls.
Simulate model outages by toggling health states. The router automatically falls back through a priority chain — requests always get routed to a healthy model with zero downtime.
If the AI classifier fails: → Rule-based keyword fallback → Hardcoded COMPLEX safety fallback. Quality is never compromised.
# Simple question → Economy model (Llama 3.1 8B)
curl -X POST http://localhost:8000/route \
-H "Content-Type: application/json" \
-d '{"text": "What is 2+2?"}'
# Response includes:
# "tier": "economy"
# "savings_percentage": 93.3
# "latency_ms": 147
# Complex question → Premium model (Qwen 3 32B)
curl -X POST http://localhost:8000/route \
-H "Content-Type: application/json" \
-d '{"text": "Design a distributed payment system for 10 million users"}'
# Response includes:
# "tier": "premium"
# "complexity": "COMPLEX"
# "latency_ms": 4821curl -X POST http://localhost:8000/battle \
-H "Content-Type: application/json" \
-d '{"text": "Explain how transformers work in ML"}'
# All 3 models respond in parallel
# NeuralOps picks the winner
# You see cost, latency, quality score for each- Python 3.11+
- Node.js 18+
- Groq API Key (free at console.groq.com)
git clone https://github.com/YOUR_GITHUB_USERNAME/NeuralOps.git
cd NeuralOpscd backend
# Create virtual environment
python -m venv .venv
source .venv/bin/activate # macOS/Linux
# .venv\Scripts\activate # Windows
# Install dependencies
pip install fastapi uvicorn aiosqlite python-dotenv groq httpx
# Configure environment
cp .env.example .env
# Add your GROQ_API_KEY to .env
# Start server
uvicorn main:app --reload --port 8000# New terminal
cd frontend
npm install
npm run devOpen http://localhost:3000 🎉
# Populates dashboard with ~300 realistic requests
cd backend
python seed_data.pyCreate backend/.env:
GROQ_API_KEY=gsk_... # Required
CLASSIFIER_MODEL=llama-3.1-8b-instant # Classifier model
CHEAP_MODEL=llama-3.1-8b-instant # Economy tier
MID_MODEL=llama-3.3-70b-versatile # Standard tier
PREMIUM_MODEL=qwen/qwen3-32b # Premium tier
DATABASE_URL=neuralops.db # SQLite pathWe earn only when you save.
NeuralOps charges 3% of your monthly savings — nothing more. Zero risk for you.
| Monthly AI Spend | Est. Savings | NeuralOps Fee | Your Net Gain |
|---|---|---|---|
| $1,000 | ~$700 | $21 | $679 |
| $10,000 | ~$7,000 | $210 | $6,790 |
| $50,000 | ~$35,000 | $1,050 | $33,950 |
| $500,000 | ~$350,000 | $10,500 | $339,500 |
Minimum fee: $99/month. If NeuralOps doesn't save you money — you don't pay. Simple.
┌──────────────────────────────────────────────────────────────┐
│ FRONTEND (React 19 + Vite) │
│ │
│ ┌───────────┐ ┌────────────┐ ┌──────────┐ ┌────────────┐ │
│ │ Dashboard │ │Battle Mode │ │ Traffic │ │ Insights │ │
│ │(Recharts) │ │ │ │ Map │ │ │ │
│ └───────────┘ └────────────┘ └──────────┘ └────────────┘ │
│ ┌──────────────────┐ ┌─────────────────────────────────┐ │
│ │ ROI Calculator │ │ Self-Healing Panel │ │
│ └──────────────────┘ └─────────────────────────────────┘ │
└─────────────────────────┬────────────────────────────────────┘
│ REST + WebSocket
┌─────────────────────────▼────────────────────────────────────┐
│ BACKEND (FastAPI) │
│ │
│ ┌────────────┐ ┌──────────┐ ┌────────────────────────┐ │
│ │ Classifier │→ │ Router │→ │ Cost Tracker │ │
│ │(Llama 8B) │ │ Engine │ │ │ │
│ └────────────┘ └──────────┘ └────────────────────────┘ │
│ ┌────────────┐ ┌──────────┐ ┌────────────────────────┐ │
│ │Rule-Based │ │Self-Heal │ │ SQLite (aiosqlite) │ │
│ │ Fallback │ │ Manager │ │ │ │
│ └────────────┘ └──────────┘ └────────────────────────┘ │
└───────┬──────────────┬──────────────────┬────────────────────┘
│ │ │
┌─────▼──────┐ ┌─────▼──────┐ ┌────────▼───────┐
│ Llama 3.1 │ │ Llama 3.3 │ │ Qwen 3 32B │
│ 8B │ │ 70B │ │ │
│ Economy │ │ Standard │ │ Premium │
│ $0.06/1M │ │ $0.59/1M │ │ $0.90/1M │
└────────────┘ └────────────┘ └───────────────┘
└──────────────┴──────────────────┘
Groq API
1. POST /route ← User prompt arrives
2. Classifier → SIMPLE / MEDIUM / COMPLEX + confidence score
3. Router → Selects cheapest healthy model
4. Groq API → Model generates response
5. Cost Tracker → Calculates actual cost vs premium baseline
6. SQLite → Request saved to database
7. WebSocket → Dashboard updated in real time
8. Response → Returned with full metadata
| Complexity | Primary | Fallback 1 | Fallback 2 |
|---|---|---|---|
| SIMPLE | Economy | Standard | Premium |
| MEDIUM | Standard | Premium | Economy |
| COMPLEX | Premium | Standard | Economy |
If all models fail → honest 503 with retry guidance.
start = time.time()
response = await call_model(prompt, model)
latency_ms = (time.time() - start) * 1000tokens = response.usage.prompt_tokens + response.usage.completion_tokens
PRICE_PER_TOKEN = {
"llama-3.1-8b-instant": 0.06 / 1_000_000,
"llama-3.3-70b-versatile": 0.59 / 1_000_000,
"qwen/qwen3-32b": 0.90 / 1_000_000,
}
actual_cost = tokens * PRICE_PER_TOKEN[model_used]
baseline_cost = tokens * PRICE_PER_TOKEN["qwen/qwen3-32b"]
savings = baseline_cost - actual_cost
savings_pct = (savings / baseline_cost) * 100Complexity-aware composite score:
| Complexity | Cost Weight | Speed Weight | Quality Weight |
|---|---|---|---|
| SIMPLE | 80% | 15% | 5% |
| MEDIUM | 15% | 15% | 70% |
| COMPLEX | 5% | 5% | 90% |
OpenAI-compatible endpoint — drop-in replacement for the OpenAI SDK/base_url. See Use NeuralOps in Your App above.
Response: standard OpenAI chat.completion shape, plus a _neuralops field:
{
"id": "chatcmpl-...",
"object": "chat.completion",
"model": "llama-3.1-8b-instant",
"choices": [{ "message": { "role": "assistant", "content": "..." }, "finish_reason": "stop" }],
"usage": { "prompt_tokens": 12, "completion_tokens": 18, "total_tokens": 30 },
"_neuralops": {
"tier": "economy",
"complexity": "SIMPLE",
"savings_percentage": 93.3,
"actual_cost": 0.0000018,
"cost_without_neuralops": 0.000027,
"routing_reason": "Single factual question",
"model_used": "Llama 3.1 8B",
"latency_ms": 147
}
}OpenAI-compatible model list — auto plus every model NeuralOps can currently route to, each tagged with its real provider and price.
Route a prompt to the optimal model.
Request:
{ "text": "Your prompt here" }Response:
{
"request_id": "uuid",
"response": "Model response text",
"model_used": "Llama 3.1 8B",
"tier": "economy",
"complexity": "SIMPLE",
"confidence": 0.97,
"routing_reason": "Single factual question",
"latency_ms": 147,
"input_tokens": 12,
"output_tokens": 18,
"actual_cost": 0.0000018,
"cost_without_neuralops": 0.000027,
"savings": 0.0000252,
"savings_percentage": 93.3,
"is_fallback": false
}Run the same prompt against all 3 models simultaneously.
Aggregate statistics — total requests, savings, model distribution.
Paginated request history.
Current health state of all model tiers.
Toggle a model's health state (simulate outage/recovery).
{ "model_key": "llama-3.1-8b-instant", "healthy": false }Real-time event stream:
new_request— fired on every routed requeststats_update— updated aggregate statshealth_change— model health state changes
NeuralOps/
├── backend/
│ ├── main.py # FastAPI app, routes, WebSocket
│ ├── classifier.py # LLM + rule-based prompt classifier
│ ├── router.py # Routing table, health, fallback chains
│ ├── model_client.py # Async Groq API calls
│ ├── cost_tracker.py # Token cost math + savings calculation
│ ├── database.py # SQLite schema + async CRUD
│ ├── models.py # Pydantic schemas
│ ├── seed_data.py # Demo data generator
│ └── .env.example # Environment template
│
├── frontend/
│ ├── src/
│ │ ├── App.jsx # Navigation + WebSocket state
│ │ └── components/
│ │ ├── Dashboard.jsx # Live stats + request feed
│ │ ├── BattleMode.jsx # Model comparison arena
│ │ ├── TrafficFlow.jsx # Live traffic visualization
│ │ ├── Insights.jsx # Analytics + intelligence report
│ │ ├── ROICalculator.jsx # Savings projection tool
│ │ ├── SelfHealingPanel.jsx # Health toggle simulation
│ │ └── ui/ # Card, Badge, shared primitives
│ ├── index.html
│ └── vite.config.js
│
└── README.md
- PostgreSQL (replace SQLite)
- API key auth per user
- OpenAI + Anthropic model support
- Rate limiting + usage quotas
- Deploy to Railway + Vercel
-
npm install neuralops-sdk - Python SDK (
pip install neuralops) - 2-line integration for any app
- Webhook support
- Multi-tenant dashboard
- Custom routing rules per customer
- Quality guarantee SLAs
- Usage-based billing (3% of savings, min $99/month)
- On-premise deployment (Ollama)
Contributions are welcome! Please open an issue first to discuss what you'd like to change.
# Fork the repo
# Create your branch
git checkout -b feature/your-feature
# Make changes + commit
git commit -m "feat: your feature description"
# Push and open a PR
git push origin feature/your-featureMIT License — see LICENSE for details.
Built with ⚡ by NeuralOps Team
If this saved you money — give it a ⭐