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How to Deploy Llama 3.2 with Ollama + Prometheus Monitoring on a $5/Month DigitalOcean Droplet: Production-Grade Inference with Cost Tracking
Stop overpaying for AI APIs — here's what serious builders do instead.
I was paying $47/month to OpenAI for inference on my side project. Then I realized: I could run Llama 3.2 on a $5/month DigitalOcean Droplet, add Prometheus monitoring, and track exactly what each request costs me. The setup takes about 45 minutes. It runs 24/7 without touching it. And I now know my true cost per inference to the cent.
This isn't a toy setup. This is production-grade infrastructure that handles real traffic, exports metrics to Prometheus, and gives you the observability you need to optimize costs and performance. By the end of this guide, you'll have:
- Llama 3.2 running with Ollama on minimal hardware
- Prometheus scraping inference metrics every 15 seconds
- A cost-per-request tracking system
- Real dashboards showing token throughput, latency, and resource utilization
- A deployment you can scale or modify without vendor lock-in
Let's build it.
Prerequisites
You'll need:
- A DigitalOcean account (or another VPS provider — this works on Linode, Vultr, Hetzner too)
- SSH access to a terminal
- 10GB of free disk space minimum (Llama 3.2 is ~7GB)
- 4GB RAM minimum (8GB recommended for comfortable inference)
- Basic Linux command familiarity
- Docker knowledge is helpful but not required — I'll give you all the commands
The total cost: $5/month for the Droplet, plus whatever you're already paying for monitoring infrastructure (Prometheus can run on the same machine).
👉 I run this on a \$6/month DigitalOcean droplet: https://m.do.co/c/9fa609b86a0e
Part 1: Setting Up Your DigitalOcean Droplet
Create a new Droplet with these specs:
- OS: Ubuntu 22.04 LTS
- Size: Basic, 2GB RAM, 1 vCPU, 50GB SSD ($5/month)
- Region: Choose closest to you (latency matters for inference)
- Authentication: SSH key (not password)
Once it's live, SSH in:
ssh root@your_droplet_ip
Update the system:
apt update && apt upgrade -y
apt install -y curl wget git build-essential
Check your available resources:
free -h
df -h
You'll see something like:
total used free
Mem: 1.9Gi 180Mi 1.7Gi
This is tight, but Ollama is optimized for exactly this scenario. The model runs in-memory, and modern LLMs compress well.
Part 2: Install Ollama
Ollama is a single binary that manages model downloads, GPU/CPU optimization, and serves an API. Installation is one line:
curl https://ollama.ai/install.sh | sh
Start the Ollama service:
systemctl start ollama
systemctl enable ollama
Verify it's running:
systemctl status ollama
You should see:
● ollama.service - Ollama
Loaded: loaded (/etc/systemd/system/ollama.service; enabled; vendor preset: enabled)
Active: active (running) since [timestamp]
Now pull the Llama 3.2 model. This is ~7GB, so it takes a few minutes:
ollama pull llama2:7b
Note: I'm using Llama 2 7B here because it's proven stable on 2GB RAM. Llama 3.2 exists, but for a $5 Droplet, 7B is the sweet spot. If you upgrade to 4GB RAM, use ollama pull llama2:13b instead.
Test the model:
curl http://localhost:11434/api/generate -d '{
"model": "llama2:7b",
"prompt": "Why is monitoring important for LLMs?",
"stream": false
}'
You'll get a JSON response with the generated text. This confirms Ollama is working.
Part 3: Set Up Prometheus Monitoring
Ollama exposes metrics on port 11434 at /metrics. We need Prometheus to scrape them.
Install Prometheus:
wget https://github.com/prometheus/prometheus/releases/download/v2.48.0/prometheus-2.48.0.linux-amd64.tar.gz
tar xvfz prometheus-2.48.0.linux-amd64.tar.gz
cd prometheus-2.48.0.linux-amd64
Create the Prometheus config file:
cat > prometheus.yml << 'EOF'
global:
scrape_interval: 15s
evaluation_interval: 15s
external_labels:
monitor: 'ollama-inference'
scrape_configs:
- job_name: 'ollama'
static_configs:
- targets: ['localhost:11434']
metrics_path: '/metrics'
scrape_interval: 15s
EOF
Start Prometheus:
./prometheus --config.file=prometheus.yml &
Or run it in the background persistently. Create a systemd service:
cat > /etc/systemd/system/prometheus.service << 'EOF'
[Unit]
Description=Prometheus
Wants=network-online.target
After=network-online.target
[Service]
User=root
Type=simple
ExecStart=/root/prometheus-2.48.0.linux-amd64/prometheus --config.file=/root/prometheus-2.48.0.linux-amd64/prometheus.yml
Restart=on-failure
RestartSec=5s
[Install]
WantedBy=multi-user.target
EOF
Enable and start:
systemctl daemon-reload
systemctl start prometheus
systemctl enable prometheus
Verify Prometheus is scraping:
curl http://localhost:9090/api/v1/targets
You should see the Ollama target with state "up".
Part 4: Build a Cost-Tracking Wrapper
Ollama's /api/generate endpoint returns token counts. We'll build a simple Python wrapper that tracks costs per request.
Install Python and dependencies:
apt install -y python3 python3-pip
pip install requests prometheus-client
Create a cost-tracking script:
cat > /root/ollama_cost_tracker.py << 'EOF'
#!/usr/bin/env python3
import json
import requests
import time
from datetime import datetime
from prometheus_client import Counter, Histogram, Gauge, start_http_server
# Prometheus metrics
request_count = Counter('ollama_requests_total', 'Total requests', ['model'])
token_count = Counter('ollama_tokens_total', 'Total tokens generated', ['model'])
prompt_tokens = Counter('ollama_prompt_tokens_total', 'Total prompt tokens', ['model'])
inference_latency = Histogram('ollama_inference_seconds', 'Inference latency', ['model'], buckets=[0.5, 1, 2, 5, 10, 30, 60])
cost_per_request = Gauge('ollama_cost_per_request_usd', 'Cost per request in USD', ['model'])
total_cost = Gauge('ollama_total_cost_usd', 'Cumulative cost in USD', ['model'])
# Cost constants (tokens per million)
# Llama 2 7B: ~$0.00001 per token (self-hosted, just tracking)
# This is what you'd pay on OpenRouter for comparison
OPENROUTER_LLAMA2_7B_COST = 0.00001 # per token
OLLAMA_COST = 0.0 # Self-hosted, but track as if using OpenRouter
running_cost = {}
def track_inference(model, response_data):
"""Track inference metrics and costs"""
tokens = response_data.get('eval_count', 0)
prompt_toks = response_data.get('prompt_eval_count', 0)
request_count.labels(model=model).inc()
token_count.labels(model=model).inc(tokens)
prompt_tokens.labels(model=model).inc(prompt_toks)
# Track latency from response time
total_duration = response_data.get('total_duration', 0) / 1e9 # nanoseconds to seconds
inference_latency.labels(model=model).observe(total_duration)
# Calculate cost (self-hosted is free, but show OpenRouter equivalent)
request_cost = (tokens + prompt_toks) * OPENROUTER_LLAMA2_7B_COST
cost_per_request.labels(model=model).set(request_cost)
if model not in running_cost:
running_cost[model] = 0.0
running_cost[model] += request_cost
total_cost.labels(model=model).set(running_cost[model])
return {
'tokens': tokens,
'prompt_tokens': prompt_toks,
'latency_seconds': total_duration,
'request_cost_usd': request_cost,
'cumulative_cost_usd': running_cost[model]
}
def generate(model, prompt, stream=False):
"""Wrapper around Ollama API with cost tracking"""
url = 'http://localhost:11434/api/generate'
payload = {
'model': model,
'prompt': prompt,
'stream': stream
}
start_time = time.time()
response = requests.post(url, json=payload, timeout=300)
response.raise_for_status()
data = response.json()
cost_data = track_inference(model, data)
print(f"[{datetime.now().isoformat()}] Model: {model}")
print(f" Tokens: {cost_data['tokens']}")
print(f" Prompt Tokens: {cost_data['prompt_tokens']}")
print(f" Latency: {cost_data['latency_seconds']:.2f}s")
print(f" Request Cost (OpenRouter equiv): ${cost_data['request_cost_usd']:.6f}")
print(f" Cumulative Cost: ${cost_data['cumulative_cost_usd']:.4f}")
print()
return data
if __name__ == '__main__':
# Start Prometheus metrics server on port 8000
start_http_server(8000)
print("Cost tracker metrics available at http://localhost:8000/metrics")
# Example inference
response = generate('llama2:7b', 'What is machine learning?')
print(f"Generated: {response['response'][:100]}...")
EOF
chmod +x /root/ollama_cost_tracker.py
Run the tracker:
python3 /root/ollama_cost_tracker.py
This exposes metrics on port 8000. Update your Prometheus config to scrape it:
cat >> /root/prometheus-2.48.0.linux-amd64/prometheus.yml << 'EOF'
- job_name: 'cost-tracker'
static_configs:
- targets: ['localhost:8000']
metrics_path: '/metrics'
EOF
Restart Prometheus:
systemctl restart prometheus
Part 5: Create a Production-Grade API Wrapper
Now let's wrap everything in a proper HTTP service that tracks costs in real-time.
Install FastAPI:
pip install fastapi uvicorn
Create the API service:
bash
cat > /root/ollama_api.py << 'EOF'
#!/usr/bin/env python3
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
import requests
import time
import json
from datetime import datetime
from prometheus_client import Counter, Histogram, Gauge, generate_latest, CONTENT_TYPE_LATEST
from fastapi.responses import Response
import uvicorn
app = FastAPI(title="Ollama Cost-Tracked API")
# Prometheus metrics
request_count = Counter(
'ollama_api_requests_total',
'Total API requests',
['model', 'endpoint']
)
token_count = Counter(
'ollama_api_tokens_total',
'Total tokens generated',
['model']
)
inference_latency = Histogram(
'ollama_api_inference_seconds',
'Inference latency',
['model'],
buckets=[0.1, 0.5, 1, 2, 5, 10, 30, 60]
)
error_count = Counter(
'ollama_api_errors_total',
'Total errors',
['model', 'error_type']
)
class GenerateRequest(BaseModel):
model: str
prompt: str
temperature: float = 0.7
top_p: float = 0.9
class GenerateResponse(BaseModel):
response: str
tokens: int
latency_seconds: float
cost_usd: float
@app.post("/generate")
async def generate(request: GenerateRequest):
"""Generate text with cost tracking"""
start_time = time.time()
try:
# Call Ollama
response = requests.post(
'http://localhost:11434/api/generate',
json={
'model': request.model,
'prompt': request.prompt,
'temperature': request.temperature,
'top_p': request.top_p,
'stream': False
},
timeout=300
)
response.raise_for_status()
data = response.json()
latency = time.time() - start_time
# Extract metrics
tokens = data.get('eval_count', 0)
prompt_tokens = data.get('prompt_eval_count', 0)
total_tokens = tokens + prompt_tokens
# Cost calculation (self-hosted is free, but track OpenRouter equivalent)
cost = total_tokens * 0.00001
# Record metrics
request_count.labels(model=request.model, endpoint='generate').inc()
token_count.labels(model=request.model).inc(tokens)
inference_latency.labels(model=request.model).observe(latency)
return GenerateResponse(
response=data['response'],
tokens=tokens,
latency_seconds=latency,
cost_usd=cost
)
except requests.exceptions.Timeout:
error_count.labels(model=request.model, error_type='timeout').inc()
raise HTTPException(status_code=504, detail="Inference timeout")
except requests.exceptions.ConnectionError:
error_count.labels(model=request.model, error_type='connection').inc()
raise HTTPException(status_code=503, detail="Ollama service unavailable")
except Exception as e:
error_count.labels(model=request.model, error_type='unknown').inc()
raise HTTPException(status_code=500, detail=str(e))
@app.get("/metrics")
async def metrics():
"""Prometheus metrics endpoint"""
return Response(generate_latest(), media_type=CONTENT_TYPE_LATEST)
@app.get("/health")
async def health():
"""Health check"""
try:
response = requests.get('http://localhost:11434/api/tags', timeout=5)
response.raise_for_status()
return {'status': 'healthy', 'timestamp': datetime.now().isoformat()}
except:
return {'status': 'unhealthy', 'timestamp': datetime.now().isoformat()}
if __name__ == '__main__':
uvicorn.run(
---
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---
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---
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