After the gateway is up, you need to expose it. While NodePort is fine for testing, in production you should front this with Nginx or Traefik to handle SSL termination and rate limiting. The raw TCP performance of our network in Oslo means you can expect single-digit millisecond latency to the Norwegian Internet Exchange (NIX), but only if your ingress controller isn't misconfigured. Here is a production-ready Nginx configuration snippet specifically tuned for buffering the large payloads often seen in image processing or batch data ingestion functions.

http {
    # Optimize for high throughput
    sendfile on;
    tcp_nopush on;
    tcp_nodelay on;
    keepalive_timeout 65;
    types_hash_max_size 2048;

    server {
        listen 80;
        server_name functions.your-domain.no;

        location / {
            proxy_pass http://127.0.0.1:8080;
            proxy_set_header Host $host;
            proxy_set_header X-Real-IP $remote_addr;
            
            # Crucial for long-running functions
            proxy_read_timeout 300s;
            proxy_send_timeout 300s;
            
            # Buffer tuning for large JSON payloads
            proxy_buffer_size 128k;
            proxy_buffers 4 256k;
            proxy_busy_buffers_size 256k;
        }
    }
}

Building and Deploying a Function

Now that the infrastructure is solid, let's look at the developer workflow. The faas-cli tool abstracts away the Docker complexity. You define a stack file (YAML) that describes your functions. Let's create a simple Python function that processes a transaction. This is where owning the stack shines: you can install system-level dependencies (like imagemagick or specific C++ libraries) into the Docker container that underlies the function, something that is often impossible or incredibly difficult in restricted public cloud environments.

First, install the CLI:

Now, create a new function scaffold:

This generates a handler.py. We will modify it to simulate a localized task, perhaps validating a Norwegian VIPPS transaction ID format.

# handler.py
import json
import os

def handle(req):
    """handle a request to the function
    Args:
        req (str): request body
    """
    try:
        payload = json.loads(req)
        transaction_id = payload.get("tx_id")
        
        # Simple validation logic
        if not transaction_id or not transaction_id.startswith("NO"):
             return {
                 "statusCode": 400,
                 "body": json.dumps({"error": "Invalid Norwegian Transaction ID"})
             }

        # Simulate processing
        return {
            "statusCode": 200,
            "body": json.dumps({
                "status": "processed",
                "region": "Oslo-DC1",
                "node": os.getenv("HOSTNAME")
            })
        }
        
    except Exception as e:
        return {"statusCode": 500, "body": str(e)}

To deploy this, we update the payment-processor.yml file. Notice how we can define environment variables and constraints directly here. This file becomes your infrastructure-as-code manifest.

version: 1.0
provider:
  name: openfaas
  gateway: http://127.0.0.1:8080
functions:
  payment-processor:
    lang: python3
    handler: ./payment-processor
    image: your-docker-user/payment-processor:latest
    environment:
      write_debug: true
    # Security context to prevent root escalation
    # Essential for multi-tenant environments
    annotations:
      com.openfaas.security.readonly: true

Finally, build and deploy via the CLI. This connects to the local Docker socket, builds the image, pushes it (if a registry is configured, or use local cache), and notifies the gateway.

Optimizing for Stability

One war story from 2021 involves a client who ran a scraping cluster. They didn't set resource limits, and one rogue function ate all the RAM, causing the Linux OOM Killer to murder the containerd process. The fix? strict resource limits in your function definition. On a CoolVDS instance, you have dedicated resources, but you still must manage them. Add limits_memory: 128m to your YAML. Additionally, ensure your liveness probes are configured correctly in the function template to avoid Kubernetes restarting pods that are just doing heavy computation.

By controlling the stack on CoolVDS, you avoid the "Noisy Neighbor" effect. In public clouds, your Lambda function might share a physical CPU core with a crypto-miner running in another tenant's account, causing unpredictable jitter. With our KVM isolation, your CPU cycles are yours alone. This consistency is paramount for financial transactions or real-time data processing where millisecond deviations are unacceptable.

Conclusion: Own Your Platform

Serverless is a powerful architectural pattern, but it shouldn't come at the cost of your digital sovereignty or your budget's predictability. By leveraging the raw power of NVMe-backed VPS instances in Norway, you can build a platform that outperforms public cloud FaaS offerings while remaining fully compliant with EU data regulations. The tools—Kubernetes, OpenFaaS, Docker—are mature and ready. The only missing piece is the infrastructure.

Don't let slow I/O kill your innovative architecture. Deploy a high-performance KVM instance on CoolVDS today and start building your private cloud in under 55 seconds.