Building and Deploying Serverless Functions on Google Cloud with Python

Prerequisites

To follow this guide, you should have a basic understanding of

and

HTTP

request handling. Familiarity with

Google Cloud Platform

(GCP) and terminal-based package management is recommended. You will need a GCP account with billing enabled to deploy resources.

Key Libraries & Tools

• Functions Framework: A Google-provided open-source library that lets you run your functions locally to simulate the cloud environment.
• Pulumi: An infrastructure-as-code tool that uses standard programming languages to define cloud resources.
• uv: A high-performance Python package installer and resolver used to manage project dependencies.
• Flask: The underlying web framework used by Google Cloud Functions to handle routing.

Code Walkthrough

1. Local Development

You can create a function by defining a simple Python routine. The

allows you to serve this locally for testing.

import functions_framework

@functions_framework.http
def hello_world(request):
    return "Hello, Serverless!"

Run this locally using the command: functions-framework --target hello_world --port 8080. This provides an immediate feedback loop before cloud deployment.

2. Infrastructure as Code with Pulumi

To deploy, use

to define the necessary infrastructure. You must create a storage bucket to hold the source code artifact and then define the function resource itself.

import pulumi
import pulumi_gcp as gcp

# Create a bucket for the code
bucket = gcp.storage.Bucket("function-bucket")

# Define the cloud function
cloud_fn = gcp.cloudfunctionsv2.Function("my-function",
    build_config=gcp.cloudfunctionsv2.FunctionBuildConfigArgs(
        runtime="python311",
        entry_point="hello_world",
    ),
    service_config=gcp.cloudfunctionsv2.FunctionServiceConfigArgs(
        max_instance_count=10,
        available_memory="256M",
        timeout_seconds=60,
    ))

3. Simulating Sub-routes

Since

typically only expose a single entry point, you can use an internal

Flask

app to handle complex routing within that single function.

from flask import Flask, request

internal_app = Flask(__name__)

@internal_app.route("/user/<id>")
def get_user(id):
    return {"user_id": id}

@functions_framework.http
def entry_point(request):
    with internal_app.test_request_context(path=request.path, method=request.method):
        return internal_app.dispatch_request()

Syntax Notes

When using

, you define resources as Python objects. Note the service_config block, which dictates how the cloud provider allocates resources.

Google Cloud Functions

specifically look for a requirements.txt file for dependency resolution; if you use

uv

, you must export your lockfile to this format during the build process.

Practical Examples

Serverless functions excel at event-driven tasks. Common use cases include processing an invoice whenever a PDF is uploaded to a storage bucket, sending a welcome email via an

trigger during user registration, or performing data validation on a

Pub/Sub

message stream.

Tips & Gotchas

• Cold Starts: If a function hasn't been called recently, the first request may be slow as the provider spins up a new container instance. Keep dependencies light to minimize this delay.
• Security: Never hardcode API keys. Use
  Google Cloud Secret Manager
  to store sensitive values and inject them as environment variables into your function.
• Access Control: Be careful with the allUsers invoker role; it makes your function public, which could lead to unexpected costs if abused by third parties.