GCP Cloud Functions

SkillCloud & infra

Deploy serverless functions on Google Cloud Platform with triggers, IAM roles, environment variables, and monitoring. Use for event-driven computing on GCP.

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the GCP Cloud Functions skill

What this skill tells your AI

The instructions your AI receives, as published by aj-geddes/useful-ai-prompts in skills/gcp-cloud-functions/SKILL.md and read by ahel’s review.

Build and deploy event-driven serverless applications with Google Cloud Functions (Gen1 and Gen2).

When to Use

  • Processing webhooks, API endpoints, or lightweight HTTP backends
  • Reacting to events from Pub/Sub, Cloud Storage, Firestore, or Eventarc
  • Running scheduled tasks (cron) without maintaining a server
  • Building data-processing pipelines triggered by file uploads
  • Prototyping microservices before committing to Cloud Run or GKE

Prerequisites

  • Google Cloud SDK (gcloud) installed and authenticated
  • APIs enabled: Cloud Functions, Cloud Build, Artifact Registry, Cloud Run (Gen2)
  • IAM role roles/cloudfunctions.developer (or roles/run.developer for Gen2)
gcloud services enable cloudfunctions.googleapis.com cloudbuild.googleapis.com \
  artifactregistry.googleapis.com run.googleapis.com eventarc.googleapis.com

Gen1 vs Gen2 Comparison

FeatureGen1Gen2 (recommended)
RuntimeCloud Functions infraBuilt on Cloud Run
Max timeout9 minutes60 minutes
Max memory8 GB32 GB
Concurrency1 request/instanceUp to 1000/instance
Traffic splittingNoYes
Eventarc triggersNoYes

Deploy an HTTP Function (Gen2)

# Python HTTP function
gcloud functions deploy hello-http \
  --gen2 --region=us-central1 --runtime=python312 \
  --trigger-http --allow-unauthenticated \
  --entry-point=hello_http \
  --memory=256Mi --timeout=60s \
  --min-instances=0 --max-instances=100 \
  --set-env-vars=APP_ENV=production --source=.

# Node.js HTTP function
gcloud functions deploy hello-node \
  --gen2 --region=us-central1 --runtime=nodejs20 \
  --trigger-http --allow-unauthenticated \
  --entry-point=helloNode --memory=256Mi --source=.

Deploy a Pub/Sub Triggered Function

gcloud pubsub topics create order-events

gcloud functions deploy process-order \
  --gen2 --region=us-central1 --runtime=python312 \
  --trigger-topic=order-events \
  --entry-point=process_order \
  --memory=512Mi --timeout=120s --retry \
  --service-account=order-processor@${PROJECT_ID}.iam.gserviceaccount.com \
  --source=.

Deploy a Cloud Storage Triggered Function

gcloud functions deploy process-upload \
  --gen2 --region=us-central1 --runtime=python312 \
  --trigger-event-filters="type=google.cloud.storage.object.v1.finalized" \
  --trigger-event-filters="bucket=my-upload-bucket" \
  --entry-point=process_upload \
  --memory=1Gi --timeout=300s --source=.

Deploy a Scheduled Function

gcloud functions deploy daily-cleanup \
  --gen2 --region=us-central1 --runtime=python312 \
  --trigger-http --no-allow-unauthenticated \
  --entry-point=daily_cleanup --source=.

gcloud scheduler jobs create http daily-cleanup-job \
  --schedule="0 2 * * *" \
  --uri="https://us-central1-${PROJECT_ID}.cloudfunctions.net/daily-cleanup" \
  --http-method=POST \
  --oidc-service-account-email=scheduler-sa@${PROJECT_ID}.iam.gserviceaccount.com \
  --location=us-central1

Python Function Examples

# main.py
import functions_framework
import base64, json
from flask import jsonify
from google.cloud import firestore

@functions_framework.http
def hello_http(request):
    """HTTP Cloud Function."""
    name = request.args.get("name", "World")
    return jsonify({"message": f"Hello, {name}!", "status": "ok"}), 200

@functions_framework.cloud_event
def process_order(cloud_event):
    """Triggered by a Pub/Sub message."""
    data = base64.b64decode(cloud_event.data["message"]["data"]).decode("utf-8")
    order = json.loads(data)
    db = firestore.Client()
    db.collection("orders").document(order["id"]).set({
        "status": "processing", "items": order["items"], "total": order["total"],
    })

@functions_framework.cloud_event
def process_upload(cloud_event):
    """Triggered when a file is uploaded to Cloud Storage."""
    data = cloud_event.data
    bucket_name, file_name = data["bucket"], data["name"]
    if not file_name.lower().endswith((".png", ".jpg", ".jpeg")):
        return
    from google.cloud import vision
    client = vision.ImageAnnotatorClient()
    image = vision.Image(source=vision.ImageSource(
        gcs_image_uri=f"gs://{bucket_name}/{file_name}"))
    labels = [l.description for l in client.label_detection(image=image).label_annotations]
    print(f"Labels for {file_name}: {labels}")
# requirements.txt
functions-framework==3.*
google-cloud-firestore==2.*
google-cloud-storage==2.*
google-cloud-vision==3.*
flask>=2.0

Node.js Function Examples

// index.js
const functions = require("@google-cloud/functions-framework");

functions.http("helloNode", (req, res) => {
  const name = req.query.name || "World";
  res.json({ message: `Hello, ${name}!`, status: "ok" });
});

functions.cloudEvent("processMessage", (cloudEvent) => {
  const data = Buffer.from(cloudEvent.data.message.data, "base64").toString();
  console.log(`Processing: ${JSON.parse(data)}`);
});

Managing Deployed Functions

gcloud functions list --gen2 --region=us-central1
gcloud functions describe hello-http --gen2 --region=us-central1
gcloud functions logs read hello-http --gen2 --region=us-central1 --limit=50
gcloud functions delete hello-http --gen2 --region=us-central1 --quiet

# Update env vars without redeploying code
gcloud functions deploy hello-http --gen2 --region=us-central1 \
  --update-env-vars=APP_ENV=staging

# Test locally before deploying
functions-framework --target=hello_http --port=8080

Terraform Configuration

resource "google_cloudfunctions2_function" "api" {
  name     = "hello-http"
  location = "us-central1"

  build_config {
    runtime     = "python312"
    entry_point = "hello_http"
    source {
      storage_source {
        bucket = google_storage_bucket.source.name
        object = google_storage_bucket_object.source.name
      }
    }
  }

  service_config {
    min_instance_count    = 0
    max_instance_count    = 100
    available_memory      = "256Mi"
    timeout_seconds       = 60
    service_account_email = google_service_account.fn.email
    environment_variables = { APP_ENV = "production" }
  }
}

resource "google_cloud_run_service_iam_member" "invoker" {
  location = google_cloudfunctions2_function.api.location
  service  = google_cloudfunctions2_function.api.name
  role     = "roles/run.invoker"
  member   = "allUsers"
}

resource "google_cloudfunctions2_function" "processor" {
  name     = "process-order"
  location = "us-central1"

  build_config {
    runtime     = "python312"
    entry_point = "process_order"
    source {
      storage_source {
        bucket = google_storage_bucket.source.name
        object = google_storage_bucket_object.source.name
      }
    }
  }

  service_config {
    max_instance_count    = 50
    available_memory      = "512Mi"
    timeout_seconds       = 120
    service_account_email = google_service_account.fn.email
  }

  event_trigger {
    trigger_region = "us-central1"
    event_type     = "google.cloud.pubsub.topic.v1.messagePublished"
    pubsub_topic   = google_pubsub_topic.orders.id
    retry_policy   = "RETRY_POLICY_RETRY"
  }
}

Troubleshooting

SymptomCauseFix
PERMISSION_DENIED on deployMissing Cloud Build or Artifact Registry permsGrant roles/cloudbuild.builds.builder to Cloud Build SA
Function deploys but returns 403Missing roles/run.invoker for Gen2Add --allow-unauthenticated or grant invoker role
Cold start latency > 5sLarge dependencies or no min instancesSet --min-instances=1; reduce deps; use lazy imports
Pub/Sub messages redeliveredFunction errors or times outIncrease --timeout; fix error handling; add dead-letter topic
Build failed during deploySyntax error or missing dependencyCheck gcloud builds log; verify requirements.txt
Cannot connect to VPC resourceFunction not on VPC connectorAdd --vpc-connector=my-connector to deploy

Related Skills

  • gcp-networking - VPC connectors for accessing private resources from functions
  • gcp-cloud-sql - Connecting Cloud Functions to managed databases
  • terraform-gcp - Deploy Cloud Functions with Infrastructure as Code
  • gcp-gke - When workloads outgrow serverless and need Kubernetes

Signals

GitHub stars
336
Forks
55
Last commit
Mar 2026

Others that do the same job

Advanced
Catalog kind
skill
Gateway key
gcp-cloud-functions
Source
github.com/aj-geddes/useful-ai-prompts