Changedetection on GKE Autopilot

changedetection.io is a self-hosted, open-source service that monitors web pages for changes and sends notifications when they occur. This module deploys changedetection.io on GKE Autopilot on top of the App_GKE foundation, which provisions and manages the shared Google Cloud and Kubernetes infrastructure.

This guide focuses on the cloud services changedetection.io uses and how to explore and operate them from the Google Cloud Console and the command line. For the mechanics that are common to every GKE application — Workload Identity, ingress, autoscaling, CI/CD, Cloud Armor, IAP, Binary Authorization, VPC Service Controls, backups, and the deployment lifecycle — refer to the App_GKE foundation guide rather than repeating them here.

What changedetection.io costs on RAD, and how that compares

changedetection.io on RAD's GKE Autopilot module costs about US$42 a month in your own Google Cloud project, with no extra licence fee — changedetection.io is open source. In a project RAD manages for you, the same deployment is metered at 67 credits a day (about 2,010 a month, about US$201 at the top-up price). Or see the Cloud Run guide for the lower-cost option. If you only need changedetection.io occasionally — studying, a demo, a short-lived project — a RAD-managed project can be deleted and restored within 30 days for a few credits, so that monthly figure becomes a few dollars instead. See Pause it for free, below.

What you pay on RAD

changedetection.io is open source, so there is no licence fee. You pay for three things:

	In a Google Cloud project you own	In a project RAD manages for you
Module fee, once per deployment	75 credits (US$7.50 at the top-up price)	67.5 credits (10% lower)
Build time	A few credits per build (roughly 3–6 credits)	The same
Google Cloud running cost	Billed by Google to your own billing account, about US$42 a month for the default configuration (table below)	Metered hourly in credits; RAD publishes 67 credits a day, about 2,010 a month (about US$201 at the top-up price)
Who owns what	You own the project and its billing; RAD deploys and updates it	RAD owns the project, with guardrails, quotas and budget alerts; you get console access, and a minimum purchased balance is held in reserve

10 credits cost US$1 on a one-off top-up, and less on a monthly plan. In a project RAD manages, the database, file server and network are shared by every application in the project, so a second application does not add a second database.

Default running cost in your own project (us-central1, Google list prices):

Resource (module default)	Per month
GKE Autopilot pod, 1x vCPU / 1 GiB	US$36.08
Cloud NAT and networking	US$5
Cloud Storage (add-ons, backups)	US$1
GKE cluster management fee	shared across every GKE app in the project; $0 if this is your only cluster, else ~73
Total (excluding the shared GKE cluster fee)	about US$42

These prices are the same in africa-south1 (Johannesburg).

GKE Autopilot does not scale to zero. At least one pod always runs, so the cost above applies whether or not anyone is using the app; extra pods are added only under load.

How it compares, for monitoring pages for changes

changedetection.io's own team sells a hosted plan directly on changedetection.io: US$8.99 a month, including 5,000 URL watches, cancel anytime. That single plan is the simplest way to use it with nothing to run; RAD's module runs the same open-source changedetection.io on infrastructure you can see, with no watch-count ceiling other than what the instance size supports, for the module fee plus the Google Cloud usage above — about US$19 a month in your own project, a little more than the hosted plan but with the data living in your own project.

Pause it for free: delete a RAD-managed project, restore it when you need it

If changedetection.io runs in a project RAD manages for you, deleting the whole project — rather than deleting just this module — unlinks its billing first, then asks Google to delete the project outright. Google keeps it, recoverable, for 30 days, and because billing is already unlinked, nothing is charged while it waits. Unlike deleting one module, this does not tear down Cloud SQL, any VM or the compute resource one by one: the whole project simply stops.

Restoring is a check, not a rebuild. Within 30 days, the project's owner can ask Google to undelete the project and reattach its billing account, then run Update on each deployment to confirm everything came back. Because nothing was individually destroyed, that Update finds the same resources already there, and an Update never charges the module fee again. For a typical 2–3-deployment chain this costs only a handful of credits in total (roughly 5–10 credits, under US$1).
What this needs. You must own the project (not one RAD only manages billing for), restore it yourself within 30 days — after that Google deletes it for good — and clear the tier's admission floor again (100 credits for the sandbox tier most study/demo use fits). Google says most services are fully working again within 36 hours of a restore.
One real gap: nightly backups do not survive. Backups are written to a bucket inside the project, and that bucket has Cloud Storage's soft-delete turned off, so it is very likely gone as soon as the project is deleted — even though the project itself is recoverable for 30 days. Copy a backup out (to Google Drive, or a bucket outside the project) before deleting if there is anything in it worth keeping; for a default install with nothing irreplaceable, this does not matter.

Source: Delete and restore projects.

Pay only while you use it, the other way: delete and redeploy

In your own project, or once the 30-day restore window above has passed, the way to stop paying is to delete the deployment and deploy it again when you need it.

What a redeploy costs. The module fee again, plus the builds: about 79 credits (about US$7.90) at the top-up price, and roughly 15–20 minutes of build time (RAD's own platform average). Deleting saves money only once changedetection.io would otherwise sit unused for about 3 days or more — in your own project that is about US$1.40 a day, and in a RAD-managed one it is 67 credits a day.
Most of the running cost here is usually the database and/or the shared file/cache VM or cluster. They stop only when nothing else in the project uses them, so deleting changedetection.io while another application shares the project saves only changedetection.io's own compute part.
Keep data first. Nightly backups go to a bucket inside the deployment and are deleted with it, so copy the latest backup out before deleting if you want to keep it.

Lab sessions and Managed Environments

Lab sessions, for training. A trainer runs a session for a class. Each participant gets the app in their own Google Cloud project for 15 minutes to 24 hours, within an allowance the trainer sets. Either the trainer funds every place, or each participant pays for their own. Everything is deleted when the session ends and unused credits go back to the trainer.
Managed Environments, for consultancies. A partner runs the app for a client from a ring-fenced wallet it funds, and settles with the client directly. If the wallet runs low, billing pauses and the data is kept, so nobody receives an unexpected charge. At the end the partner hands the project over and the deployments become the client's own.

Credits can be bought in more than 20 currencies, including XAF, XOF, NGN, GHS, KES and ZAR, by card, bank transfer or mobile money.

Sources (8 October 2026): Google Cloud list prices from the Cloud Billing Catalog API; RAD fees and the daily-credit estimate from radmodules.dev/pricing; Delete and restore projects for the pause/restore window and backup-bucket caveat; changedetection.io hosted plan; prices change, check each source before relying on a figure.

1. Overview

changedetection.io runs as a single Python/Flask web workload. The deployment wires together a deliberately small set of Google Cloud services — there is no database and no cache:

Capability	Google Cloud service	Notes
Compute	GKE Autopilot	Python/Flask pod, 1 vCPU / 1 GiB by default, listens on port 5000
Database	None	changedetection.io stores all state on disk, not in SQL
Persistent datastore	Block Persistent Disk (PVC) or Cloud Storage (GCS FUSE)	Mounted at `/datastore`; a block PVC via StatefulSet is strongly preferred
Cache & queue	None	Redis is not used; explicitly disabled
Secrets	Secret Manager	No app secret is injected; the REST API token is created in the web UI
Ingress	Cloud Load Balancing	`ClusterIP` Service behind a Kubernetes Ingress with a reserved static IP and managed certificate (custom domain enabled by default; a `nip.io` host is used when none is set)

Sensible defaults worth knowing up front:

No database, no Redis. changedetection.io is entirely self-contained. There is no Cloud SQL instance, no db-init job, and no schema migration step. Redis is disabled (enable_redis = false) and enable_cloudsql_volume = false.
Block PVC is the default for the datastore. changedetection.io writes a file-based datastore (watch JSON plus history snapshot files) that behaves best on a POSIX block volume, so stateful_pvc_enabled = true by default, mounting a 20Gi block PVC (via a StatefulSet) at /datastore. This automatically disables the GCS FUSE volume at the same path (enable_gcs_storage_volume = false) to avoid a double-mount.
GCS FUSE is the fallback. With stateful_pvc_enabled = false, the datastore GCS bucket is mounted at /datastore via GCS FUSE instead. This works but is less well-suited to the app's file-based writes than a block PVC.
Single replica by default. min_instance_count = 1 and max_instance_count = 1. The fetch scheduler runs in-process against a single datastore; running more than one replica against the same datastore risks concurrent-write corruption. Keep max_instance_count = 1.
No login by default. The dashboard ships with no login. Set a password in Settings → General immediately, and/or front the workload with IAP.
BASE_URL is not set automatically on GKE. Set BASE_URL (the notification-link host) to the external LoadBalancer or custom-domain URL via environment_variables after the external IP is known.
Version pinning. With application_version = "latest" the image build pins a known-good tag (0.50.19) via the app-specific CHANGEDETECTION_VERSION build arg.

2. Google Cloud Services & How to Explore Them

All commands assume you have run gcloud container clusters get-credentials <cluster> --region <region> --project <project> and that PROJECT, REGION, and NAMESPACE are set. The namespace and other identifiers are reported in the deployment Outputs.

A. GKE Autopilot — the changedetection.io workload

changedetection.io pods are scheduled on Autopilot, which bills for the CPU/memory the pods actually request. Since stateful_pvc_enabled = true by default, workload_type auto-resolves to a StatefulSet with a per-pod PVC; set stateful_pvc_enabled = false for a Deployment with a GCS FUSE volume instead.

Console: Kubernetes Engine → Workloads → select the changedetection workload to see pods and events. Kubernetes Engine → Services & Ingress shows the external IP.

CLI:

kubectl get pods,svc -n "$NAMESPACE"
kubectl get statefulset,pvc -n "$NAMESPACE"          # when stateful_pvc_enabled = true
kubectl logs -n "$NAMESPACE" <pod-name> --tail=100

See App_GKE for how Autopilot, scaling, and the workload type (Deployment vs StatefulSet) are managed.

B. Database — not used

changedetection.io uses no SQL database. database_type = NONE is fixed by Changedetection_Common; no Cloud SQL instance is created and there is no db-init initialization job. The db_name/db_user inputs exist only for Foundation compatibility and provision nothing. All persistent state lives in the datastore volume described below.

C. Persistent datastore (PVC or Cloud Storage)

All watch configuration, page snapshots, and change history live under /datastore.

Block PVC (default). With stateful_pvc_enabled = true (the default), a StatefulSet mounts a 20Gi block Persistent Disk at stateful_pvc_mount_path (/datastore).
```
kubectl get pvc -n "$NAMESPACE"
kubectl describe pvc -n "$NAMESPACE" <pvc-name>
```
GCS FUSE (fallback). With stateful_pvc_enabled = false, the datastore GCS bucket is mounted at /datastore via the GCS FUSE CSI driver.
```
gcloud storage buckets list --project "$PROJECT" --filter="name~storage"
gcloud storage ls gs://<data-bucket>/
```

See App_GKE for StatefulSet PVCs, CMEK options, and GCS FUSE mounts.

D. Cache & queue — not used

changedetection.io does not use Redis or any external queue; its watch scheduler is in-process. enable_redis = false is set explicitly (overriding the App_GKE default of true) and no Redis inputs are wired.

E. Secret Manager

No application secret is injected into the pod — the optional REST API token is generated inside the web UI (Settings → API), and the datastore uses no encryption key. Secret Manager is still available for any operator-supplied secret_environment_variables.

Console: Security → Secret Manager.

CLI:

gcloud secrets list --project "$PROJECT"
gcloud secrets versions access latest --secret=<secret-name> --project "$PROJECT"

See App_GKE for the Secret Store CSI integration and rotation.

F. Networking & ingress

By default the Kubernetes Service is ClusterIP (service_type = ClusterIP) and the workload is exposed through a Kubernetes Ingress with a reserved static IP and a Google-managed certificate (enable_custom_domain = true, reserve_static_ip = true). When no application_domains are set, a nip.io hostname derived from the reserved IP is used so HTTPS works out of the box.

Console: Network services → Load balancing; VPC network → IP addresses.

CLI:

kubectl get ingress,svc -n "$NAMESPACE"
gcloud compute addresses list --project "$PROJECT"

See App_GKE for custom domains, Cloud CDN, and static IP details.

G. Cloud Logging & Monitoring

Pod stdout/stderr flow to Cloud Logging; GKE metrics flow to Cloud Monitoring. Optional uptime checks and alert policies are available.

Console: Logging → Logs Explorer; Monitoring → Dashboards / Alerting.

CLI:

gcloud logging read 'resource.type="k8s_container" AND resource.labels.namespace_name="'"$NAMESPACE"'"' \
  --project "$PROJECT" --limit 50

3. changedetection.io Application Behaviour

No first-deploy database setup. There is no database and no init job. On first boot changedetection.io creates its datastore files (url-watches.json and per-watch history directories) under /datastore if they do not already exist.
No schema migrations. Datastore format upgrades are handled internally by the application on start; there is no separate migration step to run.
The datastore volume is the only stateful asset. Everything the app remembers — watches, snapshots, diff history, notification config, and any UI password — lives on the /datastore volume (block PVC or GCS bucket). Deleting the PVC/bucket wipes all state.
No login by default. The dashboard ships open. Set a password under Settings → General → Password immediately after first access, and/or enable IAP in front of the workload. There is no default admin account or credential.
REST API token is created in the UI. To use the REST API, generate a token under Settings → API and pass it as the x-api-key header. It is not injected via an environment variable.
Set BASE_URL after the external IP is known. Unlike the Cloud Run variant, the GKE wrapper does not inject a usable BASE_URL. Set it to the external URL so notification bodies contain working links:
```
kubectl set env -n "$NAMESPACE" statefulset/<name> \
  BASE_URL=https://changedetection.example.com
```
Or set environment_variables = { BASE_URL = "https://…" } in the module config.
Health path. Startup and liveness probes target / — the web UI, which returns HTTP 200 once the Flask server is ready. First boot is fast (no migrations); the default startup probe allows a 15-second initial delay plus a 10-retry window.
Scaling constraint. Keep max_instance_count = 1. Multiple replicas share the same datastore and would race on writes; the app has no distributed coordination.

Inspect the running environment:

kubectl exec -n "$NAMESPACE" <pod-name> -- env | grep -E 'DATASTORE_PATH|BASE_URL'

4. Configuration Variables

Variables are grouped exactly as they appear on the deployment platform. Only settings specific to or notable for changedetection.io are listed; every other input is inherited from App_GKE with its standard behaviour and defaults.