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Use Cloud Scheduler to trigger a Cloud Run service or job in the Mumbai region, asia-south1. Run headless Chrome with Puppeteer or Playwright in that workload, save each capture to your chosen destination, and secure the scheduler’s invocation with an authorized service account. Set the schedule and its time zone in Cloud Scheduler; the schedule’s time zone determines when the cron expression runs.
Choose a Cloud Run service or job
Both patterns work with Cloud Scheduler. Choose based on how you want a capture run to start and finish, rather than assuming one is universally better. Google documents scheduling for both a securely invoked Cloud Run service and a Cloud Run job (Cloud Run services; Cloud Run jobs).
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| Pattern | Invocation shape | Good fit | Authentication and operations |
|---|---|---|---|
| Cloud Run service | Cloud Scheduler makes an authenticated HTTP request to the service endpoint. | Your capture begins when an HTTP request reaches an endpoint, such as a route that starts one capture or a batch. | Configure authenticated invocation; the Google tutorial uses an OIDC token and service account. Inspect service logs and handle the HTTP response. |
| Cloud Run job | Cloud Scheduler starts a job execution; the process runs and exits when its work is done. | A task-oriented capture run that should complete as a finite execution, rather than remain available as a request-handling service. | Choose a service account permitted to invoke the job. Inspect the execution and its logs, and decide how the run records or stores output. |
The official guides establish these scheduling mechanisms, but they do not provide a screenshot-workload cost comparison. Choose based on invocation shape, permissions, and how you want to operate the capture.
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Set up the Mumbai resources
- Enable Cloud Scheduler API. The scheduler needs its API enabled before you create a job. Follow Google’s Cloud Scheduler job setup.
- Deploy the capture workload in Mumbai. Select
asia-south1for Cloud Run. Google identifies this region as Mumbai, India for both Cloud Run and Cloud Scheduler (Cloud Run locations; Cloud Scheduler locations). - Decide where outputs and dependent services live. The screenshot destination is an implementation choice; configure the workload to write captures to your chosen storage or deliver them to your application. Google notes that using connected products in different locations can affect latency and cost. Keep that in mind when choosing the locations of the workload and any services it depends on.
- Implement the browser capture. Package headless Chrome and a browser automation library in the container. Google’s Cloud Run browser automation guidance names Puppeteer and Playwright and identifies web-page screenshots as a use case (Browser automation on Cloud Run). The capture script, retry behavior, and output-handling design are yours to choose; Google’s guidance does not prescribe a screenshot-specific implementation.
Write and verify the schedule
Cloud Scheduler uses a five-field Unix-cron expression and a separately selected time zone. For example, 0 */3 * * * is Google’s documented example for running every three hours (Cron job schedules and time zones). Choose the time zone that matches the operating hours you intend; don’t assume the cron text alone communicates that choice.
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- Create a Cloud Scheduler job and choose
asia-south1for the scheduler location. - Enter the five-field cron expression for the desired recurrence and select its time zone.
- Set the target to the Cloud Run service or job. For a service, configure an authenticated HTTP target; for a job, configure the job execution target.
- Select or configure the service account used for invocation and grant only the permissions it needs to invoke the target.
- Run an initial invocation, then check the Cloud Run service logs or job execution logs and confirm that the output reached its intended destination.
Google’s HTTP function scheduling tutorial says an initial invocation can take a few minutes to configure and run. That observation is specific to the tutorial’s setup and should not be treated as a guaranteed startup time for every Cloud Run screenshot workload (HTTP target tutorial).
Handle access, output, and failures deliberately
- Protect the trigger. Do not leave an endpoint public merely to make scheduling easy. Use the authenticated configuration documented for the service or the permissions required to invoke a job.
- Make the capture result observable. Log the target URL and whether capture succeeded, and make failures visible in Cloud Run logs or job execution status. Choose an output destination and a way to identify each scheduled run.
- Decide retry and duplicate behavior. The official browser automation guidance does not prescribe a screenshot retry policy. If you add retries, make output handling safe when a run is repeated, for example by using a run timestamp or stable object naming scheme appropriate to your retention needs.
- Account for external pages. A page may be slow, unavailable, or require authentication. Configure browser timeouts and any required headers or cookies for your use case, and avoid treating a successful scheduler trigger as proof that a useful image was captured.
Troubleshooting scheduled captures
- The scheduler cannot invoke the target: verify the Cloud Scheduler API is enabled, that the target URL or job is correct, and that the configured service account has permission to invoke it. For a service, check the authentication configuration and OIDC setup; do not solve an authorization error by making the endpoint public unless that is an intentional security decision.
- The job runs at an unexpected time: check the selected time zone as well as the five cron fields. Confirm that the expression represents the recurrence you intend.
- The run starts but produces no image: inspect the Cloud Run logs and execution result, then check navigation errors, browser startup, the target page’s access requirements, and the configured output destination. Scheduler success only indicates that the target was triggered; it does not establish that the page rendered correctly.
- The page is blank or incomplete: check the browser’s navigation and waiting logic, the page’s client-side rendering, and any lazy-loaded content. Adjust the capture implementation’s wait conditions to the site rather than relying on an assumed fixed delay.
- Captures are slower or costlier than expected: there is no documented screenshot latency or workload-specific cost comparison in the cited guidance. Review execution logs and dependent-service locations; cross-location use can affect latency and cost.
Or skip the browser setup
ScreenshotNeo is a website screenshot API and MCP server for developers. Instead of packaging and maintaining headless Chrome in your own Cloud Run workload, you can request a capture with one GET call. Its API supports PNG, JPEG, WebP, or PDF output, and accepts the parameter names other screenshot APIs use. See the ScreenshotNeo API documentation for request options.
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Example cURL request, using the target URL https://stripe.com:
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curl -G "https://api.screenshotneo.com/v1/shot" -d access_key=YOUR_API_KEY --data-urlencode url=https://stripe.com -o shot.webpCookie and consent banners, newsletter popups, and chat widgets are removed before capture; each cleanup step can be turned off. Bot checks or CAPTCHAs, blank pages, timeouts, failed loads, and cache hits are not billed, and response headers identify the page verdict and billing status. An MCP server provides take_screenshot, get_page_info, and capture_pdf tools for AI agents and MCP clients. The free plan includes 1,000 shots per month with no card; paid plans start at $5 for 3,000 shots.
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Frequently Asked Questions
Can Cloud Scheduler run a Playwright screenshot job in Mumbai?
Yes. Google documents browser automation with Playwright on Cloud Run and scheduling Cloud Run jobs; use Mumbai, asia-south1, for the relevant resources.
Does putting the scheduler and Cloud Run in Mumbai guarantee a particular screenshot speed?
No. The cited Google documentation does not establish a guaranteed screenshot latency. Page behavior, workload configuration, and the locations of connected services can affect the result.
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