You can run PageSpeed Insights (PSI) for hundreds of URLs and collect the results in one spreadsheet, but the documented PSI API accepts one URL per request. The practical solution is to automate repeated runPagespeed calls, extract consistent fields from each response, and write the results to Google Sheets in batches. This guide shows how to design that workflow so failures, missing field data, and changing Lighthouse versions remain visible.
How the batch workflow works
Google’s PageSpeed Insights API v5 analyzes the URL supplied in the required url parameter. Its documented method, runPagespeed, is a single-URL request; Google does not document a native bulk-URL request. To cover hundreds of pages, a script or other client must call the method once per URL and combine selected response fields into spreadsheet rows. See Google’s PageSpeed Insights API overview and runPagespeed method reference.
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- Prepare the URL list. Put one complete URL per row in a source column. Remove blank rows and decide whether redirects should be reported under the submitted URL, the final URL, or both.
- Set consistent analysis options. Choose a strategy—desktop or mobile—and the Lighthouse categories to request. The API defaults to desktop; if you omit the category parameter, the method runs Performance only.
- Request each URL. Loop through the list and call
runPagespeedwith the same options for every URL in the batch. Treat each response independently so a failed request does not discard successful results for other pages. - Extract fields you will compare. Read a stable set of values—such as the performance score, selected lab metrics, field-data availability, and final URL—instead of copying the entire nested audit response into cells.
- Write rows in batches. Accumulate results and send them to the spreadsheet in fewer write operations rather than making a separate spreadsheet write for every cell or URL.
- Keep failures and missing data in the output. Write an error or unavailable-data status for an affected URL rather than silently skipping it. This makes the final sheet a record of the whole input list, not just the pages that succeeded.
Those steps describe the workflow rather than a ready-made script: the Google API documentation establishes the request and response pattern, while batching, row design, pacing, and recovery are implementation choices. Google’s getting-started guide explains API access and the request format.
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Choose what each spreadsheet row should record
A useful report needs enough context to interpret a score later. Create one row per requested URL and include the settings and outcome alongside the measurements.
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| Column | What to record | Why it matters |
|---|---|---|
| Input URL | The URL submitted to PSI | Preserves the original item from your list. |
| Final URL | The resolved URL reported in the response, when available | Shows whether a redirect or canonical destination affected the analysis. |
| Fetch time | Date and time of the request | Helps distinguish separate runs and interpret results that may change over time. |
| Strategy | Desktop or mobile | Identifies the requested analysis context. |
| Category | The requested Lighthouse category or categories | Makes clear what the score or audit results represent. |
| Performance score | The returned score, if present | Provides a compact summary; retain the relevant metrics because a score alone does not explain a result. |
| Lab metrics and audit summaries | The selected Lighthouse values useful to your review | Supports diagnosis without placing the full nested response in the main report. |
| Field data | Availability and reported values, if present | Separates real-user data from Lighthouse lab results. |
| Field-data granularity | Page-level URL or origin-level, if identified | Prevents values from a whole origin being mistaken for data about one page. |
| Status and error | Success, API status, or error details | Shows which URLs need a retry or investigation. |
| Lighthouse version | The version in the response, when present | Provides context when results or response fields differ across runs. |
Keep a copy of the raw response outside the main spreadsheet if you need deeper audit details or want to reprocess results later. Parse defensively: response structure can evolve, and a field that is absent should not cause the entire URL’s row to disappear.
Understand lab results and field data before comparing pages
Lighthouse lab data
PSI’s Lighthouse analysis runs in a controlled environment. It is useful for finding and debugging performance issues, but it does not necessarily reproduce every real-user bottleneck. Treat it as a diagnostic measurement under the chosen strategy, not a promise that every visitor will see the same result. Google explains the distinction in About PageSpeed Insights.
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CrUX field data
Chrome User Experience Report (CrUX) data summarizes real-user experience over a collection window. A page may not have enough samples for page-level data; PSI may then show origin-level data, or no field data may be available. Label the granularity in your sheet and do not compare an origin-level value for one URL with page-level values for others as though they had the same scope.
Google’s PSI getting-started guide says CrUX data in the PSI API is planned for discontinuation and recommends the CrUX API or CrUX History API for continued access. The cited guide does not establish a completion date, so check its current migration notice before building a workflow that depends on PSI returning field data. If you query CrUX separately, Chrome’s CrUX API documentation reports a limit of 150 queries per minute per Google Cloud project and says data may lag the current time by about two days. That limit applies to the CrUX API, not PSI’s runPagespeed method.
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Plan for quotas, rate limits, and interruptions
There is no PSI request-per-minute or daily limit established here. Check the current quota for your Google Cloud project and build pacing around the limit that applies to it; do not assume the spreadsheet service’s limits are also PSI limits.
Google Sheets API limits
Google’s Sheets API quota documentation lists 300 read requests per minute per project and 60 per minute per user per project, with equivalent write limits. It recommends exponential backoff after an HTTP 429 response and suggests keeping payloads to 2 MB or less for performance. These are Sheets API limits, not PageSpeed API quotas. Batch writes to reduce spreadsheet calls, and retry rate-limited writes with increasing delays. See Google Sheets API usage limits.
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Apps Script limits
If you run the workflow in Apps Script, its URL Fetch, execution-time, and other quotas are separate again and depend on account type. Google says exceeding a quota can stop a script; quotas reset 24 hours after the first request, and published limits can change. Check the live Apps Script quotas page for the account you plan to use before estimating how many URLs a run can handle. Save progress so a stopped execution can resume without repeating completed work.
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Make the run resumable
- Track each input URL’s status, such as pending, complete, or failed.
- Store request settings and the timestamp with each result.
- Use bounded retries for transient errors and rate limits; retain the last error when retries are exhausted.
- Write successful rows in batches and checkpoint progress so a timeout does not require starting from the first URL.
- Review failed rows separately and rerun only those that need another attempt.
Keep results interpretable as PSI evolves
The API is versioned as v5, but Lighthouse has its own release cycle. Google’s release notes state: “As of Oct 20, 2025, PageSpeed Insights and the API have been updated to Lighthouse 13.0.” The same release notes warn that some version updates can introduce breaking response changes. Include the request date, URL, strategy, categories, and returned Lighthouse version when available; avoid depending on one assumed response shape as the only success path. See Google’s PageSpeed Insights release notes.
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Scores and field-data coverage are not guaranteed to remain identical between runs. A spreadsheet with dates, settings, result status, and field-data scope lets you compare measurements without confusing a changed test context with a change to the page itself.
Choose an execution approach that fits your workflow
An Apps Script implementation can keep the automation close to a Google Sheet, while a separate client can offer more control over pacing, retries, and retaining raw responses. Neither approach is inherently best for every batch; choose based on setup, recovery, and maintenance needs.
| Decision factor | Apps Script | Separate client |
|---|---|---|
| Setup and authentication | Convenient when the workflow is centered on Google Sheets; subject to Apps Script account quotas. | Requires its own runtime and Google API authentication setup. |
| Pacing and concurrency | Can control request sequencing, within execution and service quotas. | Can be designed with explicit concurrency and pacing controls. |
| Resume after failure | Needs checkpoints and resumable execution logic. | Can use a persistent queue or checkpoint store, if implemented. |
| Raw JSON and version metadata | Can store selected metadata or responses, subject to script and sheet design. | Can retain raw responses separately and send summarized rows to Sheets. |
| Maintenance | Must account for script quotas and changes to Apps Script or API behavior. | Must maintain the runtime, credentials, and spreadsheet integration. |
For a modest, occasional batch, direct sheet-centered automation may be simpler. For repeated or operationally important runs, prioritize resumability, clear logs, controlled retries, and storage for raw responses—whichever execution approach you choose.
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