Nov 25, 2022

How to overcome the limitations of the Google Analytics 4 API quotas in Looker Studio (formerly Google Data Studio)

8-MINUTE READ | By Ralph Spandl

Google AnalyticsLooker Studio (Google Data Studio)

[ Updated Nov 28, 2023 ]

Over the past week, you may have heard about “the quota apocalypse” or the “GA4 connector debacle” that a Google Analytics 4 update created for those reporting on GA4 properties in Looker Studio (formerly Google Data Studio).

Remember to check our GA4 Knowledge Center for tutorial videos, support articles, and blog posts.

Tweet from Mikko Piippo on GA4
Source: Mikko Piippo

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What happened with the GA4 API quotas? 

In a nutshell, Google changed its API limits for concurrent requests or “quotas”.

Looker Studio announced the quota change on November 10, 2022.

What initially appeared to be a significant but temporary glitch in Looker Studio instead turned out to be the result of changes from Google Analytics 4, which subsequently impacted almost every user and took the industry by complete surprise.

This critical change means Looker Studio editors using the native GA4 connector can’t visualize their data. Often, the issue seems intermittent, and charts only display an error message:

Looker studio cannot connect to your data set. Exhausted concurrent request quota. Please send fewer requests concurrently. For example, wait for requests to finish before sending more. Click here to learn more about request quotas or contact Google Analytics Support for more information.”

Looker Studio displays the error message once the quotas for a specific property are reached. Once this happens, Google Analytics doesn’t return any data to Looker Studio.

Data Set Configuration Error
Looker Studio cannot connect to your data set.
Exhausted concurrent request quota. Please send fewer requests concurrently. For example, wait for requests to finish before sending more. Click here to learn more about request quotas or contact Google Analytics Support for more information.
Error ID: 26f3bca3

What are GA4 API quotas?

GA4 API quotas fit into three request categories: Core, Realtime, and Funnel. “API requests to Core methods charge Core quotas. API Requests to Realtime methods charge Realtime quotas. One request will not consume both Core & Realtime quotas.” The quotas are used to ensure fairness and parity amongst Google customers.

Tweet from Brian Stark on the new GA4 API quotas
Source: Brian Stark

The official Google Analytics documentation sheds a little light on these quotas. The page features a long list of numbers, likely to hold little meaning to most Looker Studio users.

Two quotas from this long list that have the highest impact on Looker Studio reports were concurrent requests and hourly tokens.

Concurrent requests are easier to understand—the more viewers access your reports simultaneously, the faster you hit the quota.

The second factor is the number of visualizations you use in your reports, as well as the complexity of the data you’re consuming. Filters, large amounts of data, and frequent interaction with your report all count against your quotas.

Where do you go from here?

Currently, there hasn’t been any official recognition by Google that this is a business-critical issue. They have provided steps to help mitigate the issue, such as:

  • Reduce traffic to the report—share the report with fewer people
  • Reduce the number of charts on each page

But, these solutions may affect Looker Studio users, especially those who have quite complex reporting needs. For example, marketing agencies may find it hard to limit traffic to a report or use fewer charts on each page—especially during peak season—since they need to share campaign results with their clients.

So as we know that this is indeed business critical, we’ll now go through the exact steps on how to overcome these limitations.

How to overcome the GA4 API limitations with Supermetrics

Looker Studio users reporting with the Supermetrics GA4 connector have been significantly less affected by this change.

At Supermetrics, we’re used to all kinds of API restrictions enforced by many of the services we connect to. Limiting simultaneous requests and caching are just two best practices we put in place to ensure flawless communication between Looker Studio and the API.

Here are some ways to minimize these issues:

  1. Limit simultaneous requests
  2. Caching
  3. Automatic retries

1. Limit simultaneous requests

Supermetrics handles requests in batches, purposely delaying some requests to avoid too many simultaneous requests. In practice, if your query uses fifteen concurrent requests, Supermetrics first runs ten queries, then the next five. This may take a bit longer, but at least the query will go through.

2. Caching

Supermetrics caches some data to avoid unnecessary calls. This means when you refresh a report with multiple queries, Supermetrics only fetches data for the ones with updated parameters.

3. Automatic retries

Despite the logic we have in place to avoid going over the quotas, sometimes we do see quota errors. In these cases, our system will automatically wait a moment, rerun the request, and do this a few times if necessary. So even in these cases, the only impact the user normally sees is that the report runs a bit slower.

The easiest ways to work around the GA4 API quota limitations

Many Looker Studio users use a few different ways to get their data into Looker Studio without directly hitting the API. Your options are:

Some of these alternatives pull your Google Analytics 4 data into a storage container with nearly no quota limits. The data is updated once a day or maybe even more frequently.

Each of these options has advantages and disadvantages. You may not be ready for BigQuery or find the Extract Data connector too limiting. You should analyze your reporting needs before switching to one of these alternatives.

Partner connectors in Looker Studio

Although the quotas have been enforced for the native Google connector, partner connectors have generally not been affected. So the easiest way to keep the connection to your GA4 data in Looker Studio is to simply use a partner connector.

Looker Studio Google Connectors view

Google’s Extract Data connector

This is a free option to use. The data extractor allows you to choose specific metrics and dimensions for your report. You basically take a snapshot of your data. After that, you can schedule the update daily, weekly, or monthly.

A real upside to the connector is that it greatly speeds up the reporting in Looker Studio. Your data loads faster and responds to filters and other changes more quickly. On the other hand, you can’t edit the fields once you’ve selected these in the report. You’re also limited by 100MB storage which may serve you for a long time but probably not forever.

You can also use Google’s Data Extract connector with Supermetrics. Link it with your existing data sources, choose the metrics you want to report, and extract the data. This way, your Looker Studio report uses the stored data rather than live, and it makes your reports a lot faster.

Google Sheets and the Supermetrics connectors

You can also use Google Sheets as data storage using Supermetrics for Google Sheets. From there, feed your data from Google Sheets to Looker Studio for visualizing and reporting.

But, you’re also limited to 1 million rows. That’s why if you’re dealing with a lot of data, it’s best to use a data warehouse like BigQuery.


With recent changes in the industry, the most stable and long-term solution to secure your data is using a data warehouse. If you already use Looker Studio for reporting, consider using BigQuery. Storing data in a data warehouse helps you get rid of data silos, have more analytics capabilities, take ownership of your data, and lower maintenance needs.

Get rid of data silos

It’s impossible to make decisions with scattered and siloed data. If you spend too much time manually collecting data from different sources, you have little to analyze the results and optimize your campaigns. Instead, you can consolidate all your marketing data into your data warehouse to create a single source of truth.

Have more analytics capabilities

A data warehouse can process much more data than a spreadsheet. Additionally, you have more flexibility to play with your data, whether you want to aggregate, join, or feed your data into a BI tool.

Take full ownership of historical data

Advertising platforms keep changing how long they’ll keep your data. Instead of depending on their data retention policies, you should save all your data in a data warehouse. This way, you have a better chance to do historical analysis.

API limitations

Lower maintenance 

You don’t have to worry about maintenance with a cloud-based data solution like BigQuery. The provider will do it for you. Your team can focus on managing and analyzing your data. Need help moving your marketing data into BigQuery? Try Supermetrics for BigQuery!

We hope this post gives you some options to solve the GA4 API limitations. Read also our article about moving your data into Looker-studio in post-GA4 API quota world. And if you’re looking for an easy and stable way to get data into Looker Studio, start a free 14-day Supermetrics trial.

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About the author

Ralph, Head of Data Visualization at Supermetrics, works at implementing the first commercial Looker Studio chart library—a collection of data visualizations that allow you to push the limits of Looker Studio.

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