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What are Google Analytics 4 API Tokens?
Google uses API tokens generated by Google Cloud Platform (GCP) to authenticate Google Analytics 4 requests, allowing users to query and retrieve data.
Developers use these API tokens to read and write data for applications like Looker Studio (previously Data Studio) and the platform’s Connectors. Google bills developers and enterprise clients based on their token usage.
How do API tokens work with Looker Studio?
Looker Studio uses API tokens to connect with Google Analytics 4 and make requests for custom visualisations and dashboards. Google calculates token usage based on a request’s complexity to execute.
Until 10 November 2022, Looker Studio users never had to worry about tokens because there were no quotas—most users were probably oblivious to their existence.
On 10 November 2022, Google released an update titled: Google Analytics Data API (GA4) concurrent request quotas enforced for Looker Studio reports. Exceeding this quota would present an error message: Exhausted concurrent requests quota.
Google Analytics Reporting API V4 has several quotas, but concurrent requests per view is the main culprit for these errors.
A second GA4 API quota limit called Core Tokens Per Project Per Property Per Hour will produce the same error. This quota is 1,250 per hour. This quota might not be an issue for small teams, but companies with multiple team members and stakeholders accessing reports will easily hit this threshold.
Understanding concurrent requests per view
A view is a report or dashboard containing multiple widgets or charts. These widgets must make one or more requests to your GA4 account to load the appropriate data. If you apply a filter, the report sends another batch of requests, adding to your concurrent and hourly quotas.
Google defines these as concurrent requests because they happen simultaneously. A single view can only make 10 of these concurrent requests at a time.
The problem with this quota is that most reports contain several widgets which collectively exceed ten requests. Additionally, multiple users accessing the same report will trigger this quota—hence the Exhausted concurrent requests quota error message.
Google uses these quotas to manage load handling, ensuring all users receive a good experience with stable performance.
How to fix Exhausted Concurrent Requests Quota Error in Looker Studio
Firstly, as per Google’s documentation: “The 10,000 requests per view (profile) per day or the 10 concurrent requests per view (profile) cannot be increased”—including purchasing additional requests.
There are several workarounds to fix the quota error. Here are some recommendations.
Hire data analytics experts
Data analytic tools like Google Analytics and Looker Studio have evolved into powerful tools with sophisticated features to make data-driven decisions. While this is a positive step forward, it comes with extra layers of complexity.
Many of the solutions we have found to Google’s quota thresholds are either ridiculously expensive or require some expertise to implement correctly. We recommend speaking to one of our data analytics experts about designing and implementing a bespoke solution for your business.
Metric Labs can save you time and money by implementing the right solution to Looker Studio’s quota problem, and our experts can help identify new business opportunities hidden in your data. Contact Metric Labs to help navigate your data analytics challenges.
Upgrade to Google Analytics 360
The easiest solution is to upgrade to Google Analytics 360—Google’s premium GA package. The problem? Prices range from $50,000 to $150,000 per annum.
For most small businesses, Google Analytics 360 is simply unaffordable and not a viable solution for the quota error.
Use a partner connector
Quotas only apply to your Looker Studio’s native connection to GA4, meaning you can use a Partner Connector to circumvent thresholds. This solution is far more affordable than Google Analytics 360, with plans starting at around $99 per month.
Here are some partner connectors to consider:
Make sure you contact these partners before signing up to verify which plan you need to meet your unique business and analytics requirements.
Extract Data Connector
Google’s Extract Data Connector is another quick fix to the quota limit. The extractor sends data to a server that your reports use as a source instead of the GA4 API.
Sadly, there are quota limits here too. Google has a 100MB limit of data storage for extracted data. The Extract Data Connector also has refresh limits, with the most frequent refresh rate being daily—which might be a solution for most stakeholder daily, weekly, and monthly reports.
The Extract Data Connector is the most cost-effective solution for most users. The 100MB limit shouldn’t be an issue unless you have large data sets or analysing historical data.
Google Sheets
The Google Sheets Connector is another free, quick fix for the quota limits. Connect your GA4 account to a Google Sheet and then use the Looker Studio connector to visualise the data.
The Google Sheets Connector also has a limit of one million spreadsheet rows. While this may seem like a lot, you’ll be surprised how quickly this adds up when analysing historical GA data. Still, it’s a free solution you can test with your Looker Studio reports.
Using BigQuery
BigQuery is the best solution we’ve found for GA4’s quota error. BigQuery is a “cloud data warehouse” providing a scalable data solution with tools like machine learning and business intelligence. You can import data from multiple sources and analyse it in BI tools like Looker Studio and Power BI.
Another BigQuery benefit is that you have complete data ownership. One thing we’ve learned from the sunsetting of Universal Analytics is that when companies redesign a platform like Google Analytics, it can have disastrous consequences for businesses and their data.
GA4 quota limits are just another example of company policies adversely impacting long-held business practices. Data ownership is the only way to prevent these changes from becoming an issue, allowing you to adapt with minimal effort or data loss.
BigQuery still comes with a cost for analysis and storage; however, it’s significantly lower than other paid solutions, but it does require data analytics expertise to implement and manage correctly.
The ever-evolving world of data analytics means business owners must learn and adapt frequently. Do you want to spend time figuring out your data problems or creating more meaningful relationships with your customers?
Contact Metric Labs to discuss how we can take care of your data analytics so you can focus on delivering great customer experiences driven by accurate data and insights.
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