Google has simply unveiled an alpha model of its Developments API at Google Search Central Dwell, Deep Dive APAC 2025. This new providing brings explore-page knowledge immediately into functions.
The API will present persistently scaled search curiosity figures. These figures align extra predictably than the present web site numbers.
Introduced by Daniel Waisberg and Hadas Jacobi, the Alpha can be opening up from at the moment, and they’re searching for testers who will use the Alpha all through 2025.
The API is not going to embrace Trending Now.
Key Options
Persistently Scaled Search Curiosity
The standout function on this Alpha launch is constant scaling.
In contrast to the online interface, the place search curiosity values shift relying in your question combine, the API returns values that stay steady throughout requests.
These gained’t be full search volumes, however within the pattern response proven, we are able to see an indicative search quantity offered alongside the scaled quantity for comparability within the Google Developments web site interface.
5-12 months Rolling Window
The API surfaces knowledge throughout a five-year rolling window.
Information is accessible as much as 48 hours in the past to protect temporal patterns, akin to annual occasions or weekly cycles.
This longer context helps you distinction at the moment’s search spikes with these of earlier years. It’s perfect for recognizing developments tied to seasonal occasions and recurring information cycles.
Versatile Aggregations And Geographic Breakdown
You select find out how to mixture knowledge: weekly, month-to-month, or yearly.
This flexibility lets you zoom in for fine-grained evaluation or step again for long-term developments.
Regional and sub-regional breakdowns are additionally uncovered by way of the API. You possibly can pinpoint curiosity in international locations, states, and even cities with out additional work.
Pattern API Request & Response
Hadas shared an instance request immediate utilizing Python, in addition to a pattern response.
The request:

The response:

print(time_series)
{
"factors": [
{
"time_range": {
"start_time": (2024-01-01),
"end_time": (2024-01-07),
},
"search_interest": 4400.0,
"scaled_search_interest": 62,
},
{
"time_range": {
"start_time": (2024-01-08),
"end_time": (2024-01-14),
},
"search_interest": 7100.0,
"scaled_search_interest": 100,
},
…
]
}
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Featured Picture: Dan Taylor/SALT.company