A public post checked crawl dates, found many keywords more than 42 days old, and called that mediocre. The decision under that reaction is whether a stored keyword row is still fresh enough to use.
A crawl date is the date on a keyword row for when that row was last crawled. Lag is the gap between that date and today. Stale means the gap is wide enough that the stored numbers may no longer describe the current results. Fresh means the gap is short enough to act on. Years of rankings, traffic, and links are a separate record, historical keyword data. A crawl date only says how long ago this row was refreshed.
What Lag Means for a Stored Row
A keyword database is a pre-computed store. Volume APIs return historical metrics from it: search volume, cost per click (what advertisers pay for a click), and difficulty (how hard the term is to rank for). Google autocomplete, the predictions under the search box, surfaces live long-tail phrasing and does not attach those metrics.
Volume figures in those stored rows trace to Google Keyword Planner, the keyword tool inside Google Ads. cloro.dev's keyword-research API guide, citing Google's Ads API documentation, says Planner reports average monthly searches over the past twelve months. The same guide, citing Google's Keyword Planner docs, says the figure is smoothed and rounded, and that the metrics are updated monthly from the last month's search volume. A row crawled yesterday still carries that twelve-month average. The crawl date tells you the age of the row. This week's demand is a separate question. When the planning average itself is the input you want, average monthly searches are the official form of that window.
The monthly refresh matters because the results page moves inside the month. cloro.dev's keyword-research API guide tracked 200 keywords for 30 days across 5,755 consecutive-day pairs: the top organic result changed on 31.7% of days, the most volatile keyword changed on 93% of days, and stable terms barely moved. The guide reads the 31.7% figure as a page rearranged on roughly one day in three. Across a 42-day crawl date, 31.7% of 42 is 13.3, about 13 days with a new top result for a keyword moving at that average. The volatile keyword was changing on most days, so the same gap weighs more there than on a term as calm as the stable ones in the test.
A zero-volume label on a newly trending phrase, or on a very specific long-tail query, can appear while autocomplete still surfaces the query. Those are phrases a database still labels zero: live phrasing ahead of the stored count. The twelve-month count still comes from the volume row. Read the crawl date when you are deciding whether that zero is lag.
Where the Crawl Date Lives and What Proves It
The 42-day dates were crawl dates on the keywords themselves, read in DataForSEO. That date belongs to the row.
DataForSEO's API v3 docs define GET /v3/dataforseo_labs/status as the call that returns Google Ads status. That endpoint reports when the Google, Bing and Amazon keyword and SERP databases were last refreshed. SERP here means the search results page. The crawl date sits on the row. The status call dates the database refresh. Read the row's date first, then the status call when you need that database date, then the volume window. A twelve-month average stays a planning figure on the day the database refreshes.
What a 42-Day Crawl Date Looked Like
The public post that called the dates mediocre was written in French. It checked DataForSEO and found crawl dates more than 42 days old on many keywords. The same post quoted a comparison of a site coded with AI, launched shortly before Thursday 24 September 2026, and ranking on page one for "commissariat lille" with a long-tail strategy about public places in France.
The comparison counted keywords for that site on that Thursday:
- Ranxplorer: 5,696
- Semrush: 1,805
- Ahrefs: 25
- Sistrix: 8
- SERanking: 5
- Ubersuggest: 0
It noted that all 5,696 Ranxplorer keywords had positive search volume. Its advice was that a tool specialized for the French market maximizes the chances of fresh, quality data for that market. The comparison's point was that putting the tools side by side is what makes the gap visible.
The counts and the crawl date were doing different jobs in that post. The counts are how far six tools disagreed on one new site. The date is how old the keyword rows that person checked were.
A different public post described OpenSEO set up on their own machine, with $50 put on DataForSEO, their own analytics and Search Console connected, and audit reports they could generate themselves. They called the setup much cheaper than the Semrush API. The only drawback they named was that the data feels slightly lagged.
When to Trust the Database vs Pull Live
Trust the stored row when the question is size and priority across a list:
- You are sizing a head term for a business case, and a twelve-month average is the input you want.
- You are comparing many keywords already in the index, where a database row gives volume and difficulty for each.
- The crawl date is close to today, or the term behaves like the stable keywords in that test, and the status call shows a recent refresh of the Google, Bing and Amazon databases.
Pull live when the question is about today:
- You are checking a fresh long-tail phrase that comes back with zero or thin volume and still appears in autocomplete.
- The crawl date is weeks old on a query that moves the way that test's most volatile keyword did.
- You need the suggestions Google is offering now, or the results page it is showing now.
When the check is today's suggestions or today's results page, qequals returns Google suggestions and live SERP results.
Check the crawl date on the row, then the twelve-month window, and pull live when the decision needs today.
