At a glance the headline numbers for Iframe loading
Iframe loading attributes: lazy vs default.
6.2% of iframes are lazy-loaded.
The iframe loading mix who uses what, and how fast each group loads
Iframe loading. On the fleet: 93.8% default, 6.2% lazy. 40.6% of sites use at least one default.
Passing LCP per bucket every category and count level at once - color is the pass rate
Each row is a category, each column its own count bucket (few on the left, many on the right); the cell is the share of those sites passing LCP.
Lazy swings the hardest: 82% of sites pass LCP with few, 70% with many. computed
Few vs many - does quantity cost LCP? the pass rate with few vs many of each category
Per category: the pass rate among pages with FEW of it (hollow ring) against pages with MANY (solid dot), worst trend first. Thin buckets are excluded from the endpoints.
More Lazy costs the most: the LCP pass rate falls from 82% with few to 70% with many. computed
Why this matters for the Core Web Vitals, and where to start fixing it
An iframe is a whole page inside your page: its own HTML, scripts and resources. An embed below the fold that loads eagerly spends bandwidth and CPU on something the visitor may never scroll to, and its work competes with your page at the busiest moment of the load.
loading=lazy on iframes defers all of that until the visitor approaches. For YouTube embeds and maps the better pattern is a facade: a static preview that becomes the real embed on click. The page pays for the embed only when someone wants it.
Chrome field data from 189,915 sites, representing millions of real page loads. How we measured.
Live queries (2) — admin only
SELECT 1 AS ok FROM site_metric_bags LIMIT 0;WITH flat AS (
SELECT b.cat, b.n, b.size, s.crux."phone".lcp AS cwv_val
FROM site_metric_bags b
JOIN sites s ON s.origin = b.origin
WHERE b.path = 'iframes.loading'
AND b.cat IS NOT NULL
),
totals AS (
SELECT cat,
SUM(n) AS fleet_n,
SUM(size) AS fleet_size,
COUNT(*) FILTER (WHERE n IS NOT NULL) AS sample,
COUNT(*) FILTER (WHERE n > 0) AS with_any,
quantile_disc(n, 0.95) AS p95,
COUNT(*) AS n_rows
FROM flat
GROUP BY cat
),
widths AS (
SELECT *,
GREATEST(1, CAST(round(COALESCE(p95, 0)) AS INTEGER)) AS cap,
LEAST(GREATEST(1, CAST(round(COALESCE(p95, 0)) AS INTEGER)), 12 - 1) AS regular_buckets
FROM totals
),
widths2 AS (
SELECT *,
GREATEST(1, CAST(ceil(cap * 1.0 / regular_buckets) AS INTEGER)) AS width
FROM widths
),
binned AS (
SELECT f.cat, f.cwv_val, w.fleet_n, w.fleet_size, w.sample, w.with_any, w.width, w.regular_buckets,
CASE WHEN f.n IS NULL THEN NULL
ELSE LEAST(CAST(FLOOR(f.n * 1.0 / w.width) AS INTEGER), w.regular_buckets)
END AS bucket_idx
FROM flat f
JOIN widths2 w ON f.cat = w.cat
)
SELECT cat, fleet_n, fleet_size, sample, with_any, width, regular_buckets,
bucket_idx,
COUNT(*) AS n,
quantile_disc(cwv_val, 0.5) AS median,
COUNT(*) FILTER (WHERE cwv_val IS NOT NULL) AS total_cwv,
COUNT(*) FILTER (WHERE cwv_val IS NOT NULL AND cwv_val <= 2500) AS good
FROM binned
WHERE bucket_idx IS NOT NULL
GROUP BY cat, fleet_n, fleet_size, sample, with_any, width, regular_buckets, bucket_idx
ORDER BY cat, bucket_idx;