tld_trends

Tool of DomainKits

gTLD registration trends over time. Analyze historical registration patterns for specific gTLDs — spot hype cycles, compare competing extensions, or check long-term health. Two modes: - action='data' + tld (single TLD): deep-dive into one TLD's trend over time. Use this to answer 'is .ai still hot?' or 'is .net dying?'. - action='compare' + tlds (comma-separated, max 5): side-by-side comparison. Use this to answer 'should I buy the .io or .ai version?' by comparing registration momentum. Best practices: - Focus on ma7 (7-day moving average) slope rather than raw daily numbers — daily counts are noisy, the moving average reveals the real trend direction. ma7 trending up = genuine momentum, flat = stable, declining = cooling off. - Compare ma7 vs ma14 for trend acceleration: ma7 crossing above ma14 = momentum building, ma7 dropping below ma14 = momentum fading. - type='newly' shows registration velocity (new domains per day) — best for detecting hype cycles and short-term momentum shifts. - type='active' shows total installed base — best for market size comparison and long-term health assessment. - When comparing TLDs, note the absolute scale difference — a TLD with 100K active domains showing 500 new/day has very different dynamics than one with 10M active showing 500 new/day. - Mutually exclusive inputs: action='data' requires 'tld' (single). action='compare' requires 'tlds' (comma-separated). Do not mix them. - days must be one of the allowed values: 7, 14, 30, 60, 90, 180. Use 30 for short-term momentum, 90-180 for trend confirmation. - Pairs well with tld_rank for today's snapshot context, and price to check registration costs for trending TLDs.