ChamsPilot · Free Tools
Free YouTube fake subscriber checker with AI deep analysis. Detect bots, fake accounts, view manipulation and inflated metrics on any channel · instant, no signup.
| Subscribers | Healthy avg views ÷ subs | Healthy like ratio | Red flag |
|---|---|---|---|
| 1K – 10K | 20% – 100%+ | 4% – 8% of views | views under 5% of subs |
| 10K – 100K | 10% – 50% | 3% – 6% of views | views under 3% of subs |
| 100K – 1M | 5% – 30% | 2.5% – 5% of views | views under 2% of subs |
| 1M+ | 3% – 20% | 2% – 4% of views | views under 1% of subs |
A channel with 500K subscribers averaging 4K views per video is the classic bought-subscriber pattern · subscribers who never watch don't exist.
Follower counts lie. A YouTube account can look huge and still be mostly bots, inactive ghosts and bought engagement. Our free checker runs the same AI forensic engine agencies use to vet creators before a brand deal · so you see the real audience, not the vanity number.
Fake and purchased followers, bot-like engagement, inactive ghost accounts, sudden follower spikes, comment pods and mismatched audience geography on any public YouTube profile.
Instead of a single ratio, our AI weighs 40+ signals · follower quality, engagement velocity, comment timing and sentiment, and coordinated bot-ring patterns · to score how authentic a YouTube audience really is.
Brands pay for reach that converts. Inflated YouTube numbers tank campaign ROI, and one bad audit can kill a partnership. Checking first protects your rate and your reputation.
We benchmark every YouTube account against tier-adjusted norms for its size, so a 10K creator is judged fairly against other 10K creators · not against megastars. That is why our verdict is accurate, not alarmist.
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4.8 / 5 · 1,247 ratings
These ratings cover this tool and the others built on the same engine. They come from people we have worked with and from visitors rating here. How we collect them
We analyze subscriber-to-view ratio, video engagement rates, comment authenticity, view velocity patterns, and channel growth to detect fake subscribers.
Yes! Basic analysis is free. For AI-powered deep analysis with comment forensics and risk scoring, upgrade to Pro.
Subscriber count, average views per video, engagement rate, comment-to-view ratio, like-to-view ratio, video upload frequency, and subscriber growth patterns.
We can still analyze engagement patterns from public video data, but hidden subscriber counts limit the depth of our analysis.
Compare average views to subscriber count: healthy channels get views equal to 5–50% of their subs. A channel with hundreds of thousands of subscribers but a few thousand views per video almost certainly bought subs. Comment volume, like ratios and growth spikes complete the picture · our checker scores all of them.
Yes. YouTube continuously audits accounts and removes spam subscribers, which is why bought subs visibly drain away over weeks. Channels caught buying engagement also risk demonetization and removal from the YouTube Partner Program.
Directly and badly. Fake subs don't watch, so they crater your views-to-subscriber ratio and watch-time signals · the exact metrics the algorithm uses to decide promotion. They also don't count toward the 4,000 valid watch-hours needed for the Partner Program.
Likes at 3–6% of views and comments at 0.3–0.7% of views are healthy for most channels. Ratios far below that on a big-subscriber channel indicate an audience that was never real.