vidIQ pulls real curves from links
vidIQ AI Coach takes a YouTube link and rebuilds the full retention graph using Studio data. It tags every dip to pacing problems or weak hooks and lists like rates plus sub gains from comparable videos. One recent test showed a 7.1 percent like rate and 52,119 views on a same-age video used for side-by-side comparison.
Creators paste the link and receive moment-by-moment explanations instead of generic advice. The output ranks issues by impact so you know which script section needs the first rewrite.
TubeBuddy stops at titles and tags
TubeBuddy scores keywords, tests thumbnails, and optimizes tags on finished videos. It never touches retention curves or script structure. Users report strong discoverability gains but zero data on where viewers actually leave.
That gap leaves creators guessing why a video with good SEO still drops to 30 percent average view duration. TubeBuddy delivers no numbers on exit points or comparable audience behavior.
Script-first tools act before the camera rolls
Retti_AI trained on 5000 real retention graphs to predict spikes and dips inside a draft script. It suggests fixes in planners and edit reviews so the final cut starts closer to 70 percent retention. OutlierKit focuses on pre-production choices and ranks video ideas by expected curve strength.
Claude plus vidIQ MCP pulls live analytics like 574 likes and plus-14 subs on a comparator video. It then produces severity-tagged reports that label fixes as Critical, High, or Opportunity. One set of prompts targets consistent 68 to 74 percent retention across multiple uploads.
Direct head-to-head results
OutlierKit beat vidIQ in five of seven tested categories when creators measured retention lift before versus after upload. vidIQ script suggestions stayed generic and failed to adapt to specific audience exit points. TubeBuddy added nothing to the retention layer in the same tests.
Price movement shows demand for the script layer. Retti_AI raised its plan from 39 to 49 dollars in seven days as more creators moved pre-production work to tools that read actual graphs.
Practical workflow that combines the three
Run topic ideas through OutlierKit first to pick the one with the strongest predicted curve. Write the script in Retti_AI or Claude and audit against the 70 percent benchmark. After upload, paste the link into vidIQ AI Coach to confirm which moments matched the forecast.
TubeBuddy enters only for title and thumbnail refinement once the retention data is already locked in. This order keeps post-production fixes small because the script already cleared the main drop-off risks.
Creators who follow this sequence report fewer videos buried at 30 percent retention and more pushes toward millions of views from the algorithm.