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VidChapter is an AI-powered platform designed to generate timestamped chapters, optimized tags, descriptions, and engaging titles for YouTube and Vimeo videos. It aims to elevate viewer experience, boost engagement, and improve SEO. The platform offers tools for video optimization, including AI-driven chapter creation, SEO-powered descriptions, captivating titles, smart tag generation, content translation, and transcription.
To use VidChapter, enter the URL of the YouTube or Vimeo video you want to optimize. The AI will analyze the video and generate chapters, tags, titles, thumbnails, scripts, social media posts, and a description. Finally, copy the AI-generated content into your video description and publish.
VidChapter is an AI-powered platform that generates timestamped chapters, tags, titles, and descriptions for YouTube and Vimeo videos, streamlining the content optimization process.
Simply paste the YouTube or Vimeo video URL, and VidChapter will automatically analyze the video to generate timestamped chapters, tags, titles, and descriptions.
VidChapter’s generated chapters allow viewers to navigate videos easily, rewatch key segments, and skip irrelevant parts, which helps increase viewer engagement and watch time.
Yes. VidChapter’s AI optimizes tags, titles, and descriptions to improve video discoverability and increase your chances of reaching a wider audience.
Yes. VidChapter automates the generation of chapters, tags, titles, and descriptions, saving you time and resources compared to manual creation or outsourcing.
VidChapter is designed for pre-recorded videos. However, you can still use features like tag, title, and description generation for live-streamed videos once they have been recorded and uploaded.
Yes. As a web-based platform, VidChapter works independently of your video editing software. You can generate your timestamps, tags, titles, and descriptions in VidChapter and add them to your videos during your editing workflow.
Each VidChapter API call uses 1 credit. For example, generating video descriptions consumes 1 credit, and generating chapters for a single video also consumes 1 credit. If you use all features—Tags, Titles, Descriptions, and Chapters—for one video, a total of 4 credits will be deducted.
We offer a refund policy if you are not satisfied with your purchase. You may request a full refund within 30 days for annual plans or 15 days for monthly plans. You can also cancel your subscription at any time without obligation.
Yes, VidChapter supports multiple languages, allowing you to generate chapters, tags, titles, and descriptions for videos in various languages.
Yes, VidChapter supports file formats such as SRT, VTT, SBV, and STL, allowing you to generate timestamps from these files.
Yes, VidChapter allows you to edit and customize the generated content to better suit your video’s specific needs and style.
Yes, VidChapter is versatile and works for various video types, including tutorials, vlogs, educational content, interviews, podcasts, and more.
Yes, you can use VidChapter for both new and existing videos. Simply enter the video URL, and the platform will analyze and generate the timestamped chapters.
VidChapter is a WYSIWYG tool that supports content in any language. For dashboard navigation, it supports English, Spanish, French, Portuguese, Mandarin, Italian, Thai, Arabic, Tamil, Telugu, Bengali, Gujarati, Hindi, Icelandic, Japanese, Malayalam, Dutch, Polish, Swedish, and Russian.
Yes. As a cloud-based application, VidChapter works on Mac, PC, tablets, and all mobile devices. It can be accessed from anywhere, and there is no software to download.
Yes, we provide comprehensive guides and step-by-step video tutorials, along with full support if you have questions or need guidance.
Free plan available.
Use these comparison pages to understand the trade-offs between the models most relevant to VidChapter.
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Compare Gemini 2.5 Flash and Gemini 2.0 Flash across pricing, context window, capabilities, benchmarks, and API access to choose the better fit for long-context workloads versus long-context workloads.
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