Gumlet's answer

Gumlet is a complete media solution that simplifies the process of video optimization and image delivery. Using Gumlet helps you reduce costs, automate your media pipeline and deliver stunning videos and images.

TranscriptFetch's answer:

Most short-form video has no caption track to download. TikTok’s auto-captions are opt-in per upload, Instagram never publishes a downloadable track, and many captions on both are burned into the video frames where no parser can read them. TranscriptFetch transcribes the audio when no caption track exists, on the same endpoint, with the same response shape. Your code never branches on which method produced the text. It also covers YouTube, TikTok, Instagram, X and Facebook plus any web page as clean Markdown, so a pipeline spanning several sources is one integration rather than five.

Gumlet's answer

We are on a mission to democratize cutting-edge video compression and encoding technology to make high-quality videos accessible to all. Gumlet stands unmatched in the realm of video and image technology, offering solutions that significantly enhance efficiency, reduce costs, and improve accessibility for content providers.

TranscriptFetch's answer:

Three reasons. Coverage: one API key and one response shape across five video platforms and the open web, instead of stitching together a library per platform. Reliability: requests run through rotating infrastructure, so code that works locally keeps working from a server, which is where most open-source approaches break. Billing that matches reality: one credit per successful response, with failed, blocked and empty results never charged. That last point matters on short-form video, where a meaningful share of any batch is music with no speech in it. There is also an MCP server, so AI agents can fetch transcripts as a tool without a custom integration.

Gumlet's answer

Gumlet's primary audience consists of content creators, digital marketers, course creators, e-comm businesses, media houses, developers, and businesses of all sizes looking to streamline their video and image delivery processes. They value efficiency, scalability, and advanced customization options to enhance user experience and engagement on their platforms

TranscriptFetch's answer:

Developers and technical teams building on video and web content. The common cases are RAG and retrieval pipelines that need video as text, AI agents that need to read a link mid-conversation, content teams repurposing short-form video at scale, and media monitoring and research tools. It is an API first, so the buyer is usually the person writing the integration rather than an end user. The free browser tools exist for one-off transcripts and for evaluating output quality before writing any code.

Gumlet's answer

Gumlet was founded in 2019 in Singapore by Aditya Patadia & Divyesh Patel, successful entrepreneurs who previously built and scaled Turing Analytics, a pioneering AI startup in the visual search space. Aditya, the creator of a popular open-source image resize library in PHP, brings a wealth of expertise to Gumlet. Gumlet was started with the goal of providing a powerful yet user-friendly platform that addresses the complexities of video hosting and media management.

TranscriptFetch's answer:

It started with discovering there is no good way to get the text of a video. YouTube’s official Data API will confirm a caption track exists and then refuse to hand it over, because captions.download requires the video owner’s OAuth token. The popular open-source libraries work until you deploy them, at which point platforms start refusing datacenter IPs. And YouTube is the easy case: TikTok and Instagram publish no caption file at all. Every workaround solved one platform, worked locally, and broke in production. TranscriptFetch is the version that handles the failure cases as first-class behaviour rather than edge cases.

TranscriptFetch's answer:

Next.js with TypeScript and Tailwind on the front end and API layer, Clerk for auth with SHA-256 hashed API keys, Neon Postgres with Drizzle ORM, Redis for caching, and Stripe for billing. The extraction layer is a Python and FastAPI service. Speech-to-text uses Whisper-class models. The MCP server is published in the official Model Context Protocol registry with a DNS-verified namespace.