TranscriptFetch VS Transcriptum

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.

Transcriptum's answer:

• Privacy-first, EU-hosted with GDPR-aligned data practices.
• Dual YouTube workflow: import original captions or re-transcribe with AI; side-by-side comparison.
• Word-level timestamps, click-to-seek, and speaker diarization for precise navigation.
• Built-in AI assistant for summaries, highlights, action items, and Q&A.
• Segment-preserving DeepL translations that keep speakers and structure.
• Flexible exports: DOCX/PDF, SRT, CSV or copy to clipboard.
• Frictionless input: drag-and-drop or cloud links (Drive, Dropbox, OneDrive, iCloud, WeTransfer).

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.

Transcriptum's answer:

• Compliance & hosting: EU infrastructure, granular access controls, clear data-retention options.
• Accuracy & control: Choose YouTube captions or fresh AI transcription.
• Editing speed: Click any word to jump; fast speaker renaming/mapping.
• Actionable insights: AI summaries, entities, and to-dos—not just raw text.
• End-to-end workflow: From recording to subtitles/exports without extra tools.

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.

Transcriptum's answer:

• Journalists, podcasters, and video creators.
• Researchers and students.
• Product, UX, and marketing teams.
• Companies documenting meetings, interviews, and trainings.

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.

Transcriptum's answer:

• Started to solve a practical pain: turning long recordings into clear, usable text and insights—without privacy trade-offs.
• Built in Europe by Daniel Miksch, focused on real-world workflows (interviews, lectures, meetings).
• Ships time-savers: accurate transcripts, fast navigation, AI summaries, and compliant exports—EU-hosted from day one.

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.

Transcriptum's answer:

• ASR & AI: AssemblyAI / LeMUR, integrated AI assistant
• Translation: DeepL API
• Payments & auth: Stripe, OAuth/Social logins
• Infrastructure: EU-hosted services, HTTPS/TLS, object storage for media