
TranscriptAPI.com
Transcriptal
SocialFetch.dev
Download Youtube Transcripts
BulkTranscript.app
TranscriptGenerator.org
YouTube-Transcript.net
Video & web data API for AI: transcripts from YouTube, TikTok, Instagram, plus any page as clean Markdown. Falls back to AI transcription when captions are missing. Built for RAG and agents.

Which is more popular?
Based on our record, Hunter.io seems to be more popular. It has been mentioned 155 times since March 2021.
Website, pricing, platforms and company facts side by side.
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| Website | transcriptfetch.com | hunter.io |
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| Company | Startup from the United States · 1 - 9 employees · 2026 | Startup from France |
| Listed in |
In their own words, as submitted to SaaSHub.


TranscriptFetch is one API for getting text out of video and web content. Send a URL from YouTube, TikTok, Instagram, X or Facebook and get back clean, timestamped text. Send any web page and get clean Markdown. One endpoint, one response shape, one API key. The part that actually matters Most...
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What each product offers, as listed by its team.


Possible disadvantages
Possible disadvantages
An editorial look at what each product does well and who it suits.


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As answered by people managing TranscriptFetch and Hunter.io.
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.
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.
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.
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.
Share your experience with using TranscriptFetch and Hunter.io. For example, how are they different and which one is better?
External articles and on-site reviews we used to compare the two products.


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Hunter, Lusha, and LeadIQ all have free plans. Among paid plans on monthly billing, LeadIQ Pro starts at $20 a month for up to 5 users, and Hunter Starter starts at €49 a month.The cheapest option depends on whether...
Hunter, on the other hand, is built around email discovery, verification, and cold email sequencing. It doesn’t automate LinkedIn actions, but instead helps you find and validate professional email addresses and run...
I tested the waterfall enrichment to understand how it works. Clay pulls the data you need from over 100 sources. Say you want to find your prospect’s valid email address, Clay scans various sources like Hunter,...
Recommendations tracked on public social media and blogs since March 2021.


Tracking TranscriptFetch since Aug 2026.
A few things worth flagging: PDL beats Clearbit's historical rates for US and Western European companies, but drops to ~52% match rate for Japan and South Korea specifically. Apollo underperforms on raw company matching but returns... - Source: dev.to / 6 months ago
The real conclusion I'd push back on from every vendor comparison I've read: there is no single tool that solves reverse lookup at 80%+ accuracy with clean data. The waterfall is the answer. The question is whether you build it yourself... - Source: dev.to / 6 months ago
Last year I ran the same LinkedIn Sales Navigator export through three enrichment APIs. Apollo matched 61% of the emails. Hunter.io matched 54%. An OSINT-first pipeline I'd built in n8n — pulling from public sources before hitting any... - Source: dev.to / 7 months ago
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