Video data & transcript APIs

An honest look at how FrameFetch compares to the main alternatives for getting data out of social videos. Different tools win for different jobs — here's where each fits. Prices are public list rates; the Supadata, Apify, Veedcrawl, TranscriptAPI, VideoDB, SocialKit, and Captapi rows were checked live 2026-07-18, the rest 2026-07-03 — verify current numbers with each vendor before deciding.

Detailed: vs Supadata · vs Apify · vs Bright Data · vs TranscriptAPI · vs Veedcrawl

Feature & pricing matrix

"—" = not offered / not surfaced in research, not a guaranteed absence. STT engines need you to already have the audio file; they don't ingest a social URL. Bright Data and Apify are general scraping/proxy infrastructure — you build the transcript/frame/OCR layer yourself on top. VideoDB is video infrastructure for footage you already own or stream (Bring Your Own Cloud), not a social-URL scraper — listed here because it surfaces for the same searches, not because it's a direct substitute. Juicer API is deliberately excluded: it's a social-media feed aggregator/embeddable-wall product, with no transcript or frame extraction capability, so it isn't a comparable product for this table.

Which should you use?

• You have a video URL and want transcript + metadata + frames + on-screen text in one call, across platforms, or your AI agent should pay for it itself → FrameFetch.
• You already host the audio and want top transcription accuracy → AssemblyAI / Deepgram.
• You need to scrape large volumes of post metadata cheaply, or need raw proxy bandwidth → Apify / Bright Data — cheaper per unit at scale, but you build the transcript/frame/OCR layer yourself.
• You only ever touch YouTube, at high steady volume → TranscriptAPI — a mature, YouTube-only tool with channel/playlist tooling FrameFetch doesn't have (FrameFetch does have its own keyword video search, added 2026-07-19), and a flat credit rate that can beat per-call pricing at scale.
• You want the closest thing to FrameFetch's multi-platform, MCP-first design → Veedcrawl — overlapping scope, different platform mix (adds X/Facebook, no frames/OCR/translation).

Where FrameFetch is different

• One call → five outputs (metadata, insights, transcript, frames, on-screen text) across six platforms, one schema — instead of stitching a scraper + an STT engine + a frame pipeline + an OCR service.
• Parametric frames (every / every-Nth / 1-per-second / a time range, any size) — for feeding vision models. Not offered by any other tool in this table.
• On-screen text (OCR) per frame — burned-in captions, price tags, signage — with confidence and position. Not offered by any tool in this table.
• Translation and a grounded, quote-verified ask field — not surfaced on any competitor's site in this research round.
• Keyword video search (POST /v1/search, YouTube, flat $0.002/call) pairs with ask so an agent can go from a bare topic to a sourced answer in two calls — see search docs.
• Whisper transcripts on TikTok & Reddit, where native captions are unreliable or absent.
• x402 payment — an autonomous agent pays per call in USDC with no signup. Uncommon in this category.

Honest trade-offs: FrameFetch isn't the cheapest per unit at massive scrape volume or steady YouTube-only volume, and dedicated STT engines may edge out raw transcription accuracy. It optimizes for breadth-in-one-call and agent-native UX. No latency, uptime, or accuracy numbers are compared anywhere on this page — none of these tools, including FrameFetch, have published measured figures we could verify.

Which FrameFetch platforms support what

Pulled live from GET /v1/platforms — the same capability matrix your code can check at request time.

On-screen text (OCR) runs on extracted frames and is available on every platform above — request text_overlay alongside frames. Only YouTube has a reliable public comment source today; every other platform's comments request is omitted with a warning at no charge rather than faking data.