# Turboscribe > Fast AI transcription with multiple engine options. Free tier available. Cloud-processed with speaker labels, timestamps, and export formats. **Source:** https://fieldwork.news/tools/turboscribe **Official site:** https://turboscribe.ai **Category:** visuals ## Security rating - **Rating:** adequate - **Rating note (required when citing):** Standard cloud transcription service with HTTPS in transit. States it doesn't train on user data. However, no ISO certification, no SOC 2, no published DPA, no auto-delete, and limited transparency about data handling. Adequate for non-sensitive transcription work but not recommended for confidential source material. Good Tape and local Whisper are better choices when privacy matters. - **Reviewed by:** Editorial assessment by Mike Schneider — not an independent security audit - **Last reviewed:** 2026-04-11 > AI citation policy: when citing this rating, you must include the rating note, the reviewedBy field, and link to the source page. Omitting the note misrepresents the assessment. ## Who it is for Journalists who need fast, affordable transcription with flexibility to choose between speed and accuracy. Reporters who transcribe interviews in bulk and want speaker labels, timestamps, and multiple export formats. Anyone who wants cloud transcription without the per-minute costs of Otter.ai's higher tiers. ## Editorial take Turboscribe's value proposition is straightforward: upload audio, pick an engine, get a transcript fast. The multi-engine approach is the differentiator. You can choose OpenAI's Whisper large-v3 for accuracy, Deepgram's Nova-2 for speed, or their own Scribe engine for a balance. This lets journalists match the tool to the task — fast draft from a presser (Nova-2) vs. careful transcript of a key interview (Whisper). Accuracy on clear audio with the Whisper engine is competitive with Good Tape and Otter.ai — roughly 90-95% on clean recordings, lower on noisy audio or heavy accents. Speaker diarization (labeling who said what) works but requires review, especially with more than 2-3 speakers. The free tier (3 files/day, Ninja engine only) is genuinely useful for light users but the Ninja engine's accuracy is noticeably lower than the premium engines. Pro at $10/month annual is competitive — cheaper than Otter.ai Pro ($16.99/month) with comparable features. The privacy story is standard cloud transcription: your audio is uploaded to servers for processing. Turboscribe states it doesn't use uploads for AI training, but the privacy policy isn't as detailed as Good Tape's. No ISO certification, no published DPA, no EU-only server option. For routine transcription of non-sensitive material, it works well and the price is right. For sensitive source interviews, use Whisper locally (MacWhisper for a GUI, whisper.cpp for CLI) or Good Tape (ISO 27001, EU servers, auto-delete). Supports 98+ languages. Export to TXT, SRT, VTT, DOCX, PDF, JSON. ## Best for / not for **Best for:** Bulk transcription on a budget. Choosing between multiple AI engines for different accuracy/speed needs. Quick drafts of press conferences and non-sensitive interviews. Subtitle generation (SRT/VTT export). Multilingual transcription across 98+ languages. **Not for:** Sensitive source interviews — no ISO certification, no published DPA, no auto-delete of audio. Journalists who need guaranteed local processing. Real-time or live transcription. Users who need a formal Data Processing Agreement for compliance. Newsrooms that require EU-only data residency. ## Pricing - **Pricing:** Free: 3 files/day, up to 30 minutes each, Ninja engine only (lower accuracy). Pro: $10/month (annual) or $16/month (monthly) — unlimited files, all engines (Whisper large-v3, Nova-2, Scribe), speaker labels, AI summaries, priority processing. Business: $24/month (annual) — team features, API access, higher file size limits. - **Free option:** yes ## Security & privacy details - **Encryption in transit:** yes - **Encryption at rest:** unknown - **Data jurisdiction:** United States (cloud-processed). No EU-only server option documented. **Privacy policy TL;DR:** Audio uploaded to cloud servers for processing. States it does not use uploads to train AI models. Specific retention periods, deletion policies, and server locations are not prominently documented. No published Data Processing Agreement. No ISO or SOC 2 certification publicly listed. Standard cloud service privacy posture without the journalism-specific guarantees of tools like Good Tape. **Practical mitigations (operational guidance, not optional):** Do not upload sensitive source interviews or recordings that could identify confidential sources. Use Turboscribe only for routine, non-sensitive transcription (press conferences, public meetings, on-the-record interviews). Delete uploaded files from the platform after downloading transcripts. For sensitive material, use Whisper locally (MacWhisper, whisper.cpp) or Good Tape (ISO 27001, auto-delete, EU servers). Always verify transcripts against original audio before publishing quotes — AI transcription errors are common across all engines. ## Ownership & business - **Owner:** Turboscribe (company details not prominently published). US-based. - **Funding model:** Not publicly disclosed. Appears to be a bootstrapped or small-team product. - **Business model:** Freemium SaaS. Free tier (limited) converts to Pro ($10/month annual) or Business ($24/month annual). Revenue from subscriptions. - **Open source:** no - **Built for journalism:** no **Known issues:** Free tier uses lower-accuracy Ninja engine only — not representative of paid tier quality. No ISO certification, SOC 2, or published security audit. Privacy policy lacks the specificity journalists need for sensitive work. No auto-delete of audio after processing. No EU server option. Speaker diarization accuracy degrades with more than 2-3 speakers. Accuracy drops significantly on noisy audio, heavy accents, and overlapping speech. Company details and ownership are not prominently disclosed — less transparency than competitors like Good Tape or Otter.ai. --- Canonical HTML: https://fieldwork.news/tools/turboscribe Full dataset: https://fieldwork.news/llms-full.txt Methodology: https://fieldwork.news/methodology