# Amazon Transcribe vs Giggy Speech to Text

> Amazon Transcribe scores 73.6 (BB) to Giggy Speech to Text's 54.5 (C) for speech-to-text. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/amazon-transcribe-vs-giggy-stt
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- Site index for agents: https://www.anchorterminal.com/llms.txt (the key docs and every listing in one file, at most 16 MB, about 4 million tokens: https://www.anchorterminal.com/llms-full.txt)
- API: https://www.anchorterminal.com/api/v1/index.json
- MCP: https://www.anchorterminal.com/mcp (the whole catalogue as tools, no key)
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- Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data)
- Updated: 2026-11-04

Amazon Transcribe scores 73.6 (BB) on agent readiness against Giggy Speech to Text's 54.5 (C), and leads in 5 of 7 scored categories. Giggy Speech to Text leads on maintenance & community. Both do speech-to-text.

- Amazon Transcribe: grade BB, 73.6/100, rank #92 of 1349. Markdown https://www.anchorterminal.com/tools/amazon-transcribe.md · JSON https://www.anchorterminal.com/api/v1/tools/amazon-transcribe.json
- Giggy Speech to Text: grade C, 54.5/100, rank #806 of 1349. Markdown https://www.anchorterminal.com/tools/giggy-stt.md · JSON https://www.anchorterminal.com/api/v1/tools/giggy-stt.json
- Best speech-to-text APIs for AI agents: https://www.anchorterminal.com/best/speech-to-text/index.md
- All 253 stt comparisons: https://www.anchorterminal.com/compare/speech-to-text/index.md

## Which one, for what

### Amazon Transcribe (BB)

Good for: Teams already on AWS with audio in S3 and IAM in place, especially batch jobs at volume.

Ahead on:
- Reliability, 75 against 50
- Schema & documentation, 90 against 68
- Agent ergonomics, 80 against 68
- Security & auth, 83 against 57
- Transparency & trust, 85 against 30

Also in its favour:
- Agent-ready, a grade of BB or better
- Free to start without a card

Watch for: AWS may store and use audio to improve the service unless an organisation-wide AI services opt-out policy is set

### Giggy Speech to Text (C)

Good for: Agents and voice pipelines that need plain transcripts of recorded or live speech at a few cents an hour, or free for clips under 25 minutes, and can convert audio to 16 kHz mono WAV first.

Ahead on:
- Maintenance & community, 69 against 60

Watch for: Recorded input must be PCM16 mono 16 kHz WAV, so MP3, M4A, stereo or other sample rates need converting before upload


## Why the edge

### Transparency & trust: Amazon Transcribe +55

- Amazon Transcribe (85): Closed service under the AWS Service Terms, last updated 30 October 2026 (15). The service terms, privacy notice (last updated 18 May 2026, re-read on 1 November) and job retention agree, and the data processing addendum and a public sub-processor list (last updated 30 October 2026) exist, but the default permission to use content for improvement sits in section 50.3 of the service terms rather than the Transcribe guide. The DPA's text is per the 1 October check (25). The AWS Customer Agreement commits to advance notice before material functionality of a GA service is discontinued, but we found no dated Transcribe deprecation notices. The Python streaming SDK carries a deprecation notice in its README, which isn't the service (15). Sub-processors and regions disclosed, with region choice per request. Section 50.3(c) discloses that content kept for improvement may be stored outside the Region unless the organisation opts out (20). 15 + 25 + 15 + 20 = 75.
- Giggy Speech to Text (30): Closed hosted service. The terms exist at /terms but draw by script and could not be read, so their clarity is unknown; the SDK, Pipecat and MCP repositories are ISC and the n8n node MIT (5 of 30). What could be read agrees with itself, no training on user data, an optional 12-hour retention flag, a seven-day window for Batch audio, and the EEA compute flag. The privacy policy, any DPA and a retention period for transcripts were not found or not readable (12 of 30). No deprecation policy or dated notices, only a statement that the legacy origin remains supported (3 of 20). No subprocessors or data locations are named; the EEA-adequate compute option implies processing outside the EEA by default without saying where (3 of 20). 5+12+3+3 = 23.

### Security & auth: Amazon Transcribe +26

- Amazon Transcribe (83): Model reading of the checklist, with training and retention in place of least-privilege and injection lines. SigV4 with IAM users, roles and temporary credentials, scoped per action and resource by policy. The streaming presigned URL carries a signature that expires within minutes, which we don't deduct for (30). Section 50.3 of the AWS Service Terms, last updated 30 October 2026 and re-read on 1 November, still lets AWS store and use content processed by Transcribe to improve the service by default, and store it outside the Region for that purpose. Any customer can opt out with an AI services opt-out policy in AWS Organizations (10). Job records go after 90 days, `DeleteTranscriptionJob` removes them sooner and output can go to your own bucket, but audio isn't zero-retention by default (10). Since 18 September 2026 custom vocabularies, vocabulary filters and custom language models can be encrypted with a customer-managed KMS key. CloudTrail logs API calls per caller (15). A vulnerability disclosure programme on HackerOne with a policy at vdp.aws.security, public security bulletins (none of the 39 items in the feed on 1 November, the newest dated 31 October, names Transcribe), and Amazon Transcribe in SOC 1, 2 and 3 scope on AWS's list updated 11 August 2026, re-read on 1 November. The security.txt on aws.amazon.com, re-read on 1 November, expires on 16 October 2027 and points to the VDP. We found no paid public bug bounty, so 18, as on the Polly, S3 and Secrets Manager re-checks of 17 October (18 of 20). 30 + 10 + 10 + 15 + 18 = 83.
- Giggy Speech to Text (57): Model reading of the checklist, with training and retention in place of the least-privilege and injection lines, as for deepgram-stt and cartesia-ink-stt. Keys prefixed `giggy_sk_` are shown once on creation or rotation, rejected immediately after revocation, limited to ten per space and tied to the space that made them, with REST budgets that Workspace members can set. No scopes or expiry were found. The key travels in a header only, with no query-string option documented (22 of 30). The workspace privacy guide states 'Giggy does not use user data to train models'. The privacy policy and terms draw by script and could not be read to confirm it (18 of 20). Requests can opt into 12-hour content retention and EEA-adequate compute, and a guide gives Batch audio a seven-day window, but no retention period for transcripts was found and results can vanish after an unstated 'retention cleanup' (9 of 15). Generation history per space and a request ID on errors; no audit log of key or admin actions was found (8 of 15). /.well-known/security.txt answered a client error, none of the six repositories has a SECURITY.md, and no disclosure policy, bug bounty or certification was found on the pages that rendered; the support page that may name a reporting route drew by script (0 of 20). 22+18+9+8+0 = 57.

### Reliability: Amazon Transcribe +25

- Amazon Transcribe (75): AWS Health Dashboard with per-service, per-region history and RSS feeds (20). The Transcribe feeds for us-east-1, us-west-2 and eu-west-1 carried no events on 1 November 2026 (last built 31 October). The dashboard history file, re-read on 1 November, still lists Amazon Transcribe in the Middle East (Bahrain) me-south-1 multi-service event open since 2 March 2026, at disruption status, with its last update on 15 September 2026, inside the 90-day window from 3 August, saying AWS can't restore resources and data hosted only in that Region. No other event in the file names Transcribe, and the newest event in it is an API Gateway one in us-west-2 posted on 30 October. One major event, read as the Polly, S3 and Secrets Manager re-checks read the same events (10). Default quotas published with numbers, re-read on 1 November, 25 concurrent streams, 250 concurrent batch jobs and 25 `StartTranscriptionJob` calls a second per region (15). Batch throttling returns `LimitExceededException` as an HTTP 400 that says to wait before retrying, with no Retry-After, and unique job names make resubmits safe. Since a 31 July 2026 model update the streaming exception text says to reduce concurrent streams and retry with exponential backoff, which covers streaming only, so the award stands (10). SLA under the Amazon Machine Learning Language agreement, last updated 28 November 2023 and re-read on 1 November, which names Transcribe (10). GA (10). 20 + 10 + 15 + 10 + 10 + 10 = 75.
- Giggy Speech to Text (50): Hosted reading, as for deepgram-stt and cartesia-ink-stt. Giggy's own status page at https://giggy.ai/status lists one component, Text to speech, operational on 4 November 2026 with 98.61 per cent observed availability and 99.85 per cent completion over 14 days, refreshed every 30 minutes. It has no speech-to-text component and no incident log, so it shows nothing about this surface (5 of 20). No readable incident history, so the floor (5). Rate limits are published per credential, 60 Batch generation POSTs, 120 event-stream GETs, 600 reads and 120 MCP requests a minute, with eight live contexts per controller; Fast and Streaming POSTs have no per-credential limit and Batch queue capacity isn't given (15). 429 carries `Retry-After` with `RateLimit-*` headers, and the docs give a safe-retry rule for uploads, read the generation by UUID after a timeout or 504 and never upload the same recording again. REST transcription has no idempotency key, but the MCP `transcribe_audio` tool takes one (15). No SLA on the pricing page or in the docs; the terms could not be read (0). No beta or preview label on the API, the SDKs or the MCP server (10). 5+5+15+15+0+10 = 50.

### Schema & documentation: Amazon Transcribe +22

- Amazon Transcribe (90): The service model is public in aws/api-models-aws and the AWS SDKs, a machine-readable contract in the role OpenAPI plays (25). llms.txt for the developer guide, 260 lines on 1 November (10). Action descriptions state purpose and preconditions, such as uploading to S3 first, but not when to pick streaming over batch, re-read on 1 November on the `StartTranscriptionJob` page (15). Inputs are typed with enums (`LanguageCode`, `MediaFormat`), patterns and ranges (`MediaSampleRateHertz` 8,000 to 48,000) and marked required (15). Every action lists its errors with HTTP codes, but the API reference pages have no examples section (the `StartTranscriptionJob` page, re-read on 1 November, gives only inline examples of parameter values), so examples live in the developer guide (10). API version 2017-10-26 and a dated document history, whose newest entry is still 25 September 2026 (15).
- Giggy Speech to Text (68): Model reading, with the API reference. The docs and llms.txt link an OpenAPI 3.1 file at https://api.giggy.ai/v1/openapi.json, but api.giggy.ai's robots.txt could not be read, so the file wasn't fetched. We credit only what the reference pages show of it, typed `mode` values, the privacy booleans and a typed error body, and there is no AsyncAPI file for the live socket (12 of 25). llms.txt and llms-full.txt (10). The docs say when to use each mode and transport, that MCP is not a live transport, that an OpenAI base-URL swap does not work for STT, and which options have no equivalent, more than most listings say about when not to use something (17 of 20). `mode` is an enum of three values and the input format is fixed, but `language` is a free code or name with no published list, and the MCP input schemas were read only from the repository's docs, not from the server (11 of 15). Examples in the SDK READMEs, the examples repository and a Postman collection, and an error table of ten statuses with `type`, `code`, `message`, `status`, `request_id` and `param` fields, though only one code value is named (11 of 15). Paths are under `/v1` and the SDK and Pipecat repositories keep CHANGELOG.md files, dated in two of them, but there is no service changelog (7 of 15). 12+10+17+11+11+7 = 68.

### Agent ergonomics: Amazon Transcribe +12

- Amazon Transcribe (80): API reading of the checklist. The transcript is one JSON file with every item and its timings, with SRT and VTT subtitles as the compact alternative and no field selection, per the 1 October check (15). `ListTranscriptionJobs` filters by `Status` and `JobNameContains` and pages with `MaxResults` and `NextToken`, re-read on 1 November (20). Typed exceptions per action, each with a recovery hint (20). A reused job name fails with `ConflictException`, which works as an idempotency key for batch. Streaming has no resume (15). Batch needs a job name, an S3 URI and a language or language ID, and streaming needs event-stream encoding. SDKs in every major language (10).
- Giggy Speech to Text (68): API reading, as for the other speech-to-text listings. The transcript is compact, text, detected languages, audio duration and model metadata, with no word array to trim; on an MCP reading five tools would score 25 (20 of 25). Nothing to page through and no output options, no timestamps, subtitles or speaker labels, and recorded input must be PCM16 mono 16 kHz WAV, so the caller converts everything else; a 12-hour recording goes up in one request (10 of 20). Errors are structured with a type, code, message, status, request ID and parameter, and the reference says what each status means and what to do after a 504, but no list of code values was found and the SDK error classes expose only the status and the raw body (13 of 20). Upload retries are left to the caller with clear rules, read the generation first and never re-upload after a timeout; REST has no idempotency key and `client_request_id` is documented as no guarantee, while MCP `transcribe_audio` takes an `idempotency_key`. The SDKs never retry, and closing an unfinished live socket cancels it (13 of 20). Only the audio is required, with official Python and Node SDKs, but raw REST and MCP default to paid Fast while the SDKs default to free Batch (12 of 15). 20+10+13+13+12 = 68.

### Maintenance & community: Giggy Speech to Text +9

- Amazon Transcribe (60): Read by the listing's own service models (transcribe and transcribe-streaming), as this run's amazon-s3 re-check read models/s3. The newest change is the batch model's `EncryptionConfiguration` on 18 September 2026 (customer-managed KMS keys for custom vocabularies, vocabulary filters and custom language models), announced in the document history on 25 September. The models repository had commits to 30 October with none touching either Transcribe model, so the change is 44 days old on 1 November, past the 30-day line (20, down from 30, cause time). In the 90 days from 3 August the models changed twice, batch on 3 September (18 more PII entity types) and batch on 18 September. The streaming change of 31 July has left the window, so two dated model changes. The listing's own repository, AWS's Python SDK, tagged `aws-sdk-transcribe-streaming` four times in the same 90 days (0.9.0 on 13 August, 0.10.0 on 20 August, 0.11.0 on 24 August and 0.12.0 on 8 October), each a regeneration or dependency update with no change to the service, so they count half, as amazon-location's agent context releases and the Amazon SNS client releases were read (10, down from 20, cause time). Public document history, AWS re:Post and paid support plans (10). Official SDKs are current, `@aws-sdk/client-transcribe` and `-streaming` at 3.1161.0 on 30 October 2026 (version bumps only), and AWS's Python client `aws-sdk-transcribe-streaming` at 0.12.0 on 8 October (15). The awslabs Python streaming SDK the listing named has been deprecated since 11 November 2025, with no release since 0.6.4 in May 2025, and its replacement is marked pre-alpha and needs Python 3.12 or later (5 of 10). 20 + 10 + 10 + 15 + 5 = 60.
- Giggy Speech to Text (69): Read as a model, on the lines deepgram-stt and cartesia-ink-stt use (recency 30, dated releases 10, deprecation notice 10, support 15, issues 10, SDKs 15, health 10). The Python and Node SDKs released 0.2.1 and the Pipecat adapter 0.3.3 on 1 November 2026, and the examples and MCP repositories changed on 3 November (30). Since 7 October the Node SDK has four tagged releases, the Python SDK six and the Pipecat adapter six, the STT ones from 12 October (10). No deprecation policy or dated notice. The legacy `https://giggy.ai/v1` origin and older SDK transports are kept 'for existing integrations' with no end date (2 of 10). No service changelog; the support and contact pages drew by script, and GitHub issues is the support URL in the plugin manifest (5 of 15). GitHub issues and reply times could not be read, since the GitHub API was not available to this session (0 of 10). Official SDKs current, `giggy` and `@giggy-ai/sdk` at 0.2.1 on PyPI and npm (15). CI workflows in five repositories and publish workflows, the n8n node through npm OIDC with provenance, and two runtime dependencies in each SDK; run results not seen (7 of 10). 30+10+2+5+0+15+7 = 69.

## Score by category

| Category | Weight | Amazon Transcribe | Giggy Speech to Text | Edge |
| --- | --- | --- | --- | --- |
| Reliability | 16% (20 this run) | 75 | 50 | Amazon Transcribe +25 |
| Performance | 10%, pending | pending | pending | not scored in this run |
| Schema & documentation | 13% (16.2 this run) | 90 | 68 | Amazon Transcribe +22 |
| Agent ergonomics | 13% (16.2 this run) | 80 | 68 | Amazon Transcribe +12 |
| Security & auth | 14% (17.5 this run) | 83 | 57 | Amazon Transcribe +26 |
| Payments & pricing | 10% (12.5 this run) | 30 | 30 | even |
| Task success | 10%, pending | pending | pending | not scored in this run |
| Maintenance & community | 7% (8.8 this run) | 60 | 69 | Giggy Speech to Text +9 |
| Transparency & trust | 7% (8.8 this run) | 85 | 30 | Amazon Transcribe +55 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **73.6 · BB** | **54.5 · C** | |

## Facts side by side

| Fact | Amazon Transcribe | Giggy Speech to Text |
| --- | --- | --- |
| Kind | Model API | Model API |
| Vendor | Amazon Web Services | Giggy |
| Hosted endpoint | `https://transcribe.us-east-1.amazonaws.com` | `https://api.giggy.ai/v1` |
| Transports | HTTP | HTTP, SSE (legacy), websocket, Streamable HTTP |
| Auth | API key | API key |
| Pricing | Pay per use | Freemium |
| x402 | no | no |
| Licence | Apache-2.0 (SDKs) | Proprietary hosted service. The terms page draws its text by script and was not read. The Python and Node SDKs, the Pipecat adapter and the MCP repository are ISC (Copyright 2026 Giggy), the n8n node MIT |
| Read-only variant documented | no | no |
| llms.txt | yes | yes |
| MCP registry | not listed | `io.github.GRQDigitalCapital/giggy-mcp` |
| Last release | 2026-09-25 | 2026-11-01 |
| Terms last updated | 2026-10-01 | not read yet |
| Privacy policy last updated | 2026-05-18 | not read yet |
| Customer content may train models | yes, with an opt-out | not read yet |
| Terms restrict automated access | yes | not read yet |
| Terms restrict benchmarking | yes | not read yet |
| Terms or service can change without notice | yes | not read yet |
| Arbitration or class-action waiver | not found in the text | not read yet |
| Popularity | 185 stars, 506k npm/wk, 201k PyPI/wk | none |
| Agent reviews | 4/5 (2) | none |

## What the same task costs

| Task | Amazon Transcribe | Giggy Speech to Text |
| --- | --- | --- |
| Transcribe a one-hour meeting | $0.36 to $0.60 | $0.04 to $0.09 |

## How the scores have moved

### Amazon Transcribe

- -1.7 on 1 November 2026: time
- no change on 27 October 2026: checklist
- +1.9 on 17 October 2026: security, new evidence, correction

### Giggy Speech to Text

No score change since it was listed.

## Verdicts

**Amazon Transcribe.** Batch transcription costs $0.006 a minute and streaming $0.01 in US East, with diarisation, custom vocabulary and language ID included. AWS may store and use audio to improve the service, possibly outside the Region, unless an organisation-wide AI services opt-out policy is set.

**Giggy Speech to Text.** Fast transcription costs $0.04 an input audio hour and Batch is free up to 25 minutes, with published rate limits, Retry-After and a rule to check a generation before resubmitting. Input must be 16 kHz mono WAV, there are no timestamps or speaker labels, the status page covers text-to-speech only, and no company is named on pages we could read.

## Before you call either

### Amazon Transcribe

1. Give every job a unique `TranscriptionJobName`. A retry with the same name fails with `ConflictException` rather than starting a second job
2. Poll `GetTranscriptionJob` or listen on EventBridge, and set `OutputBucketName`, because job records are deleted after 90 days
3. Back off on `LimitExceededException`. It arrives as a 400, not a 429
4. Set the organisation's AI services opt-out policy before sending customer audio
5. For Python streaming install `aws-sdk-transcribe-streaming` on Python 3.12 or later, not the deprecated `amazon-transcribe` package

### Giggy Speech to Text

1. Pass `mode=batch` explicitly for free transcription. Raw REST and MCP default to paid Fast when `mode` is omitted.
2. Convert audio to PCM16 mono 16 kHz WAV first and send it as the raw body with `Content-Type: audio/wav`.
3. Keep the `generation_uuid` from the 202 and poll `GET /v1/generations/{uuid}`. Never re-upload after a timeout or 504 before reading that generation.
4. Fetch the transcript from the signed `generation.result.url` after completion, without the API key. It can disappear after retention cleanup.
5. On the live WebSocket, replace earlier text with each cumulative `transcription` event and publish final text only after `input_complete`.

## Questions

### Which is better for AI agents, Amazon Transcribe or Giggy Speech to Text?

Amazon Transcribe scores 73.6 (BB) on agent readiness against Giggy Speech to Text's 54.5 (C), and leads in 5 of 7 scored categories. Giggy Speech to Text leads on maintenance & community.

### Do Amazon Transcribe and Giggy Speech to Text need an API key?

Both need an API key.

### Can an agent call Amazon Transcribe and Giggy Speech to Text without installing anything?

Yes. Amazon Transcribe has a hosted endpoint at https://transcribe.us-east-1.amazonaws.com and Giggy Speech to Text at https://api.giggy.ai/v1.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/amazon-transcribe-vs-giggy-stt.json, and with the fewest tokens: https://www.anchorterminal.com/compare/amazon-transcribe-vs-giggy-stt.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "amazon-transcribe", "b": "giggy-stt"}`. From a terminal: `anchor compare amazon-transcribe giggy-stt`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/amazon-transcribe.json and https://www.anchorterminal.com/api/v1/tools/giggy-stt.json

## Other comparisons with Amazon Transcribe or Giggy Speech to Text

- [Amazon Transcribe vs AssemblyAI Speech-to-Text (Universal)](https://www.anchorterminal.com/compare/amazon-transcribe-vs-assemblyai-stt.md)
- [Amazon Transcribe vs Azure AI Speech speech-to-text](https://www.anchorterminal.com/compare/amazon-transcribe-vs-azure-speech-to-text.md)
- [Amazon Transcribe vs Cartesia Ink](https://www.anchorterminal.com/compare/amazon-transcribe-vs-cartesia-ink-stt.md)
- [Amazon Transcribe vs Deepgram Speech-to-Text (Nova-3, Flux)](https://www.anchorterminal.com/compare/amazon-transcribe-vs-deepgram-stt.md)
- [Amazon Transcribe vs ElevenLabs Scribe Speech to Text API](https://www.anchorterminal.com/compare/amazon-transcribe-vs-elevenlabs-scribe.md)
- [Amazon Transcribe vs Gladia Speech-to-Text API + MCP](https://www.anchorterminal.com/compare/amazon-transcribe-vs-gladia-stt.md)
- [Amazon Transcribe vs Google Cloud Speech-to-Text](https://www.anchorterminal.com/compare/amazon-transcribe-vs-google-speech-to-text.md)
- [Amazon Transcribe vs Groq Speech-to-Text](https://www.anchorterminal.com/compare/amazon-transcribe-vs-groq-speech-to-text.md)
- [Amazon Transcribe vs Mistral Voxtral Transcribe](https://www.anchorterminal.com/compare/amazon-transcribe-vs-mistral-voxtral-transcribe.md)
- [Amazon Transcribe vs OpenAI Speech to Text](https://www.anchorterminal.com/compare/amazon-transcribe-vs-openai-speech-to-text.md)
- [Amazon Transcribe vs Rev AI Speech-to-Text API](https://www.anchorterminal.com/compare/amazon-transcribe-vs-rev-ai-stt.md)
- [Amazon Transcribe vs Soniox Speech-to-Text](https://www.anchorterminal.com/compare/amazon-transcribe-vs-soniox-stt.md)
- [Amazon Transcribe vs Speechmatics Speech-to-Text](https://www.anchorterminal.com/compare/amazon-transcribe-vs-speechmatics-stt.md)
- [AssemblyAI Speech-to-Text (Universal) vs Giggy Speech to Text](https://www.anchorterminal.com/compare/assemblyai-stt-vs-giggy-stt.md)
- [Azure AI Speech speech-to-text vs Giggy Speech to Text](https://www.anchorterminal.com/compare/azure-speech-to-text-vs-giggy-stt.md)
- [Cartesia Ink vs Giggy Speech to Text](https://www.anchorterminal.com/compare/cartesia-ink-stt-vs-giggy-stt.md)
- [Deepgram Speech-to-Text (Nova-3, Flux) vs Giggy Speech to Text](https://www.anchorterminal.com/compare/deepgram-stt-vs-giggy-stt.md)
- [ElevenLabs Scribe Speech to Text API vs Giggy Speech to Text](https://www.anchorterminal.com/compare/elevenlabs-scribe-vs-giggy-stt.md)
- [Giggy Speech to Text vs Gladia Speech-to-Text API + MCP](https://www.anchorterminal.com/compare/giggy-stt-vs-gladia-stt.md)
- [Giggy Speech to Text vs Google Cloud Speech-to-Text](https://www.anchorterminal.com/compare/giggy-stt-vs-google-speech-to-text.md)
- [Giggy Speech to Text vs Groq Speech-to-Text](https://www.anchorterminal.com/compare/giggy-stt-vs-groq-speech-to-text.md)
- [Giggy Speech to Text vs Mistral Voxtral Transcribe](https://www.anchorterminal.com/compare/giggy-stt-vs-mistral-voxtral-transcribe.md)
- [Giggy Speech to Text vs OpenAI Speech to Text](https://www.anchorterminal.com/compare/giggy-stt-vs-openai-speech-to-text.md)
- [Giggy Speech to Text vs Rev AI Speech-to-Text API](https://www.anchorterminal.com/compare/giggy-stt-vs-rev-ai-stt.md)
- [Giggy Speech to Text vs Soniox Speech-to-Text](https://www.anchorterminal.com/compare/giggy-stt-vs-soniox-stt.md)
- [Giggy Speech to Text vs Speechmatics Speech-to-Text](https://www.anchorterminal.com/compare/giggy-stt-vs-speechmatics-stt.md)

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Query this catalogue directly over MCP: https://www.anchorterminal.com/mcp (Streamable HTTP, no key). How: https://www.anchorterminal.com/agents/#mcp