{ "data": { "a": { "slug": "giggy-stt", "name": "Giggy Speech to Text", "vendor": "Giggy", "vendorUrl": "https://giggy.ai", "kind": "model", "category": "speech-to-text", "summary": "Giggy's hosted speech-to-text API transcribes 16 kHz mono WAV recordings in free Batch, prepaid Fast and Streaming modes, and live audio over a WebSocket, with Python and Node SDKs and a remote MCP server.", "url": "https://www.anchorterminal.com/tools/giggy-stt", "markdownUrl": "https://www.anchorterminal.com/tools/giggy-stt.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/giggy-stt.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/giggy-stt.json", "dns": "giggy-stt.grade.anchorterminal.com", "repo": "https://github.com/giggy-ai/giggy-js", "license": "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", "transports": [ "http", "sse", "websocket", "streamable-http" ], "remoteUrl": "https://api.giggy.ai/v1", "packages": [ { "registry": "pypi", "name": "giggy" }, { "registry": "npm", "name": "@giggy-ai/sdk" }, { "registry": "pypi", "name": "pipecat-giggy" } ], "auth": "api-key", "authNotes": "Self-serve API key prefixed `giggy_sk_`, shown once when created or rotated, rejected immediately after revocation, up to ten active per Personal space or Workspace and tied for good to the space that created it. REST sends it in `xi-api-key`; the WebSocket upgrade and the MCP server take `Authorization: Bearer`. No key scopes or expiry were found, and no query-string option is documented (https://giggy.ai/docs/api-reference).", "pricing": "freemium", "pricingNotes": "Batch transcription is free, queued, for recordings of 0.5 seconds to 25 minutes. Fast costs 4,000 credits ($0.04) and Streaming 8,000 credits ($0.08) per input audio hour on Pro, Studio or Scale credits, rounded up to one credit per request. Credits come in one-time packs from $3 and expire a year after purchase. Personal accounts get free credits topped up to 10,000 a month. Whether a card is needed was not stated on any page read (https://giggy.ai/pricing).", "priceSummary": "Freemium", "where": "hosted", "x402": { "level": "no", "evidence": "No x402, MPP or L402 on the pricing page, the speech API docs, the API reference, llms.txt or llms-full.txt read on 2026-11-04. Credits are bought in prepaid packs; the payment method is not named on any page read.", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-11-04" }, "docsUrl": "https://giggy.ai/docs/speech-api", "llmsTxt": "https://giggy.ai/llms.txt", "openapi": "https://api.giggy.ai/v1/openapi.json", "registryName": "io.github.GRQDigitalCapital/giggy-mcp", "capabilities": [ "speech.stt", "speech.batch", "speech.streaming", "speech.languages" ], "tags": [ "official", "hosted", "model", "closed-source", "streaming", "batch", "async-jobs", "free-tier", "python", "typescript", "openapi", "llms-txt", "mcp", "websocket", "new" ], "lastRelease": "2026-11-01", "graded": true, "anchor": { "graded": true, "score": 54.5, "grade": "C", "agentReady": false, "rank": 903, "ranked": true, "rankOf": 1486, "categoryRank": 17, "methodology": "0.7", "run": "2026-10-01", "scores": { "ergonomics": 68, "maintenance": 69, "payments": 30, "reliability": 50, "schema": 68, "security": 57, "transparency": 30 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-04" }, "negative": 0, "verdict": "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.", "bestFor": "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.", "strengths": [ "Fast is $0.04 and Streaming $0.08 per input audio hour on Pro credits, and Batch is free for recordings of 0.5 seconds to 25 minutes", "Per-credential rate limits are published with numbers, 429 carries `Retry-After`, and `RateLimit-*` headers report what is left", "The docs say to read a generation by UUID after a timeout or 504 instead of uploading again, and the SDKs never retry paid requests", "llms.txt, llms-full.txt and SDK READMEs state what the API does not do, including no diarisation, timestamps or OpenAI-compatible STT", "Official Python and Node SDKs at 0.2.1, a Pipecat adapter with live STT and a remote MCP server, all released within the last four weeks" ], "weaknesses": [ "Recorded input must be PCM16 mono 16 kHz WAV, so MP3, M4A, stereo or other sample rates need converting before upload", "No word timestamps, speaker labels, confidence scores or redaction, per the speech API docs", "The status page has one component, Text to speech, and no incident history, so speech-to-text availability isn't shown", "REST transcription has no idempotency key, and `client_request_id` is documented as not making a retried upload safe", "The terms, privacy policy, support and contact pages draw by script, and no company, address or security contact was found on any page read" ], "agentNotes": [ "Pass `mode=batch` explicitly for free transcription. Raw REST and MCP default to paid Fast when `mode` is omitted.", "Convert audio to PCM16 mono 16 kHz WAV first and send it as the raw body with `Content-Type: audio/wav`.", "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.", "Fetch the transcript from the signed `generation.result.url` after completion, without the API key. It can disappear after retention cleanup.", "On the live WebSocket, replace earlier text with each cumulative `transcription` event and publish final text only after `input_complete`." ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "C", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 54.5 } ], "editorialScores": { "ergonomics": 68, "maintenance": 69, "payments": 30, "reliability": 50, "schema": 68, "security": 57, "transparency": 23 }, "provenanceScore": 36, "checked": { "categories": { "ergonomics": "2026-11-04", "maintenance": "2026-11-04", "payments": "2026-11-04", "reliability": "2026-11-04", "schema": "2026-11-04", "security": "2026-11-04", "transparency": "2026-11-04" }, "last": "2026-11-04", "oldest": "2026-11-04", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "python -m pip install giggy==0.2.1 # or: npm install @giggy-ai/sdk", "http": "curl -X POST \"https://api.giggy.ai/v1/audio/transcriptions?mode=batch\u0026language=en\" \\\n -H \"xi-api-key: $GIGGY_API_KEY\" -H \"Content-Type: audio/wav\" \\\n --data-binary @recording.wav", "config": { "mcpServers": { "giggy-speech": { "headers": { "Authorization": "Bearer ${GIGGY_API_KEY}" }, "type": "http", "url": "https://giggy.ai/mcp" } } } }, "letme": { "capability": "https://letme.dev/speech.stt", "tool": "https://letme.dev/giggy-stt" }, "area": "voice", "unitPrices": [ { "item": "Fast, Pro, Studio or Scale credits", "unit": "audio-minute", "usd": 0.000667, "note": "published as $0.04 per input audio hour, rounded up to one credit per request" }, { "item": "Streaming, Pro, Studio or Scale credits", "unit": "audio-minute", "usd": 0.001333, "note": "published as $0.08 per input audio hour" }, { "item": "Fast, Starter pack", "unit": "audio-minute", "usd": 0.000765, "note": "$3 for 261,300 credits, $0.0459 an hour" }, { "item": "Streaming, Starter pack", "unit": "audio-minute", "usd": 0.00153, "note": "$0.0918 an hour" }, { "item": "Batch", "unit": "audio-minute", "usd": 0, "note": "free, queued, 0.5 seconds to 25 minutes a recording" } ], "provenance": { "legalEntity": "", "domain": "giggy.ai", "domainRegistered": "", "endpointOnVendorDomain": true, "terms": "https://giggy.ai/terms", "privacy": "https://giggy.ai/privacy", "statusPage": "https://giggy.ai/status", "changelog": "", "securityTxt": "none", "checked": "2026-11-04", "notes": [ "unchecked: the legal entity. /terms, /privacy, /acceptable-use, /contact, /docs/support and /docs/privacy-rights returned only their titles and metadata, because their text is drawn by script, and /terms.md answered a client error. The about, pricing, grants and docs pages that did render name only Giggy, with 'Made in California' on the about page. The repositories' licences say 'Copyright 2026 Giggy'.", "The status page tracks one component, Text to speech, with 14-day availability and completion figures refreshed every 30 minutes and no incident history. It has no speech-to-text component.", "No service changelog was found. The SDK and Pipecat repositories carry CHANGELOG.md files, dated in the Python SDK and the Pipecat adapter.", "https://giggy.ai/.well-known/security.txt answered a client error. None of the six public repositories has a SECURITY.md.", "robots.txt on giggy.ai allows / and disallows /api/, /ops/, /app/ and /blog/*.md for `*` and for the named AI agents, with no Content-Signal line. api.giggy.ai's robots.txt could not be read (the fetch needed an approval that wasn't given), so nothing on that host was fetched, the OpenAPI file included.", "unchecked: RDAP for giggy.ai (refused for want of an approval on 4 November 2026)." ], "score": 36 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/giggy-stt.json", "live": { "slug": "giggy-stt", "probe": { "target": "https://api.giggy.ai/v1", "method": "get", "lastAt": "2026-11-07T18:20:26.968324226Z", "lastOk": true, "lastStatus": 404, "lastMs": 361, "authRequired": false, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 251, "p95ms24h": 556, "samples24h": 239, "samples30d": 700, "days": [ { "date": "2026-11-04", "probes": 87, "ok": 87 }, { "date": "2026-11-05", "probes": 185, "ok": 185 }, { "date": "2026-11-06", "probes": 241, "ok": 241 }, { "date": "2026-11-07", "probes": 187, "ok": 187 } ] }, "vendorStatus": { "page": "https://giggy.ai/status", "indicator": "unknown", "summary": "no machine-readable status found", "checkedAt": "2026-11-07T04:47:50.248239505Z" }, "versions": [ { "registry": "github", "name": "giggy-ai/giggy-js", "version": "v0.1.0", "released": "2026-10-07", "seenAt": "2026-11-06T23:28:11.885054385Z" }, { "registry": "mcp-registry", "name": "io.github.GRQDigitalCapital/giggy-mcp", "version": "1.0.2", "seenAt": "2026-11-07T18:16:28.95801333Z" }, { "registry": "npm", "name": "@giggy-ai/sdk", "version": "0.2.1", "seenAt": "2026-11-06T23:28:09.922333865Z" }, { "registry": "pypi", "name": "giggy", "version": "0.2.1", "released": "2026-11-01", "seenAt": "2026-11-06T23:28:09.736179919Z" }, { "registry": "pypi", "name": "pipecat-giggy", "version": "0.3.3", "released": "2026-11-01", "seenAt": "2026-11-06T23:28:10.15808183Z" } ], "githubStars": 0, "npmWeekly": 249, "pypiWeekly": 140, "securityTxt": { "url": "https://giggy.ai/.well-known/security.txt", "state": "none", "checkedAt": "2026-11-07T16:01:25.045857827Z" }, "llmsTxt": { "url": "https://giggy.ai/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-11-07T15:32:35.7230198Z" }, "pages": [ { "url": "https://giggy.ai/pricing", "kind": "pricing", "status": 200, "checkedAt": "2026-11-07T14:10:27.231106636Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "857ac594af2d" }, { "url": "https://giggy.ai/privacy", "kind": "privacy", "status": 200, "checkedAt": "2026-11-07T14:10:29.525027715Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "e3b0c44298fc" }, { "url": "https://giggy.ai/terms", "kind": "terms", "status": 200, "checkedAt": "2026-11-07T14:10:31.471008718Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "e3b0c44298fc" } ], "mcpTools": { "url": "https://giggy.ai/mcp", "checkedAt": "2026-11-07T01:44:48.687478609Z", "status": "auth", "note": "asks for credentials before listing its tools", "changedAt": "2026-11-07T01:44:48.687478609Z" }, "updatedAt": "2026-11-07T18:20:26.968324226Z" } }, "answer": "Giggy Speech to Text and IBM Watson Speech to Text score within a point of each other on agent readiness, 54.5 (C) and 54.5 (C). IBM Watson Speech to Text leads on security \u0026 auth, payments \u0026 pricing and transparency \u0026 trust.", "b": { "slug": "ibm-watson-stt", "name": "IBM Watson Speech to Text", "vendor": "IBM", "vendorUrl": "https://www.ibm.com/products/speech-to-text", "kind": "model", "category": "speech-to-text", "summary": "IBM Watson Speech to Text transcribes audio over synchronous and asynchronous HTTP and a WebSocket interface on IBM Cloud, with large speech models, next-generation models, custom language and acoustic models, speaker labels and language identification.", "url": "https://www.anchorterminal.com/tools/ibm-watson-stt", "markdownUrl": "https://www.anchorterminal.com/tools/ibm-watson-stt.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/ibm-watson-stt.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/ibm-watson-stt.json", "dns": "ibm-watson-stt.grade.anchorterminal.com", "transports": [ "http", "websocket" ], "packages": [ { "registry": "pypi", "name": "ibm-watson" }, { "registry": "npm", "name": "ibm-watson" } ], "auth": "api-key", "authNotes": "An IBM Cloud IAM API key per service instance, exchanged for a bearer token or sent with Basic auth as `apikey:\u003ckey\u003e`. Service credentials carry IAM roles. The WebSocket interface takes the IAM token as an `access_token` query parameter.", "pricing": "freemium", "pricingNotes": "Lite plan free for 500 minutes a month, deleted after 30 days of inactivity. Plus plan $0.02 a minute up to 999,999 minutes a month and $0.01 above, with up to 100 concurrent requests. Premium by quote.", "priceSummary": "Freemium", "where": "local", "x402": { "level": "no", "evidence": "No x402, MPP or L402 in the docs or API reference (checked 2026-11-06). Billing runs through an IBM Cloud account.", "endpoints": [] }, "toolCount": 0, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-11-06" }, "docsUrl": "https://cloud.ibm.com/docs/speech-to-text", "capabilities": [ "speech.stt", "speech.streaming", "speech.batch", "speech.diarisation", "speech.languages" ], "tags": [ "hosted", "freemium", "closed-source", "python", "typescript", "enterprise", "streaming", "batch", "async-jobs", "webhooks" ], "lastRelease": "2026-06-29", "graded": true, "anchor": { "graded": true, "score": 54.5, "grade": "C", "agentReady": false, "rank": 904, "ranked": true, "rankOf": 1486, "categoryRank": 18, "methodology": "0.7", "run": "2026-10-01", "scores": { "ergonomics": 66, "maintenance": 33, "payments": 40, "reliability": 40, "schema": 55, "security": 68, "transparency": 80 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-11-06" }, "negative": 0, "verdict": "Watson STT covers sync, async and WebSocket transcription in ten locations, with custom models, $0.02 a minute on Plus and 500 free minutes. There is no machine-readable spec or readable status history, requests are logged for improvement unless opted out, the WebSocket token goes in the URL, and the last release note is from June 2026.", "bestFor": "Enterprises on IBM Cloud needing custom acoustic models, HIPAA on Premium and many locations.", "strengths": [ "Sync, async with callbacks and WebSocket streaming on one service", "500 free minutes a month on Lite and $0.02 a minute on Plus", "Custom language and acoustic models on paid plans", "Dated deprecation of previous-generation models with over a year of notice", "`DELETE /v1/user_data` removes data by customer ID" ], "weaknesses": [ "No public OpenAPI file or llms.txt", "The WebSocket interface takes the IAM token as a URL query parameter", "Requests are logged by default to improve the service unless the account or request opts out", "The status page is drawn by script and no SLA was read", "No release note since 29 June 2026" ], "agentNotes": [ "Send `X-Watson-Learning-Opt-Out: true` or opt out at account level before sending sensitive audio", "Use the async `/v1/recognitions` interface with a callback for files over 100 MB, up to 1 GB", "Pass a large speech or next-generation model name in `model`, since previous-generation models were removed", "Tag requests with `X-Watson-Metadata: customer_id=...` so the data can be deleted later", "Keep within 100 concurrent requests on Plus across HTTP and WebSocket" ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "C", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 54.5 } ], "editorialScores": { "ergonomics": 66, "maintenance": 33, "payments": 40, "reliability": 40, "schema": 55, "security": 68, "transparency": 65 }, "provenanceScore": 94, "checked": { "categories": { "ergonomics": "2026-11-06", "maintenance": "2026-11-06", "payments": "2026-11-06", "reliability": "2026-11-06", "schema": "2026-11-06", "security": "2026-11-06", "transparency": "2026-11-06" }, "last": "2026-11-06", "oldest": "2026-11-06", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "pip install ibm-watson" }, "letme": { "capability": "https://letme.dev/speech.stt", "tool": "https://letme.dev/ibm-watson-stt" }, "sameCompany": [ "granite-embedding-small-english-r2", "granite-guardian", "granite-switch", "granite-4-2-3b" ], "area": "voice", "unitPrices": [ { "item": "Plus plan, first 999,999 minutes a month", "unit": "audio-minute", "usd": 0.02 }, { "item": "Plus plan, from 1,000,000 minutes a month", "unit": "audio-minute", "usd": 0.01 } ], "provenance": { "legalEntity": "International Business Machines Corporation", "domain": "ibm.com", "domainRegistered": "1986-03-19", "domainNote": "The endpoints are on watson.cloud.ibm.com, under ibm.com.", "endpointOnVendorDomain": true, "terms": "https://cloud.ibm.com/docs/overview?topic=overview-terms", "privacy": "https://www.ibm.com/privacy", "statusPage": "https://cloud.ibm.com/status", "changelog": "https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-release-notes", "securityTxt": "valid", "checked": "2026-11-06", "notes": [ "cloud.ibm.com/robots.txt disallows only /docs/search?q= and sets Content-Signal ai-train=yes, search=yes, ai-input=yes.", "www.ibm.com/.well-known/security.txt names IBM PSIRT, HackerOne and psirt@us.ibm.com and expires on 6 December 2026.", "cloud.ibm.com/status returns a page drawn by script, so its incident history wasn't read.", "The IBM Cloud terms page was found but its text wasn't read, so no clause is quoted.", "RDAP gives ibm.com a registration date of 19 March 1986." ], "score": 94 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/ibm-watson-stt.json", "live": { "slug": "ibm-watson-stt", "vendorStatus": { "page": "https://cloud.ibm.com/status", "indicator": "unknown", "summary": "no machine-readable status found", "checkedAt": "2026-11-07T04:47:55.897466786Z" }, "versions": [ { "registry": "npm", "name": "ibm-watson", "version": "12.2.0", "seenAt": "2026-11-06T23:34:06.712780272Z" }, { "registry": "pypi", "name": "ibm-watson", "version": "11.2.0", "released": "2026-01-26", "seenAt": "2026-11-06T23:34:06.544738844Z" } ], "npmWeekly": 12367, "pypiWeekly": 10209, "securityTxt": { "url": "https://ibm.com/.well-known/security.txt", "state": "valid", "expires": "2026-12-07T00:00:00+00:00", "checkedAt": "2026-11-07T16:00:08.596796331Z" }, "pages": [ { "url": "https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-release-notes", "kind": "changelog", "status": 200, "checkedAt": "2026-11-07T14:01:17.694727718Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "8dcf87213e78" }, { "url": "https://www.ibm.com/privacy", "kind": "privacy", "status": 200, "checkedAt": "2026-11-07T14:30:01.132495816Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "cf480cd7bcca" }, { "url": "https://cloud.ibm.com/docs/overview?topic=overview-terms", "kind": "terms", "status": 200, "checkedAt": "2026-11-07T14:01:13.338724324Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "a0514cb0ed80" } ], "updatedAt": "2026-11-07T16:00:08.596796331Z" } }, "edges": [ { "by": 50, "category": "transparency", "leads": "ibm-watson-stt", "name": "Transparency \u0026 trust", "reasons": { "a": "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.", "b": "Closed service under the IBM Cloud terms (15). The data security page states default logging for improvement with an opt-out, async retention, GDPR deletion by customer ID and plan-level separation, and they agree with each other. The DPA wasn't read (20 of 30). Previous-generation models carried a dated deprecation of 15 March 2022 and removal on 31 July 2023, and release notes say changes are compatible unless noted (18 of 20). Ten locations are named, with sub-processors not read (12 of 20). Total 65." }, "scores": { "a": 30, "b": 80 } }, { "by": 36, "category": "maintenance", "leads": "giggy-stt", "name": "Maintenance \u0026 community", "reasons": { "a": "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.", "b": "Read as a model. The newest release note is 29 June 2026 (`parameter_set` for large speech models), 130 days ago (10). No dated entries in the 90 days from 8 August (0). Release notes, IBM support and a public docs repository last changed 30 June 2026 (8 of 15). Official SDKs, `ibm-watson` 11.2.0 on PyPI (26 January 2026) and 12.2.0 on npm (January 2026) (10 of 15). CI not read (5 of 10). Total 33." }, "scores": { "a": 69, "b": 33 } }, { "by": 13, "category": "schema", "leads": "giggy-stt", "name": "Schema \u0026 documentation", "reasons": { "a": "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.", "b": "No public OpenAPI or other machine-readable definition was found (0). No llms.txt (0). The reference says which parameters each model family supports and the docs cover each feature (15 of 20). Typed parameters with defaults, ranges and enums (13 of 15). curl and SDK examples on every method, and errors with `error`, `code`, `code_description` and `warnings`, with invalid arguments reported as warnings (12 of 15). Versioned `/v1` paths and dated release notes back to 2015 (15). Total 55." }, "scores": { "a": 68, "b": 55 } }, { "by": 11, "category": "security", "leads": "ibm-watson-stt", "name": "Security \u0026 auth", "reasons": { "a": "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.", "b": "Model reading of the checklist, with training and retention in place of the least-privilege and injection lines. IAM API keys per instance with Reader, Writer and Manager roles and short-lived bearer tokens (30). The WebSocket interface takes the token as an `access_token` query parameter, so 10 off (20 of 30). Request and response data are logged by default to improve the service, and any account can opt out per account or per request (10 of 20). Async results expire after a week by default (`results_ttl` shortens it), a delete method exists, and `DELETE /v1/user_data` removes data by customer ID (10 of 15). Activity Tracker events record changes to the service's state (10 of 15). Valid security.txt to 6 December 2026, PSIRT and HackerOne, HIPAA readiness on Premium. Certifications weren't read (18 of 20). Total 68." }, "scores": { "a": 57, "b": 68 } }, { "by": 10, "category": "reliability", "leads": "giggy-stt", "name": "Reliability", "reasons": { "a": "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.", "b": "IBM Cloud has a status page at cloud.ibm.com/status, but it is drawn by script and showed no components or history to our reader, read as gemini-live's script-shell page was (10 of 20, and 5 for no readable history). Plus allows up to 100 concurrent transcription requests across WebSocket and HTTP (15). No 429 or backoff guidance was found in the API reference or the docs repository (0). No SLA was read for the service (0, unchecked). Next-generation and large speech models are GA, language by language (10). 10 + 5 + 15 + 0 + 0 + 10 = 40." }, "scores": { "a": 50, "b": 40 } }, { "by": 10, "category": "payments", "leads": "ibm-watson-stt", "name": "Payments \u0026 pricing", "reasons": { "a": "No x402, MPP or L402 (0). Per-hour prices for Batch, Fast and Streaming and every credit pack published without a login (20). Free Batch transcription and Personal free credits topped up to 10,000 a month, but no page read says whether a card is needed, so half, as other listings read an unstated card requirement (10 of 20). A person signs up in a browser and creates the key there; no programmatic key route was found (0). 0+20+10+0 = 30.", "b": "No x402, MPP or L402 (0). Per-minute Plus prices published in the docs without a login (20). The Lite plan gives 500 free minutes a month, and no page read says a card is needed (20). A person creates an IBM Cloud account and service instance in a browser (0). Total 40." }, "scores": { "a": 30, "b": 40 } } ], "facts": [ { "a": "Model API", "b": "Model API", "name": "Kind" }, { "a": "Giggy", "b": "IBM", "name": "Vendor" }, { "a": "https://api.giggy.ai/v1", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "HTTP, SSE (legacy), websocket, Streamable HTTP", "b": "HTTP, websocket", "name": "Transports" }, { "a": "API key", "b": "API key", "name": "Auth" }, { "a": "Freemium", "b": "Freemium", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "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", "b": "none", "name": "Licence" }, { "a": "none", "b": "0", "name": "Tools exposed" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "yes", "b": "no", "name": "llms.txt" }, { "a": "io.github.GRQDigitalCapital/giggy-mcp", "b": "not listed", "name": "MCP registry" }, { "a": "2026-11-01", "b": "2026-06-29", "name": "Last release" }, { "a": "not read yet", "b": "no date given", "name": "Terms last updated" }, { "a": "not read yet", "b": "2026-08-25", "name": "Privacy policy last updated" }, { "a": "not read yet", "b": "not found in the text", "name": "Customer content may train models" }, { "a": "not read yet", "b": "not found in the text", "name": "Terms restrict automated access" }, { "a": "not read yet", "b": "not found in the text", "name": "Terms restrict benchmarking" }, { "a": "not read yet", "b": "not found in the text", "name": "Terms or service can change without notice" }, { "a": "not read yet", "b": "not found in the text", "name": "Arbitration or class-action waiver" } ], "faq": [ { "answer": "Giggy Speech to Text and IBM Watson Speech to Text score within a point of each other on agent readiness, 54.5 (C) and 54.5 (C). IBM Watson Speech to Text leads on security \u0026 auth, payments \u0026 pricing and transparency \u0026 trust.", "question": "Which is better for AI agents, Giggy Speech to Text or IBM Watson Speech to Text?" }, { "answer": "Both need an API key.", "question": "Do Giggy Speech to Text and IBM Watson Speech to Text need an API key?" }, { "answer": "Giggy Speech to Text has a hosted endpoint at https://api.giggy.ai/v1. No hosted endpoint is listed for IBM Watson Speech to Text.", "question": "Can an agent call Giggy Speech to Text and IBM Watson Speech to Text without installing anything?" } ], "goodFor": [ { "aheadOn": [ "Reliability, 50 against 40", "Schema \u0026 documentation, 68 against 55", "Maintenance \u0026 community, 69 against 33" ], "also": [ "A hosted endpoint, with nothing to install" ], "goodFor": "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.", "slug": "giggy-stt", "watchFor": "Recorded input must be PCM16 mono 16 kHz WAV, so MP3, M4A, stereo or other sample rates need converting before upload" }, { "aheadOn": [ "Security \u0026 auth, 68 against 57", "Payments \u0026 pricing, 40 against 30", "Transparency \u0026 trust, 80 against 30" ], "also": null, "goodFor": "Enterprises on IBM Cloud needing custom acoustic models, HIPAA on Premium and many locations.", "slug": "ibm-watson-stt", "watchFor": "No public OpenAPI file or llms.txt" } ], "history": null, "job": { "capability": "speech.stt", "name": "Speech-to-text" }, "others": [ { "json": "https://www.anchorterminal.com/compare/amazon-transcribe-vs-giggy-stt.json", "title": "Amazon Transcribe vs Giggy Speech to Text", "url": "https://www.anchorterminal.com/compare/amazon-transcribe-vs-giggy-stt" }, { "json": "https://www.anchorterminal.com/compare/amazon-transcribe-vs-ibm-watson-stt.json", "title": "Amazon Transcribe vs IBM Watson Speech to Text", "url": "https://www.anchorterminal.com/compare/amazon-transcribe-vs-ibm-watson-stt" }, { "json": "https://www.anchorterminal.com/compare/assemblyai-stt-vs-giggy-stt.json", "title": "AssemblyAI Speech-to-Text (Universal) vs Giggy Speech to Text", "url": "https://www.anchorterminal.com/compare/assemblyai-stt-vs-giggy-stt" }, { "json": "https://www.anchorterminal.com/compare/assemblyai-stt-vs-ibm-watson-stt.json", "title": "AssemblyAI Speech-to-Text (Universal) vs IBM Watson Speech to Text", "url": "https://www.anchorterminal.com/compare/assemblyai-stt-vs-ibm-watson-stt" }, { "json": "https://www.anchorterminal.com/compare/azure-speech-to-text-vs-giggy-stt.json", "title": "Azure AI Speech speech-to-text vs Giggy Speech to Text", "url": "https://www.anchorterminal.com/compare/azure-speech-to-text-vs-giggy-stt" }, { "json": "https://www.anchorterminal.com/compare/azure-speech-to-text-vs-ibm-watson-stt.json", "title": "Azure AI Speech speech-to-text vs IBM Watson Speech to Text", "url": "https://www.anchorterminal.com/compare/azure-speech-to-text-vs-ibm-watson-stt" }, { "json": "https://www.anchorterminal.com/compare/cartesia-ink-stt-vs-giggy-stt.json", "title": "Cartesia Ink vs Giggy Speech to Text", "url": "https://www.anchorterminal.com/compare/cartesia-ink-stt-vs-giggy-stt" }, { "json": "https://www.anchorterminal.com/compare/cartesia-ink-stt-vs-ibm-watson-stt.json", "title": "Cartesia Ink vs IBM Watson Speech to Text", "url": "https://www.anchorterminal.com/compare/cartesia-ink-stt-vs-ibm-watson-stt" }, { "json": "https://www.anchorterminal.com/compare/deepgram-stt-vs-giggy-stt.json", "title": "Deepgram Speech-to-Text (Nova-3, Flux) vs Giggy Speech to Text", "url": "https://www.anchorterminal.com/compare/deepgram-stt-vs-giggy-stt" }, { "json": "https://www.anchorterminal.com/compare/deepgram-stt-vs-ibm-watson-stt.json", "title": "Deepgram Speech-to-Text (Nova-3, Flux) vs IBM Watson Speech to Text", "url": "https://www.anchorterminal.com/compare/deepgram-stt-vs-ibm-watson-stt" }, { "json": "https://www.anchorterminal.com/compare/elevenlabs-scribe-vs-giggy-stt.json", "title": "ElevenLabs Scribe Speech to Text API vs Giggy Speech to Text", "url": "https://www.anchorterminal.com/compare/elevenlabs-scribe-vs-giggy-stt" }, { "json": "https://www.anchorterminal.com/compare/elevenlabs-scribe-vs-ibm-watson-stt.json", "title": "ElevenLabs Scribe Speech to Text API vs IBM Watson Speech to Text", "url": "https://www.anchorterminal.com/compare/elevenlabs-scribe-vs-ibm-watson-stt" }, { "json": "https://www.anchorterminal.com/compare/giggy-stt-vs-gladia-stt.json", "title": "Giggy Speech to Text vs Gladia Speech-to-Text API + MCP", "url": "https://www.anchorterminal.com/compare/giggy-stt-vs-gladia-stt" }, { "json": "https://www.anchorterminal.com/compare/giggy-stt-vs-google-speech-to-text.json", "title": "Giggy Speech to Text vs Google Cloud Speech-to-Text", "url": "https://www.anchorterminal.com/compare/giggy-stt-vs-google-speech-to-text" }, { "json": "https://www.anchorterminal.com/compare/giggy-stt-vs-groq-speech-to-text.json", "title": "Giggy Speech to Text vs Groq Speech-to-Text", "url": "https://www.anchorterminal.com/compare/giggy-stt-vs-groq-speech-to-text" }, { "json": "https://www.anchorterminal.com/compare/giggy-stt-vs-inworld-stt.json", "title": "Giggy Speech to Text vs Inworld STT", "url": "https://www.anchorterminal.com/compare/giggy-stt-vs-inworld-stt" }, { "json": "https://www.anchorterminal.com/compare/giggy-stt-vs-minimax-stt.json", "title": "Giggy Speech to Text vs MiniMax Speech-to-Text", "url": "https://www.anchorterminal.com/compare/giggy-stt-vs-minimax-stt" }, { "json": "https://www.anchorterminal.com/compare/giggy-stt-vs-mistral-voxtral-transcribe.json", "title": "Giggy Speech to Text vs Mistral Voxtral Transcribe", "url": "https://www.anchorterminal.com/compare/giggy-stt-vs-mistral-voxtral-transcribe" }, { "json": "https://www.anchorterminal.com/compare/giggy-stt-vs-openai-speech-to-text.json", "title": "Giggy Speech to Text vs OpenAI Speech to Text", "url": "https://www.anchorterminal.com/compare/giggy-stt-vs-openai-speech-to-text" }, { "json": "https://www.anchorterminal.com/compare/giggy-stt-vs-rev-ai-stt.json", "title": "Giggy Speech to Text vs Rev AI Speech-to-Text API", "url": "https://www.anchorterminal.com/compare/giggy-stt-vs-rev-ai-stt" }, { "json": "https://www.anchorterminal.com/compare/giggy-stt-vs-smallest-pulse-stt.json", "title": "Giggy Speech to Text vs Smallest.ai Pulse STT", "url": "https://www.anchorterminal.com/compare/giggy-stt-vs-smallest-pulse-stt" }, { "json": "https://www.anchorterminal.com/compare/giggy-stt-vs-soniox-stt.json", "title": "Giggy Speech to Text vs Soniox Speech-to-Text", "url": "https://www.anchorterminal.com/compare/giggy-stt-vs-soniox-stt" }, { "json": "https://www.anchorterminal.com/compare/giggy-stt-vs-speechmatics-stt.json", "title": "Giggy Speech to Text vs Speechmatics Speech-to-Text", "url": "https://www.anchorterminal.com/compare/giggy-stt-vs-speechmatics-stt" }, { "json": "https://www.anchorterminal.com/compare/gladia-stt-vs-ibm-watson-stt.json", "title": "Gladia Speech-to-Text API + MCP vs IBM Watson Speech to Text", "url": "https://www.anchorterminal.com/compare/gladia-stt-vs-ibm-watson-stt" }, { "json": "https://www.anchorterminal.com/compare/google-speech-to-text-vs-ibm-watson-stt.json", "title": "Google Cloud Speech-to-Text vs IBM Watson Speech to Text", "url": "https://www.anchorterminal.com/compare/google-speech-to-text-vs-ibm-watson-stt" }, { "json": "https://www.anchorterminal.com/compare/groq-speech-to-text-vs-ibm-watson-stt.json", "title": "Groq Speech-to-Text vs IBM Watson Speech to Text", "url": "https://www.anchorterminal.com/compare/groq-speech-to-text-vs-ibm-watson-stt" }, { "json": "https://www.anchorterminal.com/compare/ibm-watson-stt-vs-inworld-stt.json", "title": "IBM Watson Speech to Text vs Inworld STT", "url": "https://www.anchorterminal.com/compare/ibm-watson-stt-vs-inworld-stt" }, { "json": "https://www.anchorterminal.com/compare/ibm-watson-stt-vs-minimax-stt.json", "title": "IBM Watson Speech to Text vs MiniMax Speech-to-Text", "url": "https://www.anchorterminal.com/compare/ibm-watson-stt-vs-minimax-stt" }, { "json": "https://www.anchorterminal.com/compare/ibm-watson-stt-vs-mistral-voxtral-transcribe.json", "title": "IBM Watson Speech to Text vs Mistral Voxtral Transcribe", "url": "https://www.anchorterminal.com/compare/ibm-watson-stt-vs-mistral-voxtral-transcribe" }, { "json": "https://www.anchorterminal.com/compare/ibm-watson-stt-vs-openai-speech-to-text.json", "title": "IBM Watson Speech to Text vs OpenAI Speech to Text", "url": "https://www.anchorterminal.com/compare/ibm-watson-stt-vs-openai-speech-to-text" }, { "json": "https://www.anchorterminal.com/compare/ibm-watson-stt-vs-rev-ai-stt.json", "title": "IBM Watson Speech to Text vs Rev AI Speech-to-Text API", "url": "https://www.anchorterminal.com/compare/ibm-watson-stt-vs-rev-ai-stt" }, { "json": "https://www.anchorterminal.com/compare/ibm-watson-stt-vs-smallest-pulse-stt.json", "title": "IBM Watson Speech to Text vs Smallest.ai Pulse STT", "url": "https://www.anchorterminal.com/compare/ibm-watson-stt-vs-smallest-pulse-stt" }, { "json": "https://www.anchorterminal.com/compare/ibm-watson-stt-vs-soniox-stt.json", "title": "IBM Watson Speech to Text vs Soniox Speech-to-Text", "url": "https://www.anchorterminal.com/compare/ibm-watson-stt-vs-soniox-stt" }, { "json": "https://www.anchorterminal.com/compare/ibm-watson-stt-vs-speechmatics-stt.json", "title": "IBM Watson Speech to Text vs Speechmatics Speech-to-Text", "url": "https://www.anchorterminal.com/compare/ibm-watson-stt-vs-speechmatics-stt" } ], "panel": null, "scores": [ { "by": 10, "edge": "giggy-stt", "giggy-stt": 50, "ibm-watson-stt": 40, "key": "reliability", "name": "Reliability", "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "by": 13, "edge": "giggy-stt", "giggy-stt": 68, "ibm-watson-stt": 55, "key": "schema", "name": "Schema \u0026 documentation", "weight": 13 }, { "by": 2, "edge": "giggy-stt", "giggy-stt": 68, "ibm-watson-stt": 66, "key": "ergonomics", "name": "Agent ergonomics", "weight": 13 }, { "by": 11, "edge": "ibm-watson-stt", "giggy-stt": 57, "ibm-watson-stt": 68, "key": "security", "name": "Security \u0026 auth", "weight": 14 }, { "by": 10, "edge": "ibm-watson-stt", "giggy-stt": 30, "ibm-watson-stt": 40, "key": "payments", "name": "Payments \u0026 pricing", "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "by": 36, "edge": "giggy-stt", "giggy-stt": 69, "ibm-watson-stt": 33, "key": "maintenance", "name": "Maintenance \u0026 community", "weight": 7 }, { "by": 50, "edge": "ibm-watson-stt", "giggy-stt": 30, "ibm-watson-stt": 80, "key": "transparency", "name": "Transparency \u0026 trust", "weight": 7 } ], "summary": "Giggy Speech to Text and IBM Watson Speech to Text score within a point of each other on agent readiness, 54.5 (C) and 54.5 (C). IBM Watson Speech to Text leads on security \u0026 auth, payments \u0026 pricing and transparency \u0026 trust. Both do speech-to-text.", "tasks": [ { "a": "$0.04 to $0.09", "b": "$0.60 to $1.20", "cheaper": "giggy-stt", "task": "Transcribe a one-hour meeting", "what": "60 minutes of recorded audio" } ], "tutorials": [], "verdicts": { "giggy-stt": "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.", "ibm-watson-stt": "Watson STT covers sync, async and WebSocket transcription in ten locations, with custom models, $0.02 a minute on Plus and 500 free minutes. There is no machine-readable spec or readable status history, requests are logged for improvement unless opted out, the WebSocket token goes in the URL, and the last release note is from June 2026." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/giggy-stt-vs-ibm-watson-stt", "json": "https://www.anchorterminal.com/compare/giggy-stt-vs-ibm-watson-stt.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/giggy-stt-vs-ibm-watson-stt.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/giggy-stt-vs-ibm-watson-stt.min.md" }, "markdown": "Giggy Speech to Text and IBM Watson Speech to Text score within a point of each other on agent readiness, 54.5 (C) and 54.5 (C). IBM Watson Speech to Text leads on security \u0026 auth, payments \u0026 pricing and transparency \u0026 trust. Both do speech-to-text.\n\n- Giggy Speech to Text: grade C, 54.5/100, rank #903 of 1486. Markdown https://www.anchorterminal.com/tools/giggy-stt.md · JSON https://www.anchorterminal.com/api/v1/tools/giggy-stt.json\n- IBM Watson Speech to Text: grade C, 54.5/100, rank #904 of 1486. Markdown https://www.anchorterminal.com/tools/ibm-watson-stt.md · JSON https://www.anchorterminal.com/api/v1/tools/ibm-watson-stt.json\n- Best speech-to-text APIs for AI agents: https://www.anchorterminal.com/best/speech-to-text/index.md\n- All 320 stt comparisons: https://www.anchorterminal.com/compare/speech-to-text/index.md\n\n## Which one, for what\n\n### Giggy Speech to Text (C)\n\nGood 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.\n\nAhead on:\n- Reliability, 50 against 40\n- Schema \u0026 documentation, 68 against 55\n- Maintenance \u0026 community, 69 against 33\n\nAlso in its favour:\n- A hosted endpoint, with nothing to install\n\nWatch for: Recorded input must be PCM16 mono 16 kHz WAV, so MP3, M4A, stereo or other sample rates need converting before upload\n\n### IBM Watson Speech to Text (C)\n\nGood for: Enterprises on IBM Cloud needing custom acoustic models, HIPAA on Premium and many locations.\n\nAhead on:\n- Security \u0026 auth, 68 against 57\n- Payments \u0026 pricing, 40 against 30\n- Transparency \u0026 trust, 80 against 30\n\nWatch for: No public OpenAPI file or llms.txt\n\n\n## Why the edge\n\n### Transparency \u0026 trust: IBM Watson Speech to Text +50\n\n- 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.\n- IBM Watson Speech to Text (80): Closed service under the IBM Cloud terms (15). The data security page states default logging for improvement with an opt-out, async retention, GDPR deletion by customer ID and plan-level separation, and they agree with each other. The DPA wasn't read (20 of 30). Previous-generation models carried a dated deprecation of 15 March 2022 and removal on 31 July 2023, and release notes say changes are compatible unless noted (18 of 20). Ten locations are named, with sub-processors not read (12 of 20). Total 65.\n\n### Maintenance \u0026 community: Giggy Speech to Text +36\n\n- 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.\n- IBM Watson Speech to Text (33): Read as a model. The newest release note is 29 June 2026 (`parameter_set` for large speech models), 130 days ago (10). No dated entries in the 90 days from 8 August (0). Release notes, IBM support and a public docs repository last changed 30 June 2026 (8 of 15). Official SDKs, `ibm-watson` 11.2.0 on PyPI (26 January 2026) and 12.2.0 on npm (January 2026) (10 of 15). CI not read (5 of 10). Total 33.\n\n### Schema \u0026 documentation: Giggy Speech to Text +13\n\n- 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.\n- IBM Watson Speech to Text (55): No public OpenAPI or other machine-readable definition was found (0). No llms.txt (0). The reference says which parameters each model family supports and the docs cover each feature (15 of 20). Typed parameters with defaults, ranges and enums (13 of 15). curl and SDK examples on every method, and errors with `error`, `code`, `code_description` and `warnings`, with invalid arguments reported as warnings (12 of 15). Versioned `/v1` paths and dated release notes back to 2015 (15). Total 55.\n\n### Security \u0026 auth: IBM Watson Speech to Text +11\n\n- 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.\n- IBM Watson Speech to Text (68): Model reading of the checklist, with training and retention in place of the least-privilege and injection lines. IAM API keys per instance with Reader, Writer and Manager roles and short-lived bearer tokens (30). The WebSocket interface takes the token as an `access_token` query parameter, so 10 off (20 of 30). Request and response data are logged by default to improve the service, and any account can opt out per account or per request (10 of 20). Async results expire after a week by default (`results_ttl` shortens it), a delete method exists, and `DELETE /v1/user_data` removes data by customer ID (10 of 15). Activity Tracker events record changes to the service's state (10 of 15). Valid security.txt to 6 December 2026, PSIRT and HackerOne, HIPAA readiness on Premium. Certifications weren't read (18 of 20). Total 68.\n\n### Reliability: Giggy Speech to Text +10\n\n- 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.\n- IBM Watson Speech to Text (40): IBM Cloud has a status page at cloud.ibm.com/status, but it is drawn by script and showed no components or history to our reader, read as gemini-live's script-shell page was (10 of 20, and 5 for no readable history). Plus allows up to 100 concurrent transcription requests across WebSocket and HTTP (15). No 429 or backoff guidance was found in the API reference or the docs repository (0). No SLA was read for the service (0, unchecked). Next-generation and large speech models are GA, language by language (10). 10 + 5 + 15 + 0 + 0 + 10 = 40.\n\n### Payments \u0026 pricing: IBM Watson Speech to Text +10\n\n- Giggy Speech to Text (30): No x402, MPP or L402 (0). Per-hour prices for Batch, Fast and Streaming and every credit pack published without a login (20). Free Batch transcription and Personal free credits topped up to 10,000 a month, but no page read says whether a card is needed, so half, as other listings read an unstated card requirement (10 of 20). A person signs up in a browser and creates the key there; no programmatic key route was found (0). 0+20+10+0 = 30.\n- IBM Watson Speech to Text (40): No x402, MPP or L402 (0). Per-minute Plus prices published in the docs without a login (20). The Lite plan gives 500 free minutes a month, and no page read says a card is needed (20). A person creates an IBM Cloud account and service instance in a browser (0). Total 40.\n\n## Score by category\n\n| Category | Weight | Giggy Speech to Text | IBM Watson Speech to Text | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 50 | 40 | Giggy Speech to Text +10 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 68 | 55 | Giggy Speech to Text +13 |\n| Agent ergonomics | 13% (16.2 this run) | 68 | 66 | Giggy Speech to Text +2 |\n| Security \u0026 auth | 14% (17.5 this run) | 57 | 68 | IBM Watson Speech to Text +11 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 30 | 40 | IBM Watson Speech to Text +10 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 69 | 33 | Giggy Speech to Text +36 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 30 | 80 | IBM Watson Speech to Text +50 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **54.5 · C** | **54.5 · C** | |\n\n## Facts side by side\n\n| Fact | Giggy Speech to Text | IBM Watson Speech to Text |\n| --- | --- | --- |\n| Kind | Model API | Model API |\n| Vendor | Giggy | IBM |\n| Hosted endpoint | `https://api.giggy.ai/v1` | no (local only) |\n| Transports | HTTP, SSE (legacy), websocket, Streamable HTTP | HTTP, websocket |\n| Auth | API key | API key |\n| Pricing | Freemium | Freemium |\n| x402 | no | no |\n| Licence | 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 | none |\n| Tools exposed | none | 0 |\n| Read-only variant documented | no | no |\n| llms.txt | yes | no |\n| MCP registry | `io.github.GRQDigitalCapital/giggy-mcp` | not listed |\n| Last release | 2026-11-01 | 2026-06-29 |\n| Terms last updated | not read yet | no date given |\n| Privacy policy last updated | not read yet | 2026-08-25 |\n| Customer content may train models | not read yet | not found in the text |\n| Terms restrict automated access | not read yet | not found in the text |\n| Terms restrict benchmarking | not read yet | not found in the text |\n| Terms or service can change without notice | not read yet | not found in the text |\n| Arbitration or class-action waiver | not read yet | not found in the text |\n\n## What the same task costs\n\n| Task | Giggy Speech to Text | IBM Watson Speech to Text |\n| --- | --- | --- |\n| Transcribe a one-hour meeting | $0.04 to $0.09 | $0.60 to $1.20 |\n\n## Verdicts\n\n**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.\n\n**IBM Watson Speech to Text.** Watson STT covers sync, async and WebSocket transcription in ten locations, with custom models, $0.02 a minute on Plus and 500 free minutes. There is no machine-readable spec or readable status history, requests are logged for improvement unless opted out, the WebSocket token goes in the URL, and the last release note is from June 2026.\n\n## Before you call either\n\n### Giggy Speech to Text\n\n1. Pass `mode=batch` explicitly for free transcription. Raw REST and MCP default to paid Fast when `mode` is omitted.\n2. Convert audio to PCM16 mono 16 kHz WAV first and send it as the raw body with `Content-Type: audio/wav`.\n3. 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.\n4. Fetch the transcript from the signed `generation.result.url` after completion, without the API key. It can disappear after retention cleanup.\n5. On the live WebSocket, replace earlier text with each cumulative `transcription` event and publish final text only after `input_complete`.\n\n### IBM Watson Speech to Text\n\n1. Send `X-Watson-Learning-Opt-Out: true` or opt out at account level before sending sensitive audio\n2. Use the async `/v1/recognitions` interface with a callback for files over 100 MB, up to 1 GB\n3. Pass a large speech or next-generation model name in `model`, since previous-generation models were removed\n4. Tag requests with `X-Watson-Metadata: customer_id=...` so the data can be deleted later\n5. Keep within 100 concurrent requests on Plus across HTTP and WebSocket\n\n## Questions\n\n### Which is better for AI agents, Giggy Speech to Text or IBM Watson Speech to Text?\n\nGiggy Speech to Text and IBM Watson Speech to Text score within a point of each other on agent readiness, 54.5 (C) and 54.5 (C). IBM Watson Speech to Text leads on security \u0026 auth, payments \u0026 pricing and transparency \u0026 trust.\n\n### Do Giggy Speech to Text and IBM Watson Speech to Text need an API key?\n\nBoth need an API key.\n\n### Can an agent call Giggy Speech to Text and IBM Watson Speech to Text without installing anything?\n\nGiggy Speech to Text has a hosted endpoint at https://api.giggy.ai/v1. No hosted endpoint is listed for IBM Watson Speech to Text.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/giggy-stt-vs-ibm-watson-stt.json, and with the fewest tokens: https://www.anchorterminal.com/compare/giggy-stt-vs-ibm-watson-stt.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"giggy-stt\", \"b\": \"ibm-watson-stt\"}`. From a terminal: `anchor compare giggy-stt ibm-watson-stt`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/giggy-stt.json and https://www.anchorterminal.com/api/v1/tools/ibm-watson-stt.json\n\n## Other comparisons with Giggy Speech to Text or IBM Watson Speech to Text\n\n- [Amazon Transcribe vs Giggy Speech to Text](https://www.anchorterminal.com/compare/amazon-transcribe-vs-giggy-stt.md)\n- [Amazon Transcribe vs IBM Watson Speech to Text](https://www.anchorterminal.com/compare/amazon-transcribe-vs-ibm-watson-stt.md)\n- [AssemblyAI Speech-to-Text (Universal) vs Giggy Speech to Text](https://www.anchorterminal.com/compare/assemblyai-stt-vs-giggy-stt.md)\n- [AssemblyAI Speech-to-Text (Universal) vs IBM Watson Speech to Text](https://www.anchorterminal.com/compare/assemblyai-stt-vs-ibm-watson-stt.md)\n- [Azure AI Speech speech-to-text vs Giggy Speech to Text](https://www.anchorterminal.com/compare/azure-speech-to-text-vs-giggy-stt.md)\n- [Azure AI Speech speech-to-text vs IBM Watson Speech to Text](https://www.anchorterminal.com/compare/azure-speech-to-text-vs-ibm-watson-stt.md)\n- [Cartesia Ink vs Giggy Speech to Text](https://www.anchorterminal.com/compare/cartesia-ink-stt-vs-giggy-stt.md)\n- [Cartesia Ink vs IBM Watson Speech to Text](https://www.anchorterminal.com/compare/cartesia-ink-stt-vs-ibm-watson-stt.md)\n- [Deepgram Speech-to-Text (Nova-3, Flux) vs Giggy Speech to Text](https://www.anchorterminal.com/compare/deepgram-stt-vs-giggy-stt.md)\n- [Deepgram Speech-to-Text (Nova-3, Flux) vs IBM Watson Speech to Text](https://www.anchorterminal.com/compare/deepgram-stt-vs-ibm-watson-stt.md)\n- [ElevenLabs Scribe Speech to Text API vs Giggy Speech to Text](https://www.anchorterminal.com/compare/elevenlabs-scribe-vs-giggy-stt.md)\n- [ElevenLabs Scribe Speech to Text API vs IBM Watson Speech to Text](https://www.anchorterminal.com/compare/elevenlabs-scribe-vs-ibm-watson-stt.md)\n- [Giggy Speech to Text vs Gladia Speech-to-Text API + MCP](https://www.anchorterminal.com/compare/giggy-stt-vs-gladia-stt.md)\n- [Giggy Speech to Text vs Google Cloud Speech-to-Text](https://www.anchorterminal.com/compare/giggy-stt-vs-google-speech-to-text.md)\n- [Giggy Speech to Text vs Groq Speech-to-Text](https://www.anchorterminal.com/compare/giggy-stt-vs-groq-speech-to-text.md)\n- [Giggy Speech to Text vs Inworld STT](https://www.anchorterminal.com/compare/giggy-stt-vs-inworld-stt.md)\n- [Giggy Speech to Text vs MiniMax Speech-to-Text](https://www.anchorterminal.com/compare/giggy-stt-vs-minimax-stt.md)\n- [Giggy Speech to Text vs Mistral Voxtral Transcribe](https://www.anchorterminal.com/compare/giggy-stt-vs-mistral-voxtral-transcribe.md)\n- [Giggy Speech to Text vs OpenAI Speech to Text](https://www.anchorterminal.com/compare/giggy-stt-vs-openai-speech-to-text.md)\n- [Giggy Speech to Text vs Rev AI Speech-to-Text API](https://www.anchorterminal.com/compare/giggy-stt-vs-rev-ai-stt.md)\n- [Giggy Speech to Text vs Smallest.ai Pulse STT](https://www.anchorterminal.com/compare/giggy-stt-vs-smallest-pulse-stt.md)\n- [Giggy Speech to Text vs Soniox Speech-to-Text](https://www.anchorterminal.com/compare/giggy-stt-vs-soniox-stt.md)\n- [Giggy Speech to Text vs Speechmatics Speech-to-Text](https://www.anchorterminal.com/compare/giggy-stt-vs-speechmatics-stt.md)\n- [Gladia Speech-to-Text API + MCP vs IBM Watson Speech to Text](https://www.anchorterminal.com/compare/gladia-stt-vs-ibm-watson-stt.md)\n- [Google Cloud Speech-to-Text vs IBM Watson Speech to Text](https://www.anchorterminal.com/compare/google-speech-to-text-vs-ibm-watson-stt.md)\n- [Groq Speech-to-Text vs IBM Watson Speech to Text](https://www.anchorterminal.com/compare/groq-speech-to-text-vs-ibm-watson-stt.md)\n- [IBM Watson Speech to Text vs Inworld STT](https://www.anchorterminal.com/compare/ibm-watson-stt-vs-inworld-stt.md)\n- [IBM Watson Speech to Text vs MiniMax Speech-to-Text](https://www.anchorterminal.com/compare/ibm-watson-stt-vs-minimax-stt.md)\n- [IBM Watson Speech to Text vs Mistral Voxtral Transcribe](https://www.anchorterminal.com/compare/ibm-watson-stt-vs-mistral-voxtral-transcribe.md)\n- [IBM Watson Speech to Text vs OpenAI Speech to Text](https://www.anchorterminal.com/compare/ibm-watson-stt-vs-openai-speech-to-text.md)\n- [IBM Watson Speech to Text vs Rev AI Speech-to-Text API](https://www.anchorterminal.com/compare/ibm-watson-stt-vs-rev-ai-stt.md)\n- [IBM Watson Speech to Text vs Smallest.ai Pulse STT](https://www.anchorterminal.com/compare/ibm-watson-stt-vs-smallest-pulse-stt.md)\n- [IBM Watson Speech to Text vs Soniox Speech-to-Text](https://www.anchorterminal.com/compare/ibm-watson-stt-vs-soniox-stt.md)\n- [IBM Watson Speech to Text vs Speechmatics Speech-to-Text](https://www.anchorterminal.com/compare/ibm-watson-stt-vs-speechmatics-stt.md)\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "attributionRequired": true, "credit": "CC BY 4.0, credit Anchor Terminal and link the page or API 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