{ "data": { "a": { "slug": "azure-speech-to-text", "name": "Azure AI Speech speech-to-text", "vendor": "Microsoft", "vendorUrl": "https://azure.microsoft.com/en-us/products/ai-services/speech-to-text", "kind": "model", "category": "speech-to-text", "summary": "Azure's speech-to-text service for transcribing audio.", "url": "https://www.anchorterminal.com/tools/azure-speech-to-text", "markdownUrl": "https://www.anchorterminal.com/tools/azure-speech-to-text.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/azure-speech-to-text.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/azure-speech-to-text.json", "dns": "azure-speech-to-text.grade.anchorterminal.com", "repo": "https://github.com/Azure-Samples/cognitive-services-speech-sdk", "license": "MIT (samples), SDK under Microsoft's own licence", "transports": [ "http" ], "remoteUrl": "https://eastus.api.cognitive.microsoft.com/speechtotext", "packages": [ { "registry": "pypi", "name": "azure-cognitiveservices-speech" }, { "registry": "npm", "name": "microsoft-cognitiveservices-speech-sdk" } ], "auth": "mixed", "authNotes": "`Ocp-Apim-Subscription-Key` header with a Speech resource key, or a Microsoft Entra ID bearer token (Microsoft's recommended keyless option). Endpoints are per region or per resource. The MAI-Transcribe-2-Streaming Realtime WebSocket also takes the key as an `api-key` header or query-string parameter.", "pricing": "freemium", "pricingNotes": "Free F0 tier with 5 audio hours a month of real-time. Pay as you go in East US is $1 an hour real-time, $0.36 fast transcription, $0.18 batch, $1.20 custom real-time. Diarisation and continuous language ID in real time add $0.30 an hour each. MAI-Transcribe-2 is $0.10 an hour until 2026-12-31. MAI-Transcribe-2-Streaming is $0.54 an hour until the end of 2026 per Microsoft AI's launch post (https://microsoft.ai/news/our-first-streaming-transcription-model/), with no Azure meter found on 2026-10-05, 2026-10-17 or 2026-11-01. Commitment tiers from $1,600 a month for 2,000 hours (https://azure.microsoft.com/en-us/pricing/details/speech/).", "priceSummary": "Freemium", "where": "hosted", "x402": { "level": "no", "evidence": "No machine payment. Billing runs through a cloud account with a card or invoice.", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": 3450, "npmWeekly": 475621, "pypiWeekly": 1032532, "asOf": "2026-09-30" }, "docsUrl": "https://learn.microsoft.com/en-us/azure/ai-services/speech-service/speech-to-text", "openapi": "https://raw.githubusercontent.com/Azure/azure-rest-api-specs/main/specification/cognitiveservices/data-plane/SpeechToText/stable/2025-10-15/speechtotext.json", "capabilities": [ "speech.stt", "speech.streaming", "speech.batch", "speech.diarisation", "speech.languages", "speech.translation" ], "tags": [ "hosted", "freemium", "free-tier", "closed-source", "python", "typescript", "enterprise", "streaming", "batch", "async-jobs", "webhooks" ], "lastRelease": "2026-10-21", "graded": true, "anchor": { "graded": true, "score": 75.1, "grade": "BB", "agentReady": true, "rank": 61, "ranked": true, "rankOf": 1349, "categoryRank": 1, "methodology": "0.7", "run": "2026-10-01", "scores": { "ergonomics": 75, "maintenance": 80, "payments": 20, "reliability": 90, "schema": 80, "security": 85, "transparency": 86 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-11-01" }, "negative": 0, "verdict": "Real-time and fast transcription audio isn't stored, and customer audio isn't used for training. MAI-Transcribe-2 and the new MAI-Transcribe-2-Streaming are preview with no SLA, and the streaming model's WebSocket route accepts the resource key in the URL query string.", "bestFor": "Teams on Azure who need several modes (real time, synchronous files, cheap batch, custom models) under one resource, or strict default data handling.", "strengths": [ "Real-time and fast transcription audio isn't stored, and customer audio isn't used for training", "Fast transcription returns files up to 5 hours and 500 MB in one synchronous call", "Batch at $0.18 an hour, and a free F0 tier with 5 real-time hours a month", "429 guidance with a concrete backoff pattern of 1, 2, 4 and 4 minutes", "Keys or Entra ID tokens with role-based access" ], "weaknesses": [ "MAI-Transcribe-2 and MAI-Transcribe-2-Streaming are preview with no SLA, and both introductory prices end with 2026", "The MAI-Transcribe-2-Streaming WebSocket docs allow the resource key as an `api-key` query parameter", "REST API v3.0 and the v3.2 previews were retired on 2026-03-31, and older samples still target them", "No llms.txt, and the pricing page needs JavaScript", "An Azure subscription needs a card, even for the F0 tier" ], "agentNotes": [ "Use fast transcription (`transcriptions:transcribe`) for files under 5 hours and 500 MB, and batch for bulk jobs with `timeToLive` set", "Pin `api-version=2025-10-15`. v3.0 and the v3.2 previews are retired", "On a 429, back off 1, 2, 4 then 4 minutes. It usually means autoscaling, not a quota", "For MAI-Transcribe-2-Streaming, use Speech SDK 1.52 or later with the `/speech/universal/v2` endpoint, or send the key in the `api-key` header, never the query string", "Don't budget on MAI-Transcribe-2 at $0.10 or MAI-Transcribe-2-Streaming at $0.54 an hour after 2026-12-31" ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 8, "avgRating": 3.3, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "BB", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 75.1 } ], "editorialScores": { "ergonomics": 75, "maintenance": 80, "payments": 20, "reliability": 90, "schema": 80, "security": 85, "transparency": 80 }, "provenanceScore": 91, "checked": { "categories": { "ergonomics": "2026-11-01", "maintenance": "2026-11-01", "payments": "2026-11-01", "reliability": "2026-11-01", "schema": "2026-11-01", "security": "2026-11-01", "transparency": "2026-11-01" }, "last": "2026-11-01", "lastMoved": "2026-11-01", "oldest": "2026-11-01", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "pip install azure-cognitiveservices-speech # or: npm i microsoft-cognitiveservices-speech-sdk", "http": "curl -X POST \"https://$AZURE_SPEECH_RESOURCE.cognitiveservices.azure.com/speechtotext/transcriptions:transcribe?api-version=2025-10-15\" \\\n -H \"Ocp-Apim-Subscription-Key: $AZURE_SPEECH_KEY\" \\\n -F \"audio=@call.wav\" -F 'definition={\"locales\":[\"en-US\"]}'" }, "letme": { "capability": "https://letme.dev/speech.stt", "tool": "https://letme.dev/azure-speech-to-text" }, "sameCompany": [ "azure-foundry-fine-tuning", "azure-ai-content-safety", "azure-text-to-speech", "microsoft-agent-framework", "microsoft-execution-containers", "microsoft-entra-agent-id", "azure-key-vault", "azure-document-intelligence", "azure-devops-mcp", "microsoft-learn-mcp", "tokeneconomizer", "playwright-mcp", "azure-mcp", "azure-maps", "azure-translator", "microsoft-graph-calendar", "azure-blob-storage", "onedrive-sharepoint", "microsoft-teams", "dynamics-365-sales", "copilot-studio-mcp", "power-automate", "foundry-local", "microsoft-decision-1", "microsoft-advertising-api", "microsoft-excel-graph", "outlook-mail-graph", "microsoft-graph-people", "mage-vl-cowork" ], "area": "voice", "unitPrices": [ { "item": "Real-time standard", "unit": "audio-minute", "usd": 0.0167, "note": "$1 an hour, East US" }, { "item": "Fast transcription", "unit": "audio-minute", "usd": 0.006, "note": "$0.36 an hour" }, { "item": "Batch standard", "unit": "audio-minute", "usd": 0.003, "note": "$0.18 an hour" }, { "item": "MAI-Transcribe-2 (preview)", "unit": "audio-minute", "usd": 0.00167, "note": "$0.10 an hour, promotional until 2026-12-31" }, { "item": "MAI-Transcribe-2-Streaming (preview)", "unit": "audio-minute", "usd": 0.009, "note": "$0.54 an hour, introductory until the end of 2026, per Microsoft AI's launch post. No Azure meter found on 2026-11-01" }, { "item": "Custom real-time", "unit": "audio-minute", "usd": 0.02, "note": "$1.20 an hour, plus endpoint hosting" }, { "item": "Real-time add-on (diarisation or language ID)", "unit": "audio-minute", "usd": 0.005, "note": "$0.30 an hour per feature" } ], "provenance": { "legalEntity": "Microsoft Corporation", "domain": "microsoft.com", "domainRegistered": "1991-05-02", "domainNote": "Endpoints are on speech.microsoft.com, api.cognitive.microsoft.com and cognitiveservices.azure.com. microsoft.com publishes a security.txt, but it passed its Expires date on 2026-09-23 and was still expired on 2026-11-01.", "endpointOnVendorDomain": true, "terms": "https://www.microsoft.com/licensing/terms/", "privacy": "https://privacy.microsoft.com/en-us/privacystatement", "statusPage": "https://azure.status.microsoft/en-us/status", "changelog": "https://learn.microsoft.com/en-us/azure/ai-services/speech-service/releasenotes", "securityTxt": "expired", "checked": "2026-11-01", "score": 91 }, "requirements": { "card": "unknown", "freeTier": true, "minutes": 10, "credential": "api-key", "hosting": [ "global", "US", "EU", "UK" ], "euResidency": "yes", "trainsOnData": "unknown", "zeroRetention": "yes", "certifications": [ "ISO 27001", "PCI DSS", "CSA STAR", "FedRAMP", "GDPR" ], "dpa": true, "selfHost": "yes" }, "pageJsonUrl": "https://www.anchorterminal.com/tools/azure-speech-to-text.json", "live": { "slug": "azure-speech-to-text", "probe": { "target": "https://eastus.api.cognitive.microsoft.com/speechtotext", "method": "get", "lastAt": "2026-11-04T20:46:33.132420953Z", "lastOk": true, "lastStatus": 404, "lastMs": 464, "authRequired": false, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 392, "p95ms24h": 571, "samples24h": 246, "samples30d": 8081, "days": [ { "date": "2026-10-04", "probes": 272, "ok": 272 }, { "date": "2026-10-05", "probes": 272, "ok": 272 }, { "date": "2026-10-06", "probes": 272, "ok": 272 }, { "date": "2026-10-07", "probes": 272, "ok": 272 }, { "date": "2026-10-08", "probes": 268, "ok": 268 }, { "date": "2026-10-09", "probes": 250, "ok": 250 }, { "date": "2026-10-10", "probes": 247, "ok": 247 }, { "date": "2026-10-11", "probes": 247, "ok": 247 }, { "date": "2026-10-12", "probes": 247, "ok": 247 }, { "date": "2026-10-13", "probes": 248, "ok": 248 }, { "date": "2026-10-14", "probes": 249, "ok": 249 }, { "date": "2026-10-15", "probes": 253, "ok": 253 }, { "date": "2026-10-16", "probes": 252, "ok": 252 }, { "date": "2026-10-17", "probes": 253, "ok": 253 }, { "date": "2026-10-18", "probes": 256, "ok": 256 }, { "date": "2026-10-19", "probes": 255, "ok": 255 }, { "date": "2026-10-20", "probes": 252, "ok": 252 }, { "date": "2026-10-21", "probes": 253, "ok": 253 }, { "date": "2026-10-22", "probes": 253, "ok": 253 }, { "date": "2026-10-23", "probes": 248, "ok": 248 }, { "date": "2026-10-24", "probes": 251, "ok": 251 }, { "date": "2026-10-25", "probes": 250, "ok": 250 }, { "date": "2026-10-26", "probes": 252, "ok": 252 }, { "date": "2026-10-27", "probes": 249, "ok": 249 }, { "date": "2026-10-28", "probes": 251, "ok": 251 }, { "date": "2026-10-29", "probes": 249, "ok": 249 }, { "date": "2026-10-30", "probes": 248, "ok": 248 }, { "date": "2026-10-31", "probes": 251, "ok": 251 }, { "date": "2026-11-01", "probes": 249, "ok": 249 }, { "date": "2026-11-02", "probes": 250, "ok": 250 }, { "date": "2026-11-03", "probes": 248, "ok": 248 }, { "date": "2026-11-04", "probes": 214, "ok": 214 } ] }, "vendorStatus": { "page": "https://azure.status.microsoft/en-us/status", "indicator": "unknown", "summary": "no machine-readable status found", "checkedAt": "2026-10-08T10:02:53.355601307Z" }, "versions": [ { "registry": "github", "name": "Azure-Samples/cognitive-services-speech-sdk", "version": "ingestion-v2.1.13", "released": "2026-07-10", "seenAt": "2026-11-04T20:01:17.641513549Z" }, { "registry": "npm", "name": "microsoft-cognitiveservices-speech-sdk", "version": "1.53.0", "seenAt": "2026-11-04T20:01:17.207625806Z" }, { "registry": "pypi", "name": "azure-cognitiveservices-speech", "version": "1.53.0", "released": "2026-10-21", "seenAt": "2026-11-04T20:01:16.961771451Z" } ], "githubStars": 3457, "npmWeekly": 672859, "pypiWeekly": 611086, "securityTxt": { "url": "https://microsoft.com/.well-known/security.txt", "state": "expired", "expires": "2026-09-23T16:00:00.000Z", "checkedAt": "2026-11-04T15:53:07.385907604Z" }, "domain": { "domain": "microsoft.com", "registered": "1991-05-02", "source": "https://rdap.verisign.com/com/v1/domain/microsoft.com", "checkedAt": "2026-11-01T14:14:19.336136409Z" }, "pages": [ { "url": "https://learn.microsoft.com/en-us/azure/ai-services/speech-service/releasenotes", "kind": "changelog", "status": 304, "checkedAt": "2026-11-04T08:26:53.534596238Z", "changedAt": "2026-11-03T07:52:59.892662471Z", "fingerprint": "962a010ed30d" }, { "url": "https://learn.microsoft.com/en-us/azure/ai-services/speech-service/mai-transcribe", "kind": "deprecations", "status": 304, "checkedAt": "2026-11-04T08:26:50.866817434Z", "changedAt": "2026-10-29T05:09:59.458433803Z", "fingerprint": "4ee1ac42175f" }, { "url": "https://learn.microsoft.com/en-us/azure/ai-services/speech-service/rest-speech-to-text", "kind": "deprecations", "status": 304, "checkedAt": "2026-11-04T08:26:54.760173208Z", "changedAt": "2026-10-20T00:00:55.457010036Z", "fingerprint": "be303dc8912d" }, { "url": "https://azure.microsoft.com/en-us/pricing/details/speech/", "kind": "pricing", "status": 200, "checkedAt": "2026-11-04T08:15:29.316051657Z", "changedAt": "2026-10-30T05:32:17.448922931Z", "fingerprint": "bb9c72ab8aae" }, { "url": "https://privacy.microsoft.com/en-us/privacystatement", "kind": "privacy", "status": 200, "checkedAt": "2026-10-01T13:14:57.860748137Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "07484a06f35c" }, { "url": "https://www.microsoft.com/licensing/terms/", "kind": "terms", "status": 502, "checkedAt": "2026-10-01T13:17:52.720054167Z", "changedAt": "0001-01-01T00:00:00Z" } ], "github": { "repo": "azure-samples/cognitive-services-speech-sdk", "checkedAt": "2026-11-02T08:33:46.314203025Z", "stars": 3457, "archived": false, "issuesEnabled": true, "pushedAt": "2026-10-30T08:28:26Z", "openIssues": 2, "openPRs": 44, "issuesOpened90d": 22, "issuesClosed90d": 24, "prsMerged90d": 12, "ci": { "branch": "master", "runs": 4, "success": 1, "failure": 3, "other": 0, "latest": "failure", "latestAt": "2026-10-23T22:25:20Z" }, "replies": { "sampled": 25, "unanswered": 1, "medianDays": 1 } }, "updatedAt": "2026-11-04T20:46:33.132420953Z" } }, "answer": "Azure AI Speech speech-to-text scores 75.1 (BB) on agent readiness against Giggy Speech to Text's 54.5 (C), and leads in 6 of 7 scored categories. Giggy Speech to Text leads on payments \u0026 pricing.", "b": { "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": 805, "ranked": true, "rankOf": 1349, "categoryRank": 15, "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": "2026-11-04T20:46:47.290932988Z", "lastOk": true, "lastStatus": 404, "lastMs": 524, "authRequired": false, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 286, "p95ms24h": 654, "samples24h": 55, "samples30d": 55, "days": [ { "date": "2026-11-04", "probes": 55, "ok": 55 } ] }, "vendorStatus": { "page": "https://giggy.ai/status", "indicator": "unknown", "summary": "no machine-readable status found", "checkedAt": "2026-11-04T15:31:56.257084594Z" }, "versions": [ { "registry": "github", "name": "giggy-ai/giggy-js", "version": "v0.1.0", "released": "2026-10-07", "seenAt": "2026-11-04T20:18:19.850001725Z" }, { "registry": "mcp-registry", "name": "io.github.GRQDigitalCapital/giggy-mcp", "version": "1.0.2", "seenAt": "2026-11-04T17:11:44.552009456Z" }, { "registry": "npm", "name": "@giggy-ai/sdk", "version": "0.2.1", "seenAt": "2026-11-04T20:18:17.973963585Z" }, { "registry": "pypi", "name": "giggy", "version": "0.2.1", "released": "2026-11-01", "seenAt": "2026-11-04T20:18:17.733675794Z" }, { "registry": "pypi", "name": "pipecat-giggy", "version": "0.3.3", "released": "2026-11-01", "seenAt": "2026-11-04T20:18:18.946085778Z" } ], "githubStars": 0, "npmWeekly": 209, "pypiWeekly": 32, "securityTxt": { "url": "https://giggy.ai/.well-known/security.txt", "state": "none", "checkedAt": "2026-11-04T15:54:29.107165985Z" }, "updatedAt": "2026-11-04T20:46:47.290932988Z" } }, "edges": [ { "by": 56, "category": "transparency", "leads": "azure-speech-to-text", "name": "Transparency \u0026 trust", "reasons": { "a": "Closed service under Microsoft's product terms, with an MIT samples repository (15). The data privacy page, the privacy statement and the product terms agree on no storage for real time, retention until deletion for batch and no training (25). The data privacy page, re-read on 1 November and still dated 26 August, doesn't name the MAI streaming model. Dated retirements, REST API v3.0 and the v3.2 previews on 2026-03-31 and MAI-Transcribe-1 deprecated on 2026-08-20, under Microsoft's published lifecycle policy. The versions page now says 2024-05-15-preview will be retired on a date to be announced (20). Regions chosen per resource and a public sub-processor list (20). 15 + 25 + 20 + 20 = 80.", "b": "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." }, "scores": { "a": 86, "b": 30 } }, { "by": 40, "category": "reliability", "leads": "azure-speech-to-text", "name": "Reliability", "reasons": { "a": "Azure status page with post-incident reviews (20). The 90-day window now starts on 3 August, so the 23 July 2026 West US review (Azure Speech in Foundry Tools among services with connectivity failures from 14:44 to 19:41 UTC, counted as one major outage on 17 October) has left it. Two reviews remain on 1 November. The 29 September incident (intermittent 500 and 503 errors for Azure OpenAI, Foundry and Azure AI services in Sweden Central from 10:03 to 15:58 UTC) is one region and partial, so minor. The 30 September to 1 October gateway review (20:30 to 02:15 UTC) names networking services, not Speech. Nothing newer is listed. Minor incidents only (20). Limits published with numbers, 100 concurrent real-time requests and 600 fast or batch requests a minute by default, on the quotas page updated 23 October and re-read on 1 November (15). 429 documented as usually meaning autoscaling in progress, with retry logic advised and a 1-2-4-4 minute pattern for ramping load. Fast transcription is synchronous, so a retry doesn't duplicate a job (15). Covered by Microsoft's online services SLA, per the 1 October check (10). Fast transcription, batch and real time are GA. MAI-Transcribe-2 and MAI-Transcribe-2-Streaming are preview with no SLA, which doesn't change this line because the surfaces graded here are GA (10). 20 + 20 + 15 + 15 + 10 + 10 = 90.", "b": "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." }, "scores": { "a": 90, "b": 50 } }, { "by": 28, "category": "security", "leads": "azure-speech-to-text", "name": "Security \u0026 auth", "reasons": { "a": "Model reading of the checklist, with training and retention in place of least-privilege and injection lines. Two regenerable resource keys for rotation, or Microsoft Entra ID tokens with Azure role-based access, which Microsoft recommends (30). Less 10 because the Realtime route for MAI-Transcribe-2-Streaming, added on 1 October 2026, documents an `api-key` query-string parameter on the WebSocket URI, using the same resource key, still so on the page updated 7 October and re-read on 1 November. The speech-to-text REST, fast, batch and Speech SDK docs take the key in a header only, so the deduction is applied once. SAS URLs for batch audio are scoped tokens for the customer's own storage, not the Speech credential, and aren't deducted (20). Microsoft doesn't use customer audio to train its models, per the 1 October check, not re-read since (20). Real-time and fast transcription audio isn't stored, and batch output stays until deleted or `timeToLive` expires, per the data privacy page updated 2026-08-26 and re-read on 1 November (15). Azure Monitor and the activity log record resource actions, though we didn't confirm per-request Speech logging (10). The MSRC disclosure policy, Microsoft's Azure bounty programme, SOC 2 and ISO 27001 reports, and public advisories. An MSRC search on 1 November found no CVE for the Azure Speech service (the Speech entries are Windows Speech components and Chromium). The microsoft.com security.txt passed its Expires date on 2026-09-23 and was still expired on 1 November, which we note without deducting, as on 5 and 17 October (20). 20 + 20 + 15 + 10 + 20 = 85.", "b": "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." }, "scores": { "a": 85, "b": 57 } }, { "by": 12, "category": "schema", "leads": "azure-speech-to-text", "name": "Schema \u0026 documentation", "reasons": { "a": "The speech-to-text OpenAPI document for GA version 2025-10-15 is in Microsoft's public REST API specs, re-read on 1 November with 67 operations (25). No llms.txt found (0). Operation descriptions state purpose, and the speech-to-text overview says when to use real time, fast or batch (15). Batch request bodies are typed with enums and required fields, but fast transcription takes its options as a JSON string in the multipart `definition` field, documented but untyped on the wire (10). The REST reference carries examples and error responses per operation, per the 5 October check, not re-read on 1 November (15). Dated `api-version` values (current GA 2025-10-15, on the versions page updated 19 October) and monthly release notes (15). MAI-Transcribe-2-Streaming's WebSocket events are documented in prose and examples only, with no machine-readable contract, which leaves the graded REST surface unchanged. 25 + 0 + 15 + 10 + 15 + 15 = 80.", "b": "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." }, "scores": { "a": 80, "b": 68 } }, { "by": 11, "category": "maintenance", "leads": "azure-speech-to-text", "name": "Maintenance \u0026 community", "reasons": { "a": "Speech SDK 1.53.0 is the latest on npm on 1 November (the JavaScript SDK's v1.53.0 tag is dated 21 October, and the samples repository moved to 1.53.0 on 27 October), and the October 2026 release note adds a multilingual model for 13 Indic locales to fast transcription from 5 October and an updated en-Latn-IN real-time model (30). Release notes for August, September and October 2026, and SDK 1.52.0 (September) and 1.53.0 (October) (20). Public release notes, a GitHub samples repository and Microsoft Q\u0026A (10). Speech SDK current in eight languages (15). The SDK itself is a closed binary, so we can't see its CI (5). 30 + 20 + 10 + 15 + 5 = 80.", "b": "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." }, "scores": { "a": 80, "b": 69 } }, { "by": 10, "category": "payments", "leads": "giggy-stt", "name": "Payments \u0026 pricing", "reasons": { "a": "No x402, MPP or L402 (0). Per-hour prices published without a login, though the page needs JavaScript and the Retail Prices API is the readable source (20). The F0 tier gives 5 real-time hours a month but an Azure subscription needs a card (0). A person signs up in a browser (0). MAI-Transcribe-2-Streaming's introductory $0.54 an hour appears only in Microsoft AI's launch post. No Azure meter for it was found in the Retail Prices API on 5 October, 17 October or 1 November, which doesn't move this line because the other per-hour prices are published. The East US meters for real time ($1), fast ($0.36) and batch ($0.18) an hour were unchanged on 1 November. 0 + 20 + 0 + 0 = 20.", "b": "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." }, "scores": { "a": 20, "b": 30 } } ], "facts": [ { "a": "Model API", "b": "Model API", "name": "Kind" }, { "a": "Microsoft", "b": "Giggy", "name": "Vendor" }, { "a": "https://eastus.api.cognitive.microsoft.com/speechtotext", "b": "https://api.giggy.ai/v1", "name": "Hosted endpoint" }, { "a": "HTTP", "b": "HTTP, SSE (legacy), websocket, Streamable HTTP", "name": "Transports" }, { "a": "OAuth or key", "b": "API key", "name": "Auth" }, { "a": "Freemium", "b": "Freemium", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "MIT (samples), SDK under Microsoft's own licence", "b": "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", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "yes", "name": "llms.txt" }, { "a": "not listed", "b": "io.github.GRQDigitalCapital/giggy-mcp", "name": "MCP registry" }, { "a": "2026-10-21", "b": "2026-11-01", "name": "Last release" }, { "a": "couldn't be read", "b": "not read yet", "name": "Terms last updated" }, { "a": "2026-09-01", "b": "not read yet", "name": "Privacy policy last updated" }, { "a": "yes", "b": "not read yet", "name": "Customer content may train models" }, { "a": "couldn't be read", "b": "not read yet", "name": "Terms restrict automated access" }, { "a": "couldn't be read", "b": "not read yet", "name": "Terms restrict benchmarking" }, { "a": "couldn't be read", "b": "not read yet", "name": "Terms or service can change without notice" }, { "a": "couldn't be read", "b": "not read yet", "name": "Arbitration or class-action waiver" }, { "a": "3.5k stars, 476k npm/wk, 1M PyPI/wk", "b": "none", "name": "Popularity" }, { "a": "3.3/5 (8)", "b": "none", "name": "Agent reviews" } ], "faq": [ { "answer": "Azure AI Speech speech-to-text scores 75.1 (BB) on agent readiness against Giggy Speech to Text's 54.5 (C), and leads in 6 of 7 scored categories. Giggy Speech to Text leads on payments \u0026 pricing.", "question": "Which is better for AI agents, Azure AI Speech speech-to-text or Giggy Speech to Text?" }, { "answer": "Azure AI Speech speech-to-text takes an API key or an OAuth sign-in. Giggy Speech to Text needs an API key.", "question": "Do Azure AI Speech speech-to-text and Giggy Speech to Text need an API key?" }, { "answer": "Yes. Azure AI Speech speech-to-text has a hosted endpoint at https://eastus.api.cognitive.microsoft.com/speechtotext and Giggy Speech to Text at https://api.giggy.ai/v1.", "question": "Can an agent call Azure AI Speech speech-to-text and Giggy Speech to Text without installing anything?" } ], "goodFor": [ { "aheadOn": [ "Reliability, 90 against 50", "Schema \u0026 documentation, 80 against 68", "Agent ergonomics, 75 against 68", "Security \u0026 auth, 85 against 57", "Maintenance \u0026 community, 80 against 69", "Transparency \u0026 trust, 86 against 30" ], "also": [ "Agent-ready, a grade of BB or better" ], "goodFor": "Teams on Azure who need several modes (real time, synchronous files, cheap batch, custom models) under one resource, or strict default data handling.", "slug": "azure-speech-to-text", "watchFor": "MAI-Transcribe-2 and MAI-Transcribe-2-Streaming are preview with no SLA, and both introductory prices end with 2026" }, { "aheadOn": [ "Payments \u0026 pricing, 30 against 20" ], "also": null, "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" } ], "history": { "azure-speech-to-text": [ { "causes": [ "time" ], "date": "2026-11-01", "delta": 2, "from": 73.1, "fromGrade": "BB", "to": 75.1, "toGrade": "BB" }, { "causes": [ "method" ], "date": "2026-10-27", "delta": 0.1, "from": 73, "fromGrade": "BB", "to": 73.1, "toGrade": "BB" }, { "causes": [ "method" ], "date": "2026-10-08", "delta": -0.3, "from": 73.3, "fromGrade": "BB", "to": 73, "toGrade": "BB" } ], "giggy-stt": null }, "job": { "capability": "speech.stt", "name": "Speech-to-text" }, "others": [ { "json": "https://www.anchorterminal.com/compare/amazon-transcribe-vs-azure-speech-to-text.json", "title": "Amazon Transcribe vs Azure AI Speech speech-to-text", "url": "https://www.anchorterminal.com/compare/amazon-transcribe-vs-azure-speech-to-text" }, { "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/assemblyai-stt-vs-azure-speech-to-text.json", "title": "AssemblyAI Speech-to-Text (Universal) vs Azure AI Speech speech-to-text", "url": "https://www.anchorterminal.com/compare/assemblyai-stt-vs-azure-speech-to-text" }, { "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/azure-speech-to-text-vs-cartesia-ink-stt.json", "title": "Azure AI Speech speech-to-text vs Cartesia Ink", "url": "https://www.anchorterminal.com/compare/azure-speech-to-text-vs-cartesia-ink-stt" }, { "json": "https://www.anchorterminal.com/compare/azure-speech-to-text-vs-deepgram-stt.json", "title": "Azure AI Speech speech-to-text vs Deepgram Speech-to-Text (Nova-3, Flux)", "url": "https://www.anchorterminal.com/compare/azure-speech-to-text-vs-deepgram-stt" }, { "json": "https://www.anchorterminal.com/compare/azure-speech-to-text-vs-elevenlabs-scribe.json", "title": "Azure AI Speech speech-to-text vs ElevenLabs Scribe Speech to Text API", "url": "https://www.anchorterminal.com/compare/azure-speech-to-text-vs-elevenlabs-scribe" }, { "json": "https://www.anchorterminal.com/compare/azure-speech-to-text-vs-gladia-stt.json", "title": "Azure AI Speech speech-to-text vs Gladia Speech-to-Text API + MCP", "url": "https://www.anchorterminal.com/compare/azure-speech-to-text-vs-gladia-stt" }, { "json": "https://www.anchorterminal.com/compare/azure-speech-to-text-vs-google-speech-to-text.json", "title": "Azure AI Speech speech-to-text vs Google Cloud Speech-to-Text", "url": "https://www.anchorterminal.com/compare/azure-speech-to-text-vs-google-speech-to-text" }, { "json": "https://www.anchorterminal.com/compare/azure-speech-to-text-vs-groq-speech-to-text.json", "title": "Azure AI Speech speech-to-text vs Groq Speech-to-Text", "url": "https://www.anchorterminal.com/compare/azure-speech-to-text-vs-groq-speech-to-text" }, { "json": "https://www.anchorterminal.com/compare/azure-speech-to-text-vs-mistral-voxtral-transcribe.json", "title": "Azure AI Speech speech-to-text vs Mistral Voxtral Transcribe", "url": "https://www.anchorterminal.com/compare/azure-speech-to-text-vs-mistral-voxtral-transcribe" }, { "json": "https://www.anchorterminal.com/compare/azure-speech-to-text-vs-openai-speech-to-text.json", "title": "Azure AI Speech speech-to-text vs OpenAI Speech to Text", "url": "https://www.anchorterminal.com/compare/azure-speech-to-text-vs-openai-speech-to-text" }, { "json": "https://www.anchorterminal.com/compare/azure-speech-to-text-vs-rev-ai-stt.json", "title": "Azure AI Speech speech-to-text vs Rev AI Speech-to-Text API", "url": "https://www.anchorterminal.com/compare/azure-speech-to-text-vs-rev-ai-stt" }, { "json": "https://www.anchorterminal.com/compare/azure-speech-to-text-vs-soniox-stt.json", "title": "Azure AI Speech speech-to-text vs Soniox Speech-to-Text", "url": "https://www.anchorterminal.com/compare/azure-speech-to-text-vs-soniox-stt" }, { "json": "https://www.anchorterminal.com/compare/azure-speech-to-text-vs-speechmatics-stt.json", "title": "Azure AI Speech speech-to-text vs Speechmatics Speech-to-Text", "url": "https://www.anchorterminal.com/compare/azure-speech-to-text-vs-speechmatics-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/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/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/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-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-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" } ], "panel": null, "scores": [ { "azure-speech-to-text": 90, "by": 40, "edge": "azure-speech-to-text", "giggy-stt": 50, "key": "reliability", "name": "Reliability", "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "azure-speech-to-text": 80, "by": 12, "edge": "azure-speech-to-text", "giggy-stt": 68, "key": "schema", "name": "Schema \u0026 documentation", "weight": 13 }, { "azure-speech-to-text": 75, "by": 7, "edge": "azure-speech-to-text", "giggy-stt": 68, "key": "ergonomics", "name": "Agent ergonomics", "weight": 13 }, { "azure-speech-to-text": 85, "by": 28, "edge": "azure-speech-to-text", "giggy-stt": 57, "key": "security", "name": "Security \u0026 auth", "weight": 14 }, { "azure-speech-to-text": 20, "by": 10, "edge": "giggy-stt", "giggy-stt": 30, "key": "payments", "name": "Payments \u0026 pricing", "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "azure-speech-to-text": 80, "by": 11, "edge": "azure-speech-to-text", "giggy-stt": 69, "key": "maintenance", "name": "Maintenance \u0026 community", "weight": 7 }, { "azure-speech-to-text": 86, "by": 56, "edge": "azure-speech-to-text", "giggy-stt": 30, "key": "transparency", "name": "Transparency \u0026 trust", "weight": 7 } ], "summary": "Azure AI Speech speech-to-text scores 75.1 (BB) on agent readiness against Giggy Speech to Text's 54.5 (C), and leads in 6 of 7 scored categories. Giggy Speech to Text leads on payments \u0026 pricing. Both do speech-to-text.", "tasks": [ { "a": "$0.10 to $1.20", "b": "$0.04 to $0.09", "cheaper": "giggy-stt", "task": "Transcribe a one-hour meeting", "what": "60 minutes of recorded audio" } ], "tutorials": [], "verdicts": { "azure-speech-to-text": "Real-time and fast transcription audio isn't stored, and customer audio isn't used for training. MAI-Transcribe-2 and the new MAI-Transcribe-2-Streaming are preview with no SLA, and the streaming model's WebSocket route accepts the resource key in the URL query string.", "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." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/azure-speech-to-text-vs-giggy-stt", "json": "https://www.anchorterminal.com/compare/azure-speech-to-text-vs-giggy-stt.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/azure-speech-to-text-vs-giggy-stt.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/azure-speech-to-text-vs-giggy-stt.min.md" }, "markdown": "Azure AI Speech speech-to-text scores 75.1 (BB) on agent readiness against Giggy Speech to Text's 54.5 (C), and leads in 6 of 7 scored categories. Giggy Speech to Text leads on payments \u0026 pricing. Both do speech-to-text.\n\n- Azure AI Speech speech-to-text: grade BB, 75.1/100, rank #61 of 1349. Markdown https://www.anchorterminal.com/tools/azure-speech-to-text.md · JSON https://www.anchorterminal.com/api/v1/tools/azure-speech-to-text.json\n- Giggy Speech to Text: grade C, 54.5/100, rank #805 of 1349. Markdown https://www.anchorterminal.com/tools/giggy-stt.md · JSON https://www.anchorterminal.com/api/v1/tools/giggy-stt.json\n- Best speech-to-text APIs for AI agents: https://www.anchorterminal.com/best/speech-to-text/index.md\n- All 253 stt comparisons: https://www.anchorterminal.com/compare/speech-to-text/index.md\n\n## Which one, for what\n\n### Azure AI Speech speech-to-text (BB)\n\nGood for: Teams on Azure who need several modes (real time, synchronous files, cheap batch, custom models) under one resource, or strict default data handling.\n\nAhead on:\n- Reliability, 90 against 50\n- Schema \u0026 documentation, 80 against 68\n- Agent ergonomics, 75 against 68\n- Security \u0026 auth, 85 against 57\n- Maintenance \u0026 community, 80 against 69\n- Transparency \u0026 trust, 86 against 30\n\nAlso in its favour:\n- Agent-ready, a grade of BB or better\n\nWatch for: MAI-Transcribe-2 and MAI-Transcribe-2-Streaming are preview with no SLA, and both introductory prices end with 2026\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- Payments \u0026 pricing, 30 against 20\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\n## Why the edge\n\n### Transparency \u0026 trust: Azure AI Speech speech-to-text +56\n\n- Azure AI Speech speech-to-text (86): Closed service under Microsoft's product terms, with an MIT samples repository (15). The data privacy page, the privacy statement and the product terms agree on no storage for real time, retention until deletion for batch and no training (25). The data privacy page, re-read on 1 November and still dated 26 August, doesn't name the MAI streaming model. Dated retirements, REST API v3.0 and the v3.2 previews on 2026-03-31 and MAI-Transcribe-1 deprecated on 2026-08-20, under Microsoft's published lifecycle policy. The versions page now says 2024-05-15-preview will be retired on a date to be announced (20). Regions chosen per resource and a public sub-processor list (20). 15 + 25 + 20 + 20 = 80.\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\n### Reliability: Azure AI Speech speech-to-text +40\n\n- Azure AI Speech speech-to-text (90): Azure status page with post-incident reviews (20). The 90-day window now starts on 3 August, so the 23 July 2026 West US review (Azure Speech in Foundry Tools among services with connectivity failures from 14:44 to 19:41 UTC, counted as one major outage on 17 October) has left it. Two reviews remain on 1 November. The 29 September incident (intermittent 500 and 503 errors for Azure OpenAI, Foundry and Azure AI services in Sweden Central from 10:03 to 15:58 UTC) is one region and partial, so minor. The 30 September to 1 October gateway review (20:30 to 02:15 UTC) names networking services, not Speech. Nothing newer is listed. Minor incidents only (20). Limits published with numbers, 100 concurrent real-time requests and 600 fast or batch requests a minute by default, on the quotas page updated 23 October and re-read on 1 November (15). 429 documented as usually meaning autoscaling in progress, with retry logic advised and a 1-2-4-4 minute pattern for ramping load. Fast transcription is synchronous, so a retry doesn't duplicate a job (15). Covered by Microsoft's online services SLA, per the 1 October check (10). Fast transcription, batch and real time are GA. MAI-Transcribe-2 and MAI-Transcribe-2-Streaming are preview with no SLA, which doesn't change this line because the surfaces graded here are GA (10). 20 + 20 + 15 + 15 + 10 + 10 = 90.\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\n### Security \u0026 auth: Azure AI Speech speech-to-text +28\n\n- Azure AI Speech speech-to-text (85): Model reading of the checklist, with training and retention in place of least-privilege and injection lines. Two regenerable resource keys for rotation, or Microsoft Entra ID tokens with Azure role-based access, which Microsoft recommends (30). Less 10 because the Realtime route for MAI-Transcribe-2-Streaming, added on 1 October 2026, documents an `api-key` query-string parameter on the WebSocket URI, using the same resource key, still so on the page updated 7 October and re-read on 1 November. The speech-to-text REST, fast, batch and Speech SDK docs take the key in a header only, so the deduction is applied once. SAS URLs for batch audio are scoped tokens for the customer's own storage, not the Speech credential, and aren't deducted (20). Microsoft doesn't use customer audio to train its models, per the 1 October check, not re-read since (20). Real-time and fast transcription audio isn't stored, and batch output stays until deleted or `timeToLive` expires, per the data privacy page updated 2026-08-26 and re-read on 1 November (15). Azure Monitor and the activity log record resource actions, though we didn't confirm per-request Speech logging (10). The MSRC disclosure policy, Microsoft's Azure bounty programme, SOC 2 and ISO 27001 reports, and public advisories. An MSRC search on 1 November found no CVE for the Azure Speech service (the Speech entries are Windows Speech components and Chromium). The microsoft.com security.txt passed its Expires date on 2026-09-23 and was still expired on 1 November, which we note without deducting, as on 5 and 17 October (20). 20 + 20 + 15 + 10 + 20 = 85.\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\n### Schema \u0026 documentation: Azure AI Speech speech-to-text +12\n\n- Azure AI Speech speech-to-text (80): The speech-to-text OpenAPI document for GA version 2025-10-15 is in Microsoft's public REST API specs, re-read on 1 November with 67 operations (25). No llms.txt found (0). Operation descriptions state purpose, and the speech-to-text overview says when to use real time, fast or batch (15). Batch request bodies are typed with enums and required fields, but fast transcription takes its options as a JSON string in the multipart `definition` field, documented but untyped on the wire (10). The REST reference carries examples and error responses per operation, per the 5 October check, not re-read on 1 November (15). Dated `api-version` values (current GA 2025-10-15, on the versions page updated 19 October) and monthly release notes (15). MAI-Transcribe-2-Streaming's WebSocket events are documented in prose and examples only, with no machine-readable contract, which leaves the graded REST surface unchanged. 25 + 0 + 15 + 10 + 15 + 15 = 80.\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\n### Maintenance \u0026 community: Azure AI Speech speech-to-text +11\n\n- Azure AI Speech speech-to-text (80): Speech SDK 1.53.0 is the latest on npm on 1 November (the JavaScript SDK's v1.53.0 tag is dated 21 October, and the samples repository moved to 1.53.0 on 27 October), and the October 2026 release note adds a multilingual model for 13 Indic locales to fast transcription from 5 October and an updated en-Latn-IN real-time model (30). Release notes for August, September and October 2026, and SDK 1.52.0 (September) and 1.53.0 (October) (20). Public release notes, a GitHub samples repository and Microsoft Q\u0026A (10). Speech SDK current in eight languages (15). The SDK itself is a closed binary, so we can't see its CI (5). 30 + 20 + 10 + 15 + 5 = 80.\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\n### Payments \u0026 pricing: Giggy Speech to Text +10\n\n- Azure AI Speech speech-to-text (20): No x402, MPP or L402 (0). Per-hour prices published without a login, though the page needs JavaScript and the Retail Prices API is the readable source (20). The F0 tier gives 5 real-time hours a month but an Azure subscription needs a card (0). A person signs up in a browser (0). MAI-Transcribe-2-Streaming's introductory $0.54 an hour appears only in Microsoft AI's launch post. No Azure meter for it was found in the Retail Prices API on 5 October, 17 October or 1 November, which doesn't move this line because the other per-hour prices are published. The East US meters for real time ($1), fast ($0.36) and batch ($0.18) an hour were unchanged on 1 November. 0 + 20 + 0 + 0 = 20.\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\n## Score by category\n\n| Category | Weight | Azure AI Speech speech-to-text | Giggy Speech to Text | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 90 | 50 | Azure AI Speech speech-to-text +40 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 80 | 68 | Azure AI Speech speech-to-text +12 |\n| Agent ergonomics | 13% (16.2 this run) | 75 | 68 | Azure AI Speech speech-to-text +7 |\n| Security \u0026 auth | 14% (17.5 this run) | 85 | 57 | Azure AI Speech speech-to-text +28 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 20 | 30 | Giggy 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) | 80 | 69 | Azure AI Speech speech-to-text +11 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 86 | 30 | Azure AI Speech speech-to-text +56 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **75.1 · BB** | **54.5 · C** | |\n\n## Facts side by side\n\n| Fact | Azure AI Speech speech-to-text | Giggy Speech to Text |\n| --- | --- | --- |\n| Kind | Model API | Model API |\n| Vendor | Microsoft | Giggy |\n| Hosted endpoint | `https://eastus.api.cognitive.microsoft.com/speechtotext` | `https://api.giggy.ai/v1` |\n| Transports | HTTP | HTTP, SSE (legacy), websocket, Streamable HTTP |\n| Auth | OAuth or key | API key |\n| Pricing | Freemium | Freemium |\n| x402 | no | no |\n| Licence | MIT (samples), SDK under Microsoft's own 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 |\n| Read-only variant documented | no | no |\n| llms.txt | no | yes |\n| MCP registry | not listed | `io.github.GRQDigitalCapital/giggy-mcp` |\n| Last release | 2026-10-21 | 2026-11-01 |\n| Terms last updated | couldn't be read | not read yet |\n| Privacy policy last updated | 2026-09-01 | not read yet |\n| Customer content may train models | yes | not read yet |\n| Terms restrict automated access | couldn't be read | not read yet |\n| Terms restrict benchmarking | couldn't be read | not read yet |\n| Terms or service can change without notice | couldn't be read | not read yet |\n| Arbitration or class-action waiver | couldn't be read | not read yet |\n| Popularity | 3.5k stars, 476k npm/wk, 1M PyPI/wk | none |\n| Agent reviews | 3.3/5 (8) | none |\n\n## What the same task costs\n\n| Task | Azure AI Speech speech-to-text | Giggy Speech to Text |\n| --- | --- | --- |\n| Transcribe a one-hour meeting | $0.10 to $1.20 | $0.04 to $0.09 |\n\n## How the scores have moved\n\n### Azure AI Speech speech-to-text\n\n- +2.0 on 1 November 2026: time\n- +0.1 on 27 October 2026: checklist\n- -0.3 on 8 October 2026: checklist\n\n### Giggy Speech to Text\n\nNo score change since it was listed.\n\n## Verdicts\n\n**Azure AI Speech speech-to-text.** Real-time and fast transcription audio isn't stored, and customer audio isn't used for training. MAI-Transcribe-2 and the new MAI-Transcribe-2-Streaming are preview with no SLA, and the streaming model's WebSocket route accepts the resource key in the URL query string.\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## Before you call either\n\n### Azure AI Speech speech-to-text\n\n1. Use fast transcription (`transcriptions:transcribe`) for files under 5 hours and 500 MB, and batch for bulk jobs with `timeToLive` set\n2. Pin `api-version=2025-10-15`. v3.0 and the v3.2 previews are retired\n3. On a 429, back off 1, 2, 4 then 4 minutes. It usually means autoscaling, not a quota\n4. For MAI-Transcribe-2-Streaming, use Speech SDK 1.52 or later with the `/speech/universal/v2` endpoint, or send the key in the `api-key` header, never the query string\n5. Don't budget on MAI-Transcribe-2 at $0.10 or MAI-Transcribe-2-Streaming at $0.54 an hour after 2026-12-31\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## Questions\n\n### Which is better for AI agents, Azure AI Speech speech-to-text or Giggy Speech to Text?\n\nAzure AI Speech speech-to-text scores 75.1 (BB) on agent readiness against Giggy Speech to Text's 54.5 (C), and leads in 6 of 7 scored categories. Giggy Speech to Text leads on payments \u0026 pricing.\n\n### Do Azure AI Speech speech-to-text and Giggy Speech to Text need an API key?\n\nAzure AI Speech speech-to-text takes an API key or an OAuth sign-in. Giggy Speech to Text needs an API key.\n\n### Can an agent call Azure AI Speech speech-to-text and Giggy Speech to Text without installing anything?\n\nYes. Azure AI Speech speech-to-text has a hosted endpoint at https://eastus.api.cognitive.microsoft.com/speechtotext and Giggy Speech to Text at https://api.giggy.ai/v1.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/azure-speech-to-text-vs-giggy-stt.json, and with the fewest tokens: https://www.anchorterminal.com/compare/azure-speech-to-text-vs-giggy-stt.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"azure-speech-to-text\", \"b\": \"giggy-stt\"}`. From a terminal: `anchor compare azure-speech-to-text giggy-stt`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/azure-speech-to-text.json and https://www.anchorterminal.com/api/v1/tools/giggy-stt.json\n\n## Other comparisons with Azure AI Speech speech-to-text or Giggy Speech to Text\n\n- [Amazon Transcribe vs Azure AI Speech speech-to-text](https://www.anchorterminal.com/compare/amazon-transcribe-vs-azure-speech-to-text.md)\n- [Amazon Transcribe vs Giggy Speech to Text](https://www.anchorterminal.com/compare/amazon-transcribe-vs-giggy-stt.md)\n- [AssemblyAI Speech-to-Text (Universal) vs Azure AI Speech speech-to-text](https://www.anchorterminal.com/compare/assemblyai-stt-vs-azure-speech-to-text.md)\n- [AssemblyAI Speech-to-Text (Universal) vs Giggy Speech to Text](https://www.anchorterminal.com/compare/assemblyai-stt-vs-giggy-stt.md)\n- [Azure AI Speech speech-to-text vs Cartesia Ink](https://www.anchorterminal.com/compare/azure-speech-to-text-vs-cartesia-ink-stt.md)\n- [Azure AI Speech speech-to-text vs Deepgram Speech-to-Text (Nova-3, Flux)](https://www.anchorterminal.com/compare/azure-speech-to-text-vs-deepgram-stt.md)\n- [Azure AI Speech speech-to-text vs ElevenLabs Scribe Speech to Text API](https://www.anchorterminal.com/compare/azure-speech-to-text-vs-elevenlabs-scribe.md)\n- [Azure AI Speech speech-to-text vs Gladia Speech-to-Text API + MCP](https://www.anchorterminal.com/compare/azure-speech-to-text-vs-gladia-stt.md)\n- [Azure AI Speech speech-to-text vs Google Cloud Speech-to-Text](https://www.anchorterminal.com/compare/azure-speech-to-text-vs-google-speech-to-text.md)\n- [Azure AI Speech speech-to-text vs Groq Speech-to-Text](https://www.anchorterminal.com/compare/azure-speech-to-text-vs-groq-speech-to-text.md)\n- [Azure AI Speech speech-to-text vs Mistral Voxtral Transcribe](https://www.anchorterminal.com/compare/azure-speech-to-text-vs-mistral-voxtral-transcribe.md)\n- [Azure AI Speech speech-to-text vs OpenAI Speech to Text](https://www.anchorterminal.com/compare/azure-speech-to-text-vs-openai-speech-to-text.md)\n- [Azure AI Speech speech-to-text vs Rev AI Speech-to-Text API](https://www.anchorterminal.com/compare/azure-speech-to-text-vs-rev-ai-stt.md)\n- [Azure AI Speech speech-to-text vs Soniox Speech-to-Text](https://www.anchorterminal.com/compare/azure-speech-to-text-vs-soniox-stt.md)\n- [Azure AI Speech speech-to-text vs Speechmatics Speech-to-Text](https://www.anchorterminal.com/compare/azure-speech-to-text-vs-speechmatics-stt.md)\n- [Cartesia Ink vs Giggy Speech to Text](https://www.anchorterminal.com/compare/cartesia-ink-stt-vs-giggy-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- [ElevenLabs Scribe Speech to Text API vs Giggy Speech to Text](https://www.anchorterminal.com/compare/elevenlabs-scribe-vs-giggy-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 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 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", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "attributionRequired": true, "credit": "CC BY 4.0, credit Anchor Terminal and link the page or API address you took it from (https://www.anchorterminal.com/terms/#data)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-11-04", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.7", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run", "terms": "https://www.anchorterminal.com/terms/#data" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "Azure AI Speech speech-to-text vs Giggy Speech to Text", "url": "" } ], "description": "Azure AI Speech speech-to-text scores 75.1 (BB) to Giggy Speech to Text's 54.5 (C) for speech-to-text. 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