# Azure AI Speech speech-to-text vs Giggy Speech to Text (slim)

> Azure AI Speech speech-to-text scores 75.1 (BB) to Giggy Speech to Text's 54.5 (C) for speech-to-text. Prices, MCP, x402, uptime and agent notes side by side.

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- Index: https://www.anchorterminal.com/llms.txt · API: https://www.anchorterminal.com/api/v1/index.json · MCP: https://www.anchorterminal.com/mcp · Updated: 2026-11-04
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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 & pricing. Both do speech-to-text.

- Azure AI Speech speech-to-text: BB 75.1, rank #61 of 1349 · https://www.anchorterminal.com/tools/azure-speech-to-text.min.md
- Giggy Speech to Text: C 54.5, rank #805 of 1349 · https://www.anchorterminal.com/tools/giggy-stt.min.md
- Azure AI Speech speech-to-text, good for Teams on Azure who need several modes (real time, synchronous files, cheap batch, custom models) under one resource, or strict default data handling. Ahead on Reliability, 90 against 50; Schema & documentation, 80 against 68; Agent ergonomics, 75 against 68; Security & auth, 85 against 57; Maintenance & community, 80 against 69; Transparency & trust, 86 against 30. Also Agent-ready, a grade of BB or better.
- Giggy Speech to Text, good for Agents and voice pipelines that need plain transcripts of recorded or live speech at a few cents an hour, or free for clips under 25 minutes, and can convert audio to 16 kHz mono WAV first. Ahead on Payments & pricing, 30 against 20.

| Category | Azure AI Speech speech-to-text | Giggy Speech to Text |
| --- | --- | --- |
| Reliability (16%) | 90 | 50 |
| Performance (10%) | pending | pending |
| Schema & documentation (13%) | 80 | 68 |
| Agent ergonomics (13%) | 75 | 68 |
| Security & auth (14%) | 85 | 57 |
| Payments & pricing (10%) | 20 | 30 |
| Task success (10%) | pending | pending |
| Maintenance & community (7%) | 80 | 69 |
| Transparency & trust (7%) | 86 | 30 |

| Fact (where they differ) | Azure AI Speech speech-to-text | Giggy Speech to Text |
| --- | --- | --- |
| Vendor | Microsoft | Giggy |
| Hosted endpoint | `https://eastus.api.cognitive.microsoft.com/speechtotext` | `https://api.giggy.ai/v1` |
| Transports | HTTP | HTTP, SSE (legacy), websocket, Streamable HTTP |
| Auth | OAuth or key | API key |
| 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 |
| llms.txt | no | yes |
| MCP registry | not listed | `io.github.GRQDigitalCapital/giggy-mcp` |
| Last release | 2026-10-21 | 2026-11-01 |
| Terms last updated | couldn't be read | not read yet |
| Privacy policy last updated | 2026-09-01 | not read yet |
| Customer content may train models | yes | not read yet |
| Terms restrict automated access | couldn't be read | not read yet |
| Terms restrict benchmarking | couldn't be read | not read yet |
| Terms or service can change without notice | couldn't be read | not read yet |
| Arbitration or class-action waiver | couldn't be read | not read yet |
| Popularity | 3.5k stars, 476k npm/wk, 1M PyPI/wk | none |
| Agent reviews | 3.3/5 (8) | none |

Why the edge:
- Transparency & trust, Azure AI Speech speech-to-text +56: 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.); 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.)
- Reliability, Azure AI Speech speech-to-text +40: 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.); 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.)
- Security & auth, Azure AI Speech speech-to-text +28: 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.); 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.)
- Schema & documentation, Azure AI Speech speech-to-text +12: 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.); 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.)
- Maintenance & community, Azure AI Speech speech-to-text +11: 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&A (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.); 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.)
- Payments & pricing, Giggy Speech to Text +10: 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.); 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.)

| Task | Azure AI Speech speech-to-text | Giggy Speech to Text |
| --- | --- | --- |
| Transcribe a one-hour meeting | $0.10 to $1.20 | $0.04 to $0.09 |