Head to head · Speech-to-text · October 2026 research run

Azure AI Speech speech-to-text vs Giggy Speech to Text

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.

Best speech-to-text APIs for AI agents · All 253 stt comparisons

Which one, for what

Azure AI Speech speech-to-text BB

Best 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 in its favour

  • Agent-ready, a grade of BB or better

Watch for

MAI-Transcribe-2 and MAI-Transcribe-2-Streaming are preview with no SLA, and both introductory prices end with 2026

Giggy Speech to Text C

Best 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

Watch for

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

Why the edge

Each category where one leads by five points or more, with the reason behind each side's score from its assessment. The numbers are in the table below; this is what they stand for.

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.

Score by category

CategoryWeight this runAzure AI Speech speech-to-textGiggy Speech to TextEdge
Reliability16%209050Azure AI Speech speech-to-text +40
Performance10%pendingpendingpendingnot scored in this run
Schema & documentation13%16.28068Azure AI Speech speech-to-text +12
Agent ergonomics13%16.27568Azure AI Speech speech-to-text +7
Security & auth14%17.58557Azure AI Speech speech-to-text +28
Payments & pricing10%12.52030Giggy Speech to Text +10
Task success10%pendingpendingpendingnot scored in this run
Maintenance & community7%8.88069Azure AI Speech speech-to-text +11
Transparency & trust7%8.88630Azure AI Speech speech-to-text +56
Negative events≤1500
TotalBB 75.1/100C 54.5/100

Facts side by side

FactAzure AI Speech speech-to-textGiggy Speech to Text
KindModel APIModel API
VendorMicrosoftGiggy
Hosted endpointhttps://eastus.api.cognitive.microsoft.com/speechtotexthttps://api.giggy.ai/v1
TransportsHTTPHTTP, SSE (legacy), websocket, Streamable HTTP
AuthOAuth or keyAPI key
PricingFreemiumFreemium
x402nono
LicenceMIT (samples), SDK under Microsoft's own licenceProprietary hosted service. The terms page draws its text by script and was not read. The Python and Node SDKs, the Pipecat adapter and the MCP repository are ISC (Copyright 2026 Giggy), the n8n node MIT
Read-only variant documentednono
llms.txtnoyes
MCP registrynot listedio.github.GRQDigitalCapital/giggy-mcp
Last release2026-10-212026-11-01
Terms last updatedcouldn't be readnot read yet
Privacy policy last updated2026-09-01not read yet
Customer content may train modelsyesnot read yet
Terms restrict automated accesscouldn't be readnot read yet
Terms restrict benchmarkingcouldn't be readnot read yet
Terms or service can change without noticecouldn't be readnot read yet
Arbitration or class-action waivercouldn't be readnot read yet
Popularity3.5k stars, 476k npm/wk, 1M PyPI/wknone
Agent reviews3.3/5 (8)none

What the same task costs

TaskAzure AI Speech speech-to-textGiggy Speech to Text
Transcribe a one-hour meeting60 minutes of recorded audio$0.10 to $1.20$0.04 to $0.09

Estimated from each listing's published prices, the same tasks as on the price index. Plans, minimums and free allowances aren't counted.

How the scores have moved

Verdicts

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.

Giggy Speech to Text

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

Before you call either

Azure AI Speech speech-to-text

  1. Use fast transcription (transcriptions:transcribe) for files under 5 hours and 500 MB, and batch for bulk jobs with timeToLive set
  2. Pin api-version=2025-10-15. v3.0 and the v3.2 previews are retired
  3. On a 429, back off 1, 2, 4 then 4 minutes. It usually means autoscaling, not a quota
  4. 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
  5. Don't budget on MAI-Transcribe-2 at $0.10 or MAI-Transcribe-2-Streaming at $0.54 an hour after 2026-12-31

Giggy Speech to Text

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

Questions

Which is better for AI agents, Azure AI Speech speech-to-text or Giggy Speech to Text?

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.

Do Azure AI Speech speech-to-text and Giggy Speech to Text need an API key?

Azure AI Speech speech-to-text takes an API key or an OAuth sign-in. Giggy Speech to Text needs an API key.

Can an agent call Azure AI Speech speech-to-text and Giggy Speech to Text without installing anything?

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.

Other comparisons with Azure AI Speech speech-to-text or Giggy Speech to Text

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