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There is no meaningful single ranking of speech-to-text API rate limits: OpenAI publishes model-tier RPM and TPM, Google Cloud publishes project- and region-scoped request limits, Azure Speech sets resource-scoped quotas, and Amazon Transcribe separates per-operation TPS from concurrency. Compare the limit that matches your workload—short file requests, batch submissions, or live streams—and verify the quota for your own account and region before sizing capacity.
What do RPM, TPM, TPS, and concurrency mean?
- RPM means requests per minute. It limits how many API requests can be sent during a minute, not how much audio those requests contain.
- TPM means tokens per minute. It is a model usage measure, not a count of transcription jobs or audio minutes.
- TPS means transactions per second for a named API operation.
- Concurrency is the number of requests, jobs, or sessions that can be active at the same time. A concurrent stream can stay open, while a submitted batch job may process a much longer recording.
These measures describe different constraints. None, on its own, tells you how many minutes of audio a service will transcribe per minute; that also depends on the request type and content limits.
What are the published limits for each provider?
The figures below are documentation values, not universal capacity guarantees. Their scopes differ, so compare only matching workload modes and quota units. Provider limits can change; check the live quota page, console, or support channel for your own configuration.
| Provider and workload | Published request or usage limit | Published concurrency | Scope and qualification |
|---|---|---|---|
| OpenAI GPT-Transcribe | Tier 1: 500 RPM and 200,000 TPM Tier 2: 5,000 RPM and 2,000,000 TPM Tier 3: 5,000 RPM and 4,000,000 TPM Tier 4: 10,000 RPM and 10,000,000 TPM Tier 5: 30,000 RPM and 150,000,000 TPM |
No concurrency figure is listed in the model limits table. | Model limits depend on usage tier; Free is unsupported for this model. OpenAI says tiers increase automatically as requests and spend increase. These figures do not establish audio-job throughput. See OpenAI’s GPT-Transcribe documentation. |
| Google Cloud Speech-to-Text synchronous recognition | 300 requests per 60 seconds per region | Not stated for this request class. | Limits apply per developer project and are shared by applications and IP addresses using that project. |
| Google Cloud Speech-to-Text batch recognition | 150 requests per 60 seconds per region | Not stated for this request class. | Limits apply per developer project and are shared by applications and IP addresses using that project. |
| Google Cloud Speech-to-Text resource and operation requests | 100 resource requests per 60 seconds per region; 150 operation requests per 60 seconds per region | Not stated for these request classes. | Project-scoped limits shared across applications and IP addresses. These request categories are distinct from the streaming session concurrency cap. |
| Google Cloud Speech-to-Text streaming | 3,000 requests per minute shared across streaming sessions; initial session configuration does not count toward this request quota | 300 concurrent sessions | Per developer project and region; apps and IPs using a project share its quota. Google’s quota page says values may change and was last updated 2026-09-30 UTC. See Google Cloud’s quota documentation. |
| Azure Speech real-time speech-to-text, Standard S0 | No RPM figure stated here for real-time requests. | Default 100 for the base model endpoint and 100 for a custom endpoint | Per Speech resource. Real-time speech-to-text and speech translation concurrency are combined. The existing concurrency value is not visible in the portal, CLI, or API; contact support to verify it. |
| Azure Speech real-time speech-to-text, Free F0 | No RPM figure stated here. | 1 | Per Speech resource. See Microsoft’s Azure Speech quotas and limits for current quota details. |
| Azure Speech fast and batch transcription, Standard S0 | 600 requests per minute shared by fast transcription and batch transcription | Not stated as a separate concurrency value here. | Per Speech resource. Azure says the shared fast/batch rate can be adjusted; other batch constraints are not adjustable. |
Amazon Transcribe job submission: StartTranscriptionJob |
25 transactions per second | 250 concurrent transcription jobs | TPS applies in each supported Region; the concurrent-job quota is separate. Check Service Quotas for the relevant AWS account and region to confirm whether the quota can be adjusted. |
Amazon Transcribe streaming: StartStreamTranscription |
25 transactions per second | 25 concurrent HTTP/2 and WebSocket streams | TPS applies in each supported Region; stream concurrency is a separate quota. Check Service Quotas for the relevant AWS account and region. See AWS’s Amazon Transcribe endpoints and quotas. |
Why request limits are not the same as audio capacity
A request quota governs submissions or API operations; concurrency governs active work. A batch submission might represent one request even when its audio is long, whereas a real-time stream occupies a session while it remains open. Payload and duration limits are a separate constraint again.
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Google’s documented content limits illustrate the distinction: synchronous recognition accepts up to 10 MB or one minute of audio; a streaming session can remain open for five minutes with audio sent near real time; and batch recognition accepts up to five files per request, with each file up to eight hours. These request-shape limits do not replace Google’s request-rate or concurrent-session quotas. See Google Cloud’s quota and limit documentation.
How should you size a transcription workload?
- Choose the workload mode. Separate short synchronous calls, batch job submissions, and live streams. Do not use a streaming concurrency figure to estimate batch submission capacity, or vice versa.
- Identify the exact quota scope. Check whether the value applies to a model tier, developer project, Speech resource, AWS account, or supported region. Shared project or resource limits may be consumed by more than one application.
- Check the operation and unit. Match the request to the specific model, endpoint, or API operation. Treat RPM, TPM, TPS, and active sessions or jobs as separate ceilings.
- Verify content constraints. Check file size, duration, files-per-request, and stream-duration limits as well as the rate quota. A workload can fit under the request cap and still exceed a content limit.
- Confirm the live quota and adjustability. Consult the provider’s quota page or account console for the project, resource, account, and region you will use. For Azure concurrency, contact support because the existing value is not exposed in the portal, CLI, or API. For AWS, inspect the relevant Service Quotas entry.
- Plan for bursts and shared usage. Keep a queue for work that cannot start immediately, limit concurrent workers to the applicable cap, and use backoff when a request is throttled. Validate the plan with a representative workload; documentation quotas alone do not establish sustained audio throughput.
Can these quotas identify the fastest or most accurate provider?
No. The published limits describe operational ceilings, not a controlled comparison of transcription speed or accuracy. A higher RPM, TPS, or concurrency number alone does not prove that a provider processes more audio per minute or produces better transcripts.
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