--- title: "Speechmatics free inference" description: "$100 for new accounts; it does not recur. See verified limits, model IDs, privacy terms, and primary sources for Speechmatics." canonical_url: "https://freeinferencing.com/provider/speechmatics/" md_url: "https://freeinferencing.com/provider/speechmatics.md" last_updated: "2026-09-19" --- # Speechmatics > $100 for new accounts; it does not recur. ## Classification - **Directory:** Current - **Free access:** One-time trial - **Status:** Current trial - **Confidence:** High - **Payment card:** No - **Account:** Required - **Equivalent paid value:** $100.00 once ## Models mentioned No stable model IDs were recorded in this snapshot. ## Limits and terms ```yaml signup_credit_usd: 100 recurrence: false realtime_concurrent_sessions: 2 batch_jobs_per_second: 1 voice_agent_concurrent_conversations: 3 languages: 55+ ``` ## What happens to your prompts? **Partially private.** Training is opt-in, but batch content is retained for seven days and portal or support features add storage and access. Privacy is audited separately from price. Review the current governing terms before sending sensitive or regulated data. ## Data governance and agreements ```yaml data_governance: review_status: reviewed plan_scope: Speechmatics Cloud speech-to-text and text-to-speech API and portal; batch, real-time, sharing, and explicit model-improvement opt-in differ. prompt_retention: Batch audio, transcripts, and configuration are retained for seven days by default; portal playback and sharing require storage. Real-time processing and negotiated deployments can differ. response_retention: Batch transcripts follow the seven-day default and can be removed by customer action; shared portal content remains available until deleted or the account is removed. ordinary_logging: Account, API, billing, diagnostic, security, and service metadata is retained under the privacy policy; API keys themselves are not stored or recorded. model_training: Speechmatics states customer audio is not used to train models unless the customer explicitly opts in. product_improvement: Opted-in audio and transcripts may improve models; otherwise content use is limited to producing outputs, portal playback, customer-directed sharing, support, and legal duties. human_or_operator_access: Authorized employees, affiliates, representatives, and subprocessors can access content as needed to deliver the service under confidentiality and data-processing obligations. subprocessors_and_routing: Speechmatics acts as processor and can use notified equivalent-obligation subprocessors; customer-directed sharing exposes stored audio and transcripts to recipients. deletion_controls: Batch data auto-deletes after seven days and can be deleted earlier; deleting the account removes content. On termination, personal data is returned or destroyed except where law requires retention. caveat: Portal playback and sharing are intentionally stored features. The seven-day default is not zero retention, although model training is opt-in. agreements: terms_of_service: https://www.speechmatics.com/legal/terms-of-service privacy_policy: https://www.speechmatics.com/legal/privacy-policy ``` ## Eligibility ```yaml account_required: true payment_method_required: false ``` ## API compatibility and modalities - **Compatibility:** Speechmatics Rest And Realtime Websocket - **Modalities:** Speech To Text, Text To Speech, Speech / audio ## Before you build with Speechmatics ### Read the classification narrowly This record describes the bounded offer supported by evidence on 2026-09-19; it does not guarantee permanence, production suitability, uptime, latency, model quality, or access from every account and region. “One-time trial” should be interpreted together with the Current directory placement, High confidence, No payment-card status, and the plan-specific sources below. ### Resolve the live model route No stable model ID is recorded. Resolve the current machine-readable ID, endpoint, authentication method, and request shape from the provider's live documentation before writing fixed production configuration. ### Match the quota to the workload shape Translate the allowance into peak requests per minute, input and output tokens, concurrency, retries, and every daily or monthly ceiling that applies to the intended account. The first limit reached by the workload is the practical ceiling. A large token pool can still fail interactive bursts, and a high request limit can still fail long-context work. Include tool calls and retry traffic, test the largest realistic payload, and treat research, experimental, and community access as conditional on their eligibility and fair-use terms. ### Preserve billing and privacy boundaries The recorded payment-card requirement is No, and account access is required. Confirm both in the live signup flow, set provider-side budgets when charges are possible, and observe the balance or usage fields after a complete request. The prompt-handling classification is **Partially private** because Training is opt-in, but batch content is retained for seven days and portal or support features add storage and access. Re-read the terms for the exact route and plan before sending sensitive, regulated, or proprietary content. ### Follow the evidence, then re-check it This record links 8 first-party sources covering the offer, catalog, limits, pricing, terms, privacy, or adoption evidence available to the audit. Prefer the newest and most specific governing document or live catalog when sources disagree. A dated finding can become stale even when the page remains online, so retain the source and snapshot that supported the decision and submit a correction when a provider changes a material term. ### Plan fallback without policy drift A fallback should preserve modality, context length, streaming, structured output, tools, safety controls, and data terms, not only API syntax. Decide which errors may retry, cap retry storms, and prevent an exhausted free route from silently switching to a billable model. Rotating aliases and free pools can change behavior without changing the endpoint, so retain the response model field and test at least one substitute route before the primary offer becomes unavailable. ### Monitor the offer as a dependency Capture the model ID, response model, rate-limit headers, usage fields, latency, HTTP status, and provider request identifier. Watch authorization failure, quota exhaustion, catalog removal, policy revision, and balance movement as separate failure modes. Re-check the live catalog and governing sources on a schedule proportionate to the workload's importance, and keep an owner and exit path for any production dependency on volatile free capacity. ### Test one complete request before scaling Start with the smallest permitted request using the exact credential, model ID, endpoint, region, and account type intended for deployment. Record the status, headers, usage fields, response model, latency, and dashboard balance movement. Then exercise an invalid model, quota exhaustion, or rate limit so failure is explicit and cannot silently switch to a paid route. Validate streaming, structured output, and tool calls separately because a free model can expose fewer features than its paid counterpart. Keep a budget ceiling outside the application whenever billing is possible, and do not send sensitive data until the observed route matches the reviewed agreement. ## Primary sources - [Speech API pricing](https://www.speechmatics.com/pricing): Official Pricing - [Credit-based billing](https://www.speechmatics.com/company/articles-and-news/moving-to-credit-based-billing): Official Announcement - [Speechmatics Terms of Service](https://www.speechmatics.com/legal/terms-of-service): Official Terms - [Speechmatics Privacy Policy](https://www.speechmatics.com/legal/privacy-policy): Official Privacy - [Speechmatics Cloud data retention](https://legacy.docs.speechmatics.com/en/cloud/introduction): Official Docs - [PyPI download stats for the official speechmatics-rt SDK](https://pypistats.org/api/packages/speechmatics-rt/recent): Package Registry Stats - [speechmatics/speechmatics-python-sdk](https://github.com/speechmatics/speechmatics-python-sdk): Official Repository - [Speechmatics in 2025: the numbers that shaped voice AI (2026-01-07)](https://www.speechmatics.com/company/articles-and-news/speechmatics-in-2025-the-numbers-that-shaped-voice-ais-breakthrough-year): Official Blog ## Sitemap See the full [semantic sitemap](/sitemap.md) for every page and markdown mirror.