Deepgram
$200 for new accounts; it does not recur.
https://api.deepgram.com/v1Models mentioned
The offer covers a dynamic catalog, provider-selected route, or model class without stable model IDs in this snapshot.
Equivalent paid value
How this was valued: Face value of the $200 signup balance. Current pricing explicitly says it does not expire, so no artificial daily, weekly, or monthly normalization is shown.
Limits and terms
- Signup Credit USD
- 200
- Expiry
- none
- Recurrence
- No
- Coverage
- All public model endpoints.
- Concurrent Requests
- Speech To Text Rest
- 50
- Speech To Text Websocket
- 150
- Whisper
- 5
- Text To Speech
- 45
- Voice Agent
- 45
- Audio Intelligence
- 10
What happens to your prompts?
Current standard terms grant a perpetual default content license for storage, training, testing, service improvement, and other business purposes; per-request training opt-out is optional.
- Plan Scope
- Deepgram hosted speech and voice APIs; Model Improvement Partnership, request opt-out, Voice Agent history, regional, dedicated, and self-hosted modes differ.
- Prompt Retention
- Current standard terms permit storage under a perpetual content license without a content TTL. Older MIP documentation describes fractional program samples and transient opted-out handling, but that stronger treatment must be confirmed against the account agreement.
- Response Retention
- Outputs fall under the same current perpetual license and unresolved TTL. Voice Agent history, reusable configurations, and other customer resources add feature-specific storage controls.
- Ordinary Logging
- Usage logs and request metadata are retained for up to 90 days in the console; summarized usage can persist longer.
- Model Training
- Current standard Terms Section 3.2 permits training and testing Models by default under a perpetual content license; Section 3.3 provides per-request API training opt-out. Separately signed MIP or enterprise agreements may differ.
- Product Improvement
- Current standard terms also permit service improvement, other product development, and other business purposes with Content. The training opt-out alone does not expressly revoke this broader content-use license.
- Human Or Operator Access
- Licensed content can be processed for training, testing, development, support, and other business purposes. Older MIP materials describe access controls but do not conclusively narrow the current standard license.
- Subprocessors And Routing
- Deepgram hosts its own models, including its hosted Whisper implementation, and offers regional, dedicated, and self-hosted endpoints; Whisper requests are not sent to OpenAI.
- Deletion Controls
- Set mip opt out=true on every applicable request to exercise the documented training opt-out and disable Voice Agent history where needed. Confirm whether the governing account agreement also narrows retention and secondary use; dedicated and self-hosted arrangements differ.
- Conflict
- Older MIP documentation and the separately signed MSA describe voluntary program sampling and stronger opted-out handling, while the current standard self-service terms grant a broad perpetual default content license. The conservative free-tier interpretation follows the current standard terms.
- Caveat
- Send mip opt out=true on every applicable API request, but confirm the account agreement and broader retention/improvement treatment rather than assuming training opt-out revokes all secondary use.
Governing documents
- Terms Of Service
- https://deepgram.com/terms
- Privacy Policy
- https://deepgram.com/privacy
- Master Services Agreement
- https://static.deepgram.com/business/MSA_20240315.pdf
Eligibility
- Account Required
- Yes
- Payment Method Required
- No
Primary sources
Before you build with Deepgram
Read the classification narrowly
This record has current first-party evidence for some zero-cost hosted inference. The label describes the bounded offer supported by evidence on 2026-10-06; it does not guarantee permanence, production suitability, uptime, latency, model quality, or access from every region.
Resolve the live model route
No stable model ID is recorded. The offer may use a dynamic catalog, a provider-selected route, or a model class, so resolve the current machine-readable ID before writing a fixed production configuration. The recorded base endpoint is https://api.deepgram.com/v1, but the provider's current API reference remains authoritative for paths, authentication, and request shape.
Confirm account and billing boundaries
An account is required, so public catalog evidence cannot by itself prove the limits, balance, or billing behavior attached to your workspace. The reviewed evidence explicitly says no payment card is required. Re-check the signup flow because eligibility and billing controls can change after the snapshot. The equivalent paid value shown here prices a documented allowance where possible; it is not cash, guaranteed savings, or protection from overage.
Re-read the prompt policy for your plan
This record classifies the reviewed handling as “Not private”: Current standard terms grant a perpetual default content license for storage, training, testing, service improvement, and other business purposes; per-request training opt-out is optional. Provider policies can distinguish free, paid, enterprise, opted-in, and feature-specific traffic, so verify the governing terms for the exact account and route that will receive your data.
Follow the evidence, then re-check it
The record links 9 first-party sources covering the offer, catalog, limits, pricing, terms, privacy, or adoption evidence available to the audit. Prefer the newest governing document or live catalog when sources disagree, and submit a correction when a provider changes a material term.
Match the quota to the workload shape
Translate the published allowance into the traffic pattern you actually expect instead of comparing headline totals alone. A daily token pool can look generous while a low requests-per-minute or concurrency ceiling blocks interactive bursts; a high request limit can still fail a long-context job when input, output, or per-request tokens are capped. Separate prompt tokens from generated tokens, include retries and tool calls, and test the largest realistic payload. If the provider documents more than one limit window, the tightest window at your peak load is the practical ceiling. Research, experimental, and community access can also carry eligibility or fair-use constraints that cannot be modeled as a simple number.
Plan fallback without changing the rules
A fallback should preserve more than API syntax. Confirm that the substitute route supports the required modality, context length, streaming behavior, structured output, tools, and safety controls, then compare its prompt-retention and training terms. An OpenAI-compatible request shape does not make providers operationally or contractually equivalent. Decide which errors may trigger a retry, cap retry storms, and prevent an exhausted free route from silently switching to a billable model. If deterministic output matters, record the model revision and sampling settings; rotating aliases and free-model pools can change behavior even when the endpoint remains available.
Monitor the offer as a dependency
Capture the model ID, response model field, rate-limit headers, usage fields, latency, HTTP status, and any provider request identifier for each test. Watch for authorization failures, quota exhaustion, catalog removal, policy revisions, and dashboard balance changes as separate failure modes. Re-check the provider’s live catalog and governing pages on a schedule proportionate to the workload’s importance, and keep the dated sources that supported your decision. Free capacity is especially suitable for prototypes, evaluations, fallbacks, and bounded workloads when the application can tolerate change; a production dependency still needs observability, an exit path, and an owner responsible for re-verification.
Test one complete request before scaling
Start with the smallest permitted request using the exact credential, model ID, endpoint, and account type you intend to deploy. Record the HTTP status, response headers, usage fields, latency, and any dashboard balance change. Then exercise the failure path: an invalid model, an exhausted quota, or a rate-limit response should fail clearly without silently switching to a paid route. If the provider supports streaming or tool calls, validate those features separately because a free model can expose a narrower capability set than its paid counterpart. Keep a budget ceiling outside the application whenever billing is possible, and avoid sending sensitive data until the observed route matches the reviewed data agreement.