GroqCloud
A recurring no-cost API allowance covering 13 cataloged models.
https://api.groq.com/openai/v1Models mentioned
canopylabs/orpheus-arabic-saudicanopylabs/orpheus-v1-englishgroq/compoundgroq/compound-minimeta-llama/llama-prompt-guard-2-22mmeta-llama/llama-prompt-guard-2-86mopenai/gpt-oss-120bopenai/gpt-oss-20bopenai/gpt-oss-safeguard-20bqwen/qwen3.6-27bqwen/qwen3.8-27bwhisper-large-v3whisper-large-v3-turboEquivalent paid value
How this was valued: Sum of independently published daily free-model quotas at Groq's current Developer paid rates. Each shared total-token envelope is allocated to costlier output tokens for a deterministic best-case upper bound; Compound systems without a public unit rate are excluded.
Limits and terms
- Scope
- organization
- Caveat
- The signed-in organization Limits page is authoritative if it differs from this public-table snapshot.
What happens to your prompts?
Inputs and outputs are not retained by default or used for training without permission; limited abuse and feature-specific retention exceptions remain.
- Plan Scope
- GroqCloud inference and related API features; offline negotiated terms may supersede the online agreement.
- Prompt Retention
- Inference inputs and outputs are not retained by default. Reliability or abuse logs may retain them up to 30 days; batch files are retained up to 30 days and fine-tuning data until deleted.
- Response Retention
- Same policy as prompts and feature state.
- Ordinary Logging
- Usage metadata is always retained and excludes customer inputs and outputs.
- Model Training
- Groq is not permitted to train or fine-tune models on inputs or outputs without explicit customer permission or instruction.
- Product Improvement
- Customer data use is limited to providing the service, customer instructions, law, reliable operation, and acceptable-use enforcement under the current agreement.
- Human Or Operator Access
- Access may occur as needed for reliable operation, abuse investigation, legal compliance, or customer-instructed features; zero-data-retention controls restrict reliability and abuse access.
- Subprocessors And Routing
- Groq affiliates, subprocessors, and contractors receive limited rights needed to deliver the service; third-party model terms may also apply.
- Deletion Controls
- All customers may enable zero data retention. The agreement calls for deletion of customer data within 30 days after termination, subject to documented or legal exceptions.
- Caveat
- Zero data retention disables features that require stored application state; regional and offline agreements may differ.
Governing documents
- Services Agreement
- https://console.groq.com/docs/legal/services-agreement
- Privacy Policy
- https://groq.com/privacy-policy
- Acceptable Use Policy
- https://console.groq.com/docs/legal/ai-policy
- Data Processing Addendum
- https://console.groq.com/docs/legal/customer-data-processing-addendum
Eligibility
- Account Required
- Yes
- Payment Method Required
- No
Primary sources
Before you build with GroqCloud
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 2026-09-19; it does not guarantee permanence, production suitability, uptime, latency, model quality, or access from every region.
Resolve the live model route
This snapshot records 13 model IDs. Match the exact ID against the provider's live catalog before using it in code; zero-price routes and aliases can rotate while an older documentation page remains online. The recorded base endpoint is https://api.groq.com/openai/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 “Private”: Inputs and outputs are not retained by default or used for training without permission; limited abuse and feature-specific retention exceptions remain. 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 12 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.