Hyperbolic
$1 for new accounts; it does not recur.
https://api.hyperbolic.xyz/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 one-time signup credit. No period normalization is shown because no expiry is captured.
Limits and terms
- Signup Credit USD
- 1
- Recurrence
- No
- Requests Per Minute
- 60
- Per Ip Requests Per Minute
- 600
- Credit And Rate Source Conflict
- An indexed version of Performance and Limits states $1 promotional credit after phone verification, while its current direct rendering retains the 60 RPM Basic tier but omits that credit sentence.An indexed version of the inference overview says $5 free credits; its current direct rendering omits that sentence.The On-Demand GPU overview advertises $10 signup credit, but that page covers GPU infrastructure and does not clearly establish the inference-API balance.The current quickstart says 100 RPM for the Free tier and requires billing information, while the more specific current Performance and Limits table says 60 RPM and $0 minimum deposit for Basic.
- Conservative Interpretation
- Retain $1 one-time and 60 RPM until an authenticated dashboard confirms the balance; do not increase value from the conflicting $5 or GPU-specific $10 copy.
What happens to your prompts?
The AI FAQ promises transient no-training inference, but feedback storage, node operators, and a broader content license remain.
- Plan Scope
- Hyperbolic hosted AI inference and gateway; GPU marketplace suppliers and third-party AI tools can create independently governed processing.
- Prompt Retention
- Hyperbolic's AI Privacy FAQ says inference input is held only temporarily and discarded after the task. Feedback ratings cause the related conversation to be stored.
- Response Retention
- Outputs are not retained after ordinary inference according to the FAQ; rated conversations and other deliberately saved content are exceptions.
- Ordinary Logging
- Account, device, IP, usage, session replay, security, and marketplace data is logged under purpose-based retention, separate from inference payloads.
- Model Training
- The terms and FAQ state Hyperbolic does not use inputs or outputs to train generative AI models.
- Product Improvement
- The general Content license permits use for operating and improving services, while the narrower AI FAQ says inference data is used only for the request. Feedback and de-identified operational data can improve the service.
- Human Or Operator Access
- The terms reserve monitoring, review, and investigation rights and say users have no expectation of privacy in transmissions; the FAQ says ordinary inference data is not stored. Feedback and investigations are access exceptions.
- Subprocessors And Routing
- Inference nodes receive input, and third-party AI tools or marketplace suppliers may apply their own retention and training terms.
- Deletion Controls
- Ordinary inference content is automatically discarded. Users can delete certain account content, while public or shared content may not be comprehensively removable.
- Caveat
- The broad monitoring and improvement language in the binding terms is less protective than the product FAQ. Third-party AI tools are expressly outside Hyperbolic's no-training commitment.
Governing documents
- Terms Of Use
- https://www.hyperbolic.ai/terms
- Privacy Policy
- https://www.hyperbolic.ai/privacy
Eligibility
- Account Required
- Yes
- Phone Verification Required
- Yes
- Payment Method Required For Basic Tier
- No
Primary sources
Before you build with Hyperbolic
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
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.hyperbolic.xyz/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 “Partially private”: The AI FAQ promises transient no-training inference, but feedback storage, node operators, and a broader content license 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 11 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.