--- title: "Hyperbolic free inference" description: "$1 for new accounts; it does not recur. See verified limits, model IDs, privacy terms, and primary sources for Hyperbolic." canonical_url: "https://freeinferencing.com/provider/hyperbolic/" md_url: "https://freeinferencing.com/provider/hyperbolic.md" last_updated: "2026-09-19" --- # Hyperbolic > $1 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:** $1.00 once - **API endpoint:** `https://api.hyperbolic.xyz/v1` ## Models mentioned No stable model IDs were recorded in this snapshot. ## Limits and terms ```yaml signup_credit_usd: 1 recurrence: false 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? **Partially private.** The AI FAQ promises transient no-training inference, but feedback storage, node operators, and a broader content license remain. 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: 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. agreements: terms_of_use: https://www.hyperbolic.ai/terms privacy_policy: https://www.hyperbolic.ai/privacy ``` ## Eligibility ```yaml account_required: true phone_verification_required: true payment_method_required_for_basic_tier: false ``` ## API compatibility and modalities - **Compatibility:** OpenAI Compatible - **Modalities:** Model inference ## Before you build with Hyperbolic ### 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 The AI FAQ promises transient no-training inference, but feedback storage, node operators, and a broader content license remain. 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 11 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 - [Performance and Limits](https://www.hyperbolic.ai/docs/inference/performance-limits): Official Docs - [Inference overview](https://www.hyperbolic.ai/docs/inference/overview): Official Docs - [Getting Started](https://www.hyperbolic.ai/docs/overview/quickstart): Official Docs - [On-Demand GPU overview](https://www.hyperbolic.ai/docs/on-demand/overview): Official Docs - [Billing and payments](https://www.hyperbolic.ai/docs/general/billing-payments): Official Billing Docs - [Hyperbolic Terms of Use](https://www.hyperbolic.ai/terms): Official Terms - [Hyperbolic Privacy Policy](https://www.hyperbolic.ai/privacy): Official Privacy - [Hyperbolic AI Privacy FAQ](https://www.hyperbolic.ai/privacy/faq): Official Docs - [Hyperbolic home page citing 250,000+ builders](https://www.hyperbolic.ai/): Official Product - [Hyperbolic Monthly Recap: April 2025 (195,000+ developers served)](https://www.hyperbolic.ai/blog/monthly-recap-april-2025): Official Blog - [HyperbolicLabs/Hyperbolic-AgentKit repository stars](https://github.com/HyperbolicLabs/Hyperbolic-AgentKit): Code Hosting Stats ## Sitemap See the full [semantic sitemap](/sitemap.md) for every page and markdown mirror.