--- title: "Arizona State University Research Computing LLM API free inference" description: "A recurring no-cost API allowance with provider-published access terms." canonical_url: "https://freeinferencing.com/provider/asu_rc_llm_api/" md_url: "https://freeinferencing.com/provider/asu_rc_llm_api.md" last_updated: "2026-09-19" --- # Arizona State University Research Computing LLM API > A recurring no-cost API allowance with provider-published access terms. ## Classification - **Directory:** Current - **Free access:** Always-free quota - **Status:** Restricted access - **Confidence:** High - **Payment card:** No - **Account:** Required - **Equivalent paid value:** Not quantifiable - **API endpoint:** `https://openai.rc.asu.edu/v1` ## Models mentioned No stable model IDs were recorded in this snapshot. ## Limits and terms ```yaml numeric_limits: not_public enforcement: HTTP_429_dynamic_fair_access ``` ## What happens to your prompts? **Partially private.** Prompts stay on ASU Research Computing hardware and are not sent to commercial providers, but retention, training, administrator access, and deletion remain undocumented. 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: partial plan_scope: ASU Research Computing OpenAI-compatible LLM API for institutional research and learning. prompt_retention: The guide says prompts remain on ASU Research Computing hardware and are not sent to commercial AI providers, but it publishes no retention maximum. response_retention: not_documented ordinary_logging: Fair-access and usage enforcement is documented, while content and metadata log fields and durations are not. model_training: No public API-specific no-training commitment was found. product_improvement: not_documented human_or_operator_access: Research Computing administrators operate the local service; public documentation does not bound content access. subprocessors_and_routing: Models run on ASU Research Computing hardware rather than commercial AI-provider endpoints. deletion_controls: No per-request deletion control or content-expiry schedule is published. caveat: Local institutional processing is favorable, but missing retention, training, administrator-access, and deletion terms prevent a private classification. ``` ## Eligibility ```yaml account_required: true identity: ASURITE VPN_required_for_key_setup: true eligible_users: ASU faculty, staff, students, and affiliates for research and learning sponsor_rule: students_and_staff_require_faculty_sponsor_for_RC_account payment_method_required: false ``` ## API compatibility and modalities - **Compatibility:** OpenAI Compatible - **Modalities:** Text generation - **Geography:** United States ## Before you build with Arizona State University Research Computing LLM API ### 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. “Always-free quota” 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 Prompts stay on ASU Research Computing hardware and are not sent to commercial providers, but retention, training, administrator access, and deletion remain undocumented. 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 4 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 - [LLM API](https://docs.rc.asu.edu/ai/api/): Official Docs - [AI getting started](https://docs.rc.asu.edu/ai/getting-started/): Official Docs - [Getting access](https://docs.rc.asu.edu/getting-access/): Official Docs - [vLLM service](https://docs.rc.asu.edu/vllm/): Official Docs ## Sitemap See the full [semantic sitemap](/sitemap.md) for every page and markdown mirror.