--- title: "LLM.API free inference" description: "1 model route currently listed at zero price. See verified limits, model IDs, privacy terms, and primary sources for LLM.API." canonical_url: "https://freeinferencing.com/provider/llmapi_ai/" md_url: "https://freeinferencing.com/provider/llmapi_ai.md" last_updated: "2026-09-19" --- # LLM.API > 1 model route currently listed at zero price. ## Classification - **Directory:** Current - **Free access:** Free model routes - **Status:** Available now - **Confidence:** High - **Payment card:** No - **Account:** Required - **Equivalent paid value:** Not quantifiable - **API endpoint:** `https://api.llmapi.ai/v1` ## Models mentioned - `zaya1-8b` ## Limits and terms ```yaml without_purchased_credits: 5 requests per 10 minutes after_adding_credits: 20 requests per minute for free models ``` ## What happens to your prompts? **Partially private.** The gateway defaults to zero retention and no training, but upstream providers are independently governed and optional logging changes retention. 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: LLM.API gateway. The gateway's defaults and optional All Data Mode are separate from each selected AI provider's independent terms. prompt_retention: Gateway default is zero content retention beyond transaction time. Optional All Data Mode retains prompts and outputs for up to 90 days for analytics, semantic caching, and debugging. response_retention: Same gateway policy as prompts; cached responses and All Data Mode create retained content. ordinary_logging: Request metadata is maintained for billing and analytics. Account, billing, fraud, security, and service-improvement data may be processed independently of customer instructions. model_training: LLM.API says it does not train, fine-tune, evaluate, benchmark, or improve models with content processed under its DPA. Upstream AI providers may use inputs or outputs for improvement or training under their own terms. product_improvement: The gateway may use controller-side operational data for service improvement, but its DPA prohibits model improvement with processor content. All Data Mode enables product features using retained content. human_or_operator_access: LLM.API says it does not inspect routed content except where All Data Mode is enabled; authorized personnel processing personal data must be under confidentiality obligations. subprocessors_and_routing: Requests go to an independently governed AI provider that is expressly not treated as LLM.API's subprocessor. Customers must assess and contract with that provider themselves. deletion_controls: All Data Mode can be disabled and its retained content deleted from the dashboard at any time. Upstream deletion and data-subject requests remain provider-specific. caveat: The gateway's zero-content-retention and no-training promises do not bind the final AI provider. The terms prohibit submitting personal, confidential, or third-party material without all necessary rights and authorizations. agreements: terms_of_use: https://llmapi.ai/terms/ data_processing_agreement: https://llmapi.ai/dpa/ ``` ## Eligibility ```yaml account_required: true payment_method_required: false ``` ## API compatibility and modalities - **Compatibility:** OpenAI Compatible - **Modalities:** Text generation ## Before you build with 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. “Free model routes” 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 The snapshot records 1 model ID. Match the exact ID against the provider's current catalog before using it in code because zero-price routes, aliases, context limits, and feature support can rotate while an older documentation page remains online. ### 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 gateway defaults to zero retention and no training, but upstream providers are independently governed and optional logging changes retention. 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 5 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 - [API resources and limits](https://docs.llmapi.ai/resources): Official Docs - [Models API](https://api.llmapi.ai/v1/models): Live Catalog - [ZAYA1-8B model page](https://llmapi.ai/models/): Official Catalog - [LLM.API Terms of Use](https://llmapi.ai/terms/): Official Terms - [LLM.API Data Processing Agreement](https://llmapi.ai/dpa/): Official Dpa ## Sitemap See the full [semantic sitemap](/sitemap.md) for every page and markdown mirror.