--- title: "AwanLLM free inference" description: "A recurring no-cost API allowance covering 6 cataloged models." canonical_url: "https://freeinferencing.com/provider/awanllm/" md_url: "https://freeinferencing.com/provider/awanllm.md" last_updated: "2026-09-19" --- # AwanLLM > A recurring no-cost API allowance covering 6 cataloged models. ## Classification - **Directory:** Current - **Free access:** Always-free quota - **Status:** Available now - **Confidence:** High - **Payment card:** No - **Account:** Required - **Equivalent paid value:** At least $79.79/mo - **API endpoint:** `https://api.awanllm.com/v1` ## Models mentioned - `Meta-Llama-3.1-8B-Instruct` - `Meta-Llama-3-8B-Instruct` - `Awanllm-Llama-3-8B-Dolfin` - `Awanllm-Llama-3-8B-Cumulus` - `Meta-Llama-3.1-70B-Instruct` - `Meta-Llama-3-70B-Instruct` ## Limits and terms ```yaml tokens: unlimited requests_per_minute: 20 small_model_requests_per_day: 200 medium_model_requests_per_day: 10 large_model_requests_per_day: 10 ``` ## What happens to your prompts? **Private.** AwanLLM says prompts and generations are not logged and discloses no content-based training or improvement use. 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: AwanLLM hosted text-generation API under its public terms and privacy policy. prompt_retention: AwanLLM states it does not log user prompts or generations. response_retention: AwanLLM states generations are not logged. ordinary_logging: Request count and request rate are logged for rate limiting and usage tracking; account email or wallet address and session information are stored. model_training: No prompt or generation content is available from ordinary API logging for training; the policy does not make a broader contractual statement about independently submitted feedback or fine-tuning data. product_improvement: No content-based improvement use is disclosed. The published privacy policy limits tracked API data to request count and rate. human_or_operator_access: The policy states prompts and generations are not logged, so no stored content-review workflow is disclosed. subprocessors_and_routing: The privacy policy says personal information is not shared with third parties; infrastructure subprocessors and model-hosting architecture are not described in detail. deletion_controls: not_documented caveat: The policy is short and does not publish retention periods for account or metadata, deletion procedures, a DPA, or a subprocessor list. agreements: terms_and_conditions: https://www.awanllm.com/terms privacy_policy: https://www.awanllm.com/privacy ``` ## Eligibility ```yaml account_required: true payment_method_required: false ``` ## API compatibility and modalities - **Compatibility:** OpenAI Compatible - **Modalities:** Text generation ## Before you build with AwanLLM ### 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 The snapshot records 6 model IDs. 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 **Private** because AwanLLM says prompts and generations are not logged and discloses no content-based training or improvement use. 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 6 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 - [Pricing](https://www.awanllm.com/pricing): Official Pricing - [Models](https://www.awanllm.com/models): Official Catalog - [Quick start](https://www.awanllm.com/quick-start): Official Docs - [AwanLLM Terms and Conditions](https://www.awanllm.com/terms): Official Terms - [AwanLLM Privacy Policy](https://www.awanllm.com/privacy): Official Privacy - [OpenRouter models endpoint (exact-model paid prices)](https://openrouter.ai/api/v1/models): Live Catalog ## Sitemap See the full [semantic sitemap](/sitemap.md) for every page and markdown mirror.