AwanLLM
A recurring no-cost API allowance covering 6 cataloged models.
https://api.awanllm.com/v1Models mentioned
Meta-Llama-3.1-8B-InstructMeta-Llama-3-8B-InstructAwanllm-Llama-3-8B-DolfinAwanllm-Llama-3-8B-CumulusMeta-Llama-3.1-70B-InstructMeta-Llama-3-70B-InstructEquivalent paid value
How this was valued: The free plan documents independent per-size-class daily request quotas (Small 200/day, Medium 10/day, Large 10/day at a shared 20 requests/minute) with "Unlimited Tokens!" per request, so each request is bounded only by the model's context length as published on AwanLLM's own models page (131,072 tokens for Meta-Llama-3.1-8B-Instruct and Meta-Llama-3.1-70B-Instruct). Each class is valued at its most expensive eligible exact model using current OpenRouter per-token prices for the same models (snapshot 2026-08-22; Meta publishes no first-party API rate and AwanLLM's own paid plans are flat subscriptions without per-token prices). The Medium quota is excluded because the current catalog labels every model Small or Large only, making this a subtotal. Class quotas are independent and summed; models inside a class share that class quota and are not additive. Assumes uninterrupted saturation with the full context consumed by every request.
Limits and terms
- 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?
AwanLLM says prompts and generations are not logged and discloses no content-based training or improvement use.
- 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.
Governing documents
- Terms And Conditions
- https://www.awanllm.com/terms
- Privacy Policy
- https://www.awanllm.com/privacy
Eligibility
- Account Required
- Yes
- Payment Method Required
- No
Primary sources
Before you build with AwanLLM
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
This snapshot records 6 model IDs. Match the exact ID against the provider's live catalog before using it in code; zero-price routes and aliases can rotate while an older documentation page remains online. The recorded base endpoint is https://api.awanllm.com/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 “Private”: AwanLLM says prompts and generations are not logged and discloses no content-based training or improvement use. 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 6 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.