---
title: "Arli AI free inference"
description: "A recurring no-cost API allowance with provider-published access terms."
canonical_url: "https://freeinferencing.com/provider/arliai/"
md_url: "https://freeinferencing.com/provider/arliai.md"
last_updated: "2026-09-19"
---

# Arli AI

> A recurring no-cost API allowance with provider-published access terms.

## Classification

- **Directory:** Current
- **Free access:** Always-free quota
- **Status:** Available now
- **Confidence:** High
- **Payment card:** No
- **Account:** Required
- **Equivalent paid value:** At least $2.33/mo
- **API endpoint:** `https://api.arliai.com/v1`

## Models mentioned

No stable model IDs were recorded in this snapshot.

## Limits and terms

```yaml
requests_per_model_per_two_days: 5
max_context_tokens: 12000
concurrent_requests: 1
```

## What happens to your prompts?

**Private.** Arli AI documents transient-only processing with no prompt/output storage, training, or content-based improvement.

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: Arli AI hosted inference API.
  prompt_retention: Arli AI states prompts and inputs are used only transiently to
    return a response and are not logged, stored, retained, or accessed
    afterward.
  response_retention: Generated outputs receive the same zero-log treatment.
  ordinary_logging: Usage metadata such as request count, model, and request
    parameters is logged for rate limiting and usage tracking without prompt or
    output content.
  model_training: Arli AI's transient-use license does not authorize training on
    prompts or outputs, and its zero-log design leaves no ordinary stored
    content for training.
  product_improvement: Aggregated or non-content usage data may support
    operations; the terms do not authorize prompt or output use for product
    improvement.
  human_or_operator_access: Arli AI states it does not store or have access to
    prompt and output content beyond transient request processing.
  subprocessors_and_routing: Open-weight or third-party models may carry model
    licenses and use restrictions, but the published terms describe Arli AI as
    the inference host rather than a changing external gateway.
  deletion_controls: There is no prompt history to delete under the stated
    zero-log design. Account and personal-data rights are governed by the
    privacy policy.
  caveat: Zero-log claims are first-party contractual statements, not an
    independently verified technical guarantee; metadata is still retained.
agreements:
  terms_and_conditions: https://www.arliai.com/terms
  privacy_policy: https://www.arliai.com/privacy
```

## Eligibility

```yaml
account_required: true
payment_method_required: false
```

## API compatibility and modalities

- **Compatibility:** OpenAI Compatible
- **Modalities:** Model inference


## Before you build with Arli AI

### 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 **Private** because Arli AI documents transient-only processing with no prompt/output storage, training, or content-based improvement. 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 7 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.arliai.com/pricing): Official Pricing
- [Text generation limits](https://www.arliai.com/docs/textgen): Official Docs
- [Public model catalog](https://api.arliai.com/model/all): Live Catalog
- [Arli AI Terms and Conditions](https://www.arliai.com/terms): Official Terms
- [Arli AI Privacy Policy](https://www.arliai.com/privacy): Official Privacy
- [OpenRouter models endpoint (exact-model paid prices)](https://openrouter.ai/api/v1/models): Live Catalog
- [Arli AI organization on Hugging Face (verified)](https://huggingface.co/ArliAI): Official Repository

## Sitemap

See the full [semantic sitemap](/sitemap.md) for every page and markdown mirror.