---
title: "Inferon free inference"
description: "A finite allowance for new accounts; it does not recur."
canonical_url: "https://freeinferencing.com/provider/inferon/"
md_url: "https://freeinferencing.com/provider/inferon.md"
last_updated: "2026-09-19"
---

# Inferon

> A finite allowance for new accounts; it does not recur.

## Classification

- **Directory:** Current
- **Free access:** One-time trial
- **Status:** Current Trial Amount Unpublished
- **Confidence:** Medium High
- **Payment card:** No
- **Account:** Required
- **Equivalent paid value:** Not quantifiable


## Models mentioned

- `flux-2-flex`
- `nano-banana-2`
- `seedream-v5-lite`
- `ltx-2-19b`
- `kling-v3-pro`
- `kling-v3-standard`
- `xai-grok-imagine-video`
- `sync-lipsync-v2`

## Limits and terms

```yaml
trial_duration_days: 7
included_ai_credit_amount: not_published
recurrence: false
authenticated_requests_per_minute: 300
generation_requests_per_minute: 30
checkout_and_billing_requests_per_minute: 10
exhaustion: Requests above the available balance are rejected.
```

## What happens to your prompts?

**Not private.** Inferon excludes training its own general models on customer content, but uploads and outputs persist by default until deletion or account closure, backups may persist for 90 days, and third-party routes have independent terms.

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: Inferon Gateway image/video generation and related workspace storage.
  prompt_retention: Prompts, uploaded files, and related request content may
    remain in the user's workspace until deletion or account closure.
  response_retention: Generated outputs remain in workspace storage until deleted
    or the account closes; backup deletion can take up to 90 days.
  ordinary_logging: Account, API-key, usage, billing, model, request, timing,
    security, and operational records are retained.
  model_training: Inferon says it does not train its own general-purpose models on
    customer content.
  product_improvement: Service and operational improvement uses exist, but the
    policy does not grant a blanket right to train Inferon's general models on
    content.
  human_or_operator_access: Staff and providers may access content for support,
    safety, security, legal, and service-delivery needs.
  subprocessors_and_routing: Prompts and files can be routed to third-party image,
    video, and lip-sync model providers under those providers' independent
    terms.
  deletion_controls: Workspace deletion and account closure remove primary
    content; backup purge may take up to 90 days and legal/security records can
    remain.
  caveat: Storage and independently governed upstream routes prevent a private
    classification despite Inferon's own no-general-model-training statement.
agreements:
  privacy_policy: https://blog.inferon.ai/policies/privacy
```

## Eligibility

```yaml
account_required: true
payment_method_required: not_documented
```

## API compatibility and modalities

- **Compatibility:** Inferon Gateway Rest
- **Modalities:** Image, Video, Lip Sync, Image / vision


## Before you build with Inferon

### 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. “One-time trial” should be interpreted together with the Current directory placement, Medium High confidence, No payment-card status, and the plan-specific sources below.

### Resolve the live model route

The snapshot records 8 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 **Not private** because Inferon excludes training its own general models on customer content, but uploads and outputs persist by default until deletion or account closure, backups may persist for 90 days, and third-party routes have independent terms. 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

- [Pricing](https://www.inferon.ai/pricing): Official Pricing
- [Gateway](https://www.inferon.ai/gateway): Official Product
- [API policy](https://blog.inferon.ai/policies/api): Official Policy
- [Privacy policy](https://blog.inferon.ai/policies/privacy): Official Privacy
- [Model usage policy](https://blog.inferon.ai/policies/model-usage): Official Policy

## Sitemap

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