---
title: "Arcee API | Ethical Stack"
url: https://ethicalstack.ai/providers/arcee-api/
catalog_version: f6baff2119d0e9158520da7fbe4232011dac79f25bd08757436d7a1dbd593db2
---

# Arcee API

Hosted, OpenAI-compatible chat-completions API with selectable models and token billing.

Canonical profile: https://ethicalstack.ai/providers/arcee-api/

Scope: Arcee Platform inference API at api.arcee.ai; selected model and any underlying model-provider terms vary by request.

Published overall: 3.4; 5/6 dimensions rated.

Round the equal-weight mean of available numerical ratings to one decimal; require at least four of six for an overall score. BDS status stays outside the mean; for eligible profiles, a boycott target caps the final score at 2/10 and a pressure target subtracts one point (minimum 1). Not listed and not checked do not adjust the score.

## Energy practices

Finding: 2/10

Assessment: https://ethicalstack.ai/providers/arcee-api/#energy

Documentation identifies a hosted API boundary, but does not report its physical footprint or request-level resource controls.

Reason: 1 + 1 reporting + 0 electricity + 0 controls = 2/10. This grades documented practices and transparency.

Argument: This practices assessment covers Arcee's hosted inference API, whose docs identify the API endpoint and selectable model catalog. Published token prices are billing information, not energy measurements; electricity sourcing and per-request energy, emissions and water remain unestablished.

Type: argument; confidence: medium

Scope (dimension): Arcee Platform inference API; physical serving resources depend on selected model and provider.

Research reviewed: not recorded; evidence date: not recorded

Unknowns: No route-specific electricity mix, energy or water measurements, regional serving map, or documented resource-saving request control was found.

```json
{
  "method": "practices-transparency-v1",
  "scope": "Arcee Platform inference API; physical serving resources depend on selected model and provider.",
  "components": {
    "disclosure": {
      "score": 1,
      "reason": "Arcee documents this as a hosted inference API and publishes a model catalog, establishing the service boundary but no route-specific footprint report.",
      "sourceUrls": [
        "https://docs.arcee.ai/get-started/quick-start.md",
        "https://docs.arcee.ai/get-started/pricing.md"
      ]
    },
    "electricity": {
      "score": 0,
      "reason": "The reviewed API materials do not map this endpoint and selected model to measured electricity use or a time- and location-matched clean-power supply.",
      "sourceUrls": [
        "https://docs.arcee.ai/get-started/quick-start.md",
        "https://docs.arcee.ai/get-started/pricing.md"
      ]
    },
    "demand": {
      "score": 0,
      "reason": "The reviewed quick start and pricing page do not document a user-facing workload control with a measured resource reduction for this API route.",
      "sourceUrls": [
        "https://docs.arcee.ai/get-started/quick-start.md",
        "https://docs.arcee.ai/get-started/pricing.md"
      ]
    }
  },
  "actualImpactMeasured": false
}
```

- [Arcee Platform API quick start](https://docs.arcee.ai/get-started/quick-start.md) (primary; checked 2026-10-24)

- [Arcee Platform model pricing](https://docs.arcee.ai/get-started/pricing.md) (primary; checked 2026-10-24)

## Training data

Finding: Depends on the model

Assessment: https://ethicalstack.ai/providers/arcee-api/#training-rights

Training-data provenance and permissions depend on the model selected through the API; hosted service terms also permit some submitted content to be used for model improvement.

Reason: Select the exact model identifier and provider before applying a model-corpus assessment; Arcee's service-level input-improvement terms are a separate data-use issue.

Argument: The API requires the caller to choose a model, and the live catalog lists Arcee and external model families with distinct releases. Arcee's privacy policy separately permits training or improvement use of unpaid-service inputs and outputs; neither fact establishes the underlying selected model's corpus provenance or permissions.

Type: argument; confidence: medium

Scope (profile): Arcee Platform inference API at api.arcee.ai; selected model and any underlying model-provider terms vary by request.

Research reviewed: not recorded; evidence date: not recorded

Unknowns: Check the exact model release, source corpus and rights record, plus the applicable model-provider terms; API content-use terms do not prove corpus permission.

```json
{
  "method": "training-rights-evidence-v1",
  "scope": "The stated training release or selected model",
  "components": null,
  "routeDependency": "Select the exact model identifier and provider before applying a model-corpus assessment; Arcee's service-level input-improvement terms are a separate data-use issue.",
  "modelReference": null
}
```

- [Arcee Platform API quick start](https://docs.arcee.ai/get-started/quick-start.md) (primary; checked 2026-10-24)

- [Arcee Platform model pricing](https://docs.arcee.ai/get-started/pricing.md) (primary; checked 2026-10-24)

- [Arcee AI Privacy Policy](https://www.arcee.ai/privacy-policy) (primary; checked 2026-10-24)

## Your data & privacy

Finding: 5/10

Assessment: https://ethicalstack.ai/providers/arcee-api/#privacy

Arcee says its inference API uses zero persistent retention by default, while its privacy policy allows unpaid-service inputs and outputs to be used for improvement unless the user opts out; flagged or reported content can still be used for safety improvement.

Reason: A defined API non-persistence policy is a meaningful control, but content-use permissions and possible third-party model-provider processing leave material route-specific exposure.

Argument: The specific retention policy says the API does not persistently store inputs or outputs after processing, but the general privacy policy allows unpaid-service content to train or improve services unless a user emails support, with safety-review and reported-content exceptions. Terms also authorize third-party model providers to process data where applicable, so the selected model route remains relevant.

Type: argument; confidence: medium

Scope (profile): Arcee Platform inference API at api.arcee.ai; selected model and any underlying model-provider terms vary by request.

Research reviewed: not recorded; evidence date: not recorded

Unknowns: The policies do not explain how default API training use is reconciled with zero persistent retention, nor identify every underlying model provider or route-level region; avoid sensitive or regulated personal data under the terms.

- [Arcee API and web-app data retention policy](https://docs.arcee.ai/policies/data-retention-policy) (primary; checked 2026-10-24)

- [Arcee AI Privacy Policy](https://www.arcee.ai/privacy-policy) (primary; checked 2026-10-24)

- [Arcee AI Terms of Service](https://www.arcee.ai/terms-of-service) (primary; checked 2026-10-24)

## Openness

Finding: 2/10

Assessment: https://ethicalstack.ai/providers/arcee-api/#openness

The API is a hosted interface; its serving runtime is not provided for self-hosting as part of this route.

Reason: The hosted API exposes a documented interface and selectable models but not the service runtime or serving infrastructure.

Argument: Arcee documents account setup, API credentials, a hosted endpoint and model selection. The company separately offers downloadable weights, but those artifacts do not make this managed API runtime inspectable or self-hostable; the selected model catalog can also include third-party model families.

Type: argument; confidence: medium

Scope (profile): Arcee Platform inference API at api.arcee.ai; selected model and any underlying model-provider terms vary by request.

Research reviewed: not recorded; evidence date: not recorded

Unknowns: The serving stack, deployment configuration and per-model hosting implementation are not published in the reviewed API documentation.

- [Arcee Platform API quick start](https://docs.arcee.ai/get-started/quick-start.md) (primary; checked 2026-10-24)

- [Arcee Platform model pricing](https://docs.arcee.ai/get-started/pricing.md) (primary; checked 2026-10-24)

- [Arcee AI product homepage and product links](https://www.arcee.ai/) (primary; checked 2026-10-24)

## Worker protections

Finding: 1/10

Assessment: https://ethicalstack.ai/providers/arcee-api/#labour

Arcee reports that its team built Trinity and will expand; reviewed primary sources do not describe workforce conditions or worker channels.

Reason: 1 base + 0 documented conditions + 0 voice/remedy + 0 assurance = 1/10. Team and funding statements do not establish worker protections.

Argument: Arcee’s company update says the Trinity team built its 2025 model lineup and that the Series B will support hiring and growth. It does not set out pay, working conditions, supplier safeguards, or grievance processes for employees and model contractors, so this remains a disclosure gap. This entry carries the same developer/operator finding to Arcee API; it does not establish that this product was deployed under every cited relationship.

Type: argument; confidence: low

Scope (dimension): Arcee AI: the documented developer/operator record, not proof of deployment of Arcee API.

Research reviewed: not recorded; evidence date: not recorded

Unknowns: No worker pay, location, conditions, supplier coverage, voice or remedy system, independent audit, or follow-through outcomes were established in the reviewed official company sources.

```json
{
  "method": "worker-safeguards-evidence-v1",
  "entity": "models/arcee-trinity",
  "company": "",
  "scope": "The named developer or operator",
  "components": {
    "conditions": {
      "score": 0,
      "reason": "Arcee discusses its team and hiring plans, but the reviewed company sources do not publish pay or working-condition standards for staff and contractors.",
      "sourceUrls": [
        "https://www.arcee.ai/blog/arcee-ai-raises-series-b-to-build-the-future-of-american-open-models"
      ]
    },
    "voice": {
      "score": 0,
      "reason": "No employee or supplier grievance channel, non-retaliation commitment, or remedy mechanism was established in the reviewed sources.",
      "sourceUrls": [
        "https://www.arcee.ai/blog/arcee-ai-raises-series-b-to-build-the-future-of-american-open-models"
      ]
    },
    "assurance": {
      "score": 0,
      "reason": "No independent worker-level audit or labor remediation outcome was established in the reviewed sources.",
      "sourceUrls": [
        "https://www.arcee.ai/blog/arcee-ai-raises-series-b-to-build-the-future-of-american-open-models"
      ]
    }
  },
  "disclosureLimited": true
}
```

- [Arcee AI Raises Series B to Build the Future of American Open Models](https://www.arcee.ai/blog/arcee-ai-raises-series-b-to-build-the-future-of-american-open-models) (primary; checked not recorded)

- [Official model card or model release repository](https://huggingface.co/arcee-ai/Trinity-Mini) (primary; checked 2026-10-24)

## Military ties

Finding: 7/10

Assessment: https://ethicalstack.ai/providers/arcee-api/#military-contracts

Arcee publicly describes a civilian-science collaboration with DOE; no Arcee-Anduril or defense deployment link was established in reviewed official company materials.

Reason: 7 base + 0 safeguard adjustment = 7/10 (2 of 9 safeguard points; every 3 adds 1). No military relationship was established in the cited sources. This ranks above known defense ties without claiming a verified clean record.

Argument: Arcee and DOE describe Genesis-Science-1 as an open model for scientific research with national laboratories shaping research tasks. That is a public civilian-government collaboration, not evidence of military use. Trinity’s OpenMDW license imposes no field-of-use restriction; no separate defense safeguard was found. This entry carries the same developer/operator finding to Arcee API; it does not establish that this product was deployed under every cited relationship.

Type: argument; confidence: medium

Scope (dimension): Arcee AI: the documented developer/operator record, not proof of deployment of Arcee API.

Research reviewed: not recorded; evidence date: not recorded

Unknowns: No military customer, Anduril relationship, defense contract, Granite-like model deployment, or military-use enforcement process is established in the reviewed official sources.

```json
{
  "method": "military-ties-priority-v2",
  "entity": "models/arcee-trinity",
  "company": "",
  "scope": "The named developer or operator",
  "components": {
    "restrictions": {
      "score": 0,
      "reason": "OpenMDW-1.1 grants unrestricted use without a field-of-use restriction; that is a license scope finding, not proof of military deployment.",
      "sourceUrls": [
        "https://openmdw.ai/faq/",
        "https://huggingface.co/arcee-ai/Trinity-Mini"
      ]
    },
    "oversight": {
      "score": 0,
      "reason": "No monitoring or enforcement for military use of downloaded Trinity weights was established.",
      "sourceUrls": [
        "https://www.arcee.ai/blog/genesis-science-1",
        "https://huggingface.co/arcee-ai/Trinity-Mini"
      ]
    },
    "transparency": {
      "score": 2,
      "reason": "Arcee publicly describes its DOE and national-laboratory Genesis collaboration; the source scopes it to scientific research.",
      "sourceUrls": [
        "https://www.arcee.ai/blog/genesis-science-1",
        "https://www.arcee.ai/blog/arcee-ai-raises-series-b-to-build-the-future-of-american-open-models"
      ]
    }
  },
  "disclosureLimited": true,
  "relationship": "civilian-government",
  "relationshipReason": "Arcee publicly collaborates with DOE and national laboratories on a scientific model. Reviewed company sources did not establish a defense relationship.",
  "score": 7,
  "baseScore": 7,
  "safeguardPoints": 2,
  "safeguardAdjustment": 0,
  "rawScore": 7,
  "cap": null
}
```

- [Arcee Genesis-Science-1 announcement](https://www.arcee.ai/blog/genesis-science-1) (primary; checked 2026-10-24)

- [Arcee AI Raises Series B to Build the Future of American Open Models](https://www.arcee.ai/blog/arcee-ai-raises-series-b-to-build-the-future-of-american-open-models) (primary; checked not recorded)

- [OpenMDW FAQ](https://openmdw.ai/faq/) (primary; checked not recorded)

- [Official model card or model release repository](https://huggingface.co/arcee-ai/Trinity-Mini) (primary; checked 2026-10-24)

## BDS targets

Finding: Not listed

Assessment: https://ethicalstack.ai/providers/arcee-api/#israel-palestine

Arcee AI is not named in the two official BNC campaign lists checked. This is a limited list check, not BDS approval.

Reason: Campaign category, excluded from numerical scoring.

Argument: The official BNC consumer boycott/pressure guide and No Tech campaign page do not name Arcee AI among their current targets. BDS uses selective campaigns, so absence from these lists is neutral and does not establish a clean record or approval. This entry carries the same developer/operator finding to Arcee API; it does not establish that this product was deployed under every cited relationship.

Type: argument; confidence: medium

Scope (dimension): Arcee AI: the documented developer/operator record, not proof of deployment of Arcee API.

Research reviewed: not recorded; evidence date: not recorded

Unknowns: This check covers only named targets in the two cited BNC campaigns; it does not cover all BDS campaigns, local groups, investments, suppliers, or downstream hosts. Lists and ownership may change.

- [BNC official campaign target guide](https://bdsmovement.net/Guide-to-BDS-Boycott) (campaign; checked 2026-10-24)

- [BNC official No Tech campaign page](https://bdsmovement.net/no-tech-oppression-apartheid-or-genocide) (campaign; checked 2026-10-24)

## Reading the evidence

- Assessments are provisional editorial judgments. Cite the profile and the underlying sources, and retain confidence, scope and unknowns.

- Unknown, not-applicable and model-dependent are different states. Missing evidence does not establish good or bad conduct.

- Company-level evidence does not establish use of a particular product or model. Account plan, model, host and route can change an assessment.

- Energy scores describe documented practices and transparency, not measured request emissions. Zero component points can mean an evidence gap.

- Published overall scores require at least four of six numerical dimensions. Preserve coverage and BDS adjustments; BDS itself is categorical.

- No named BDS target found is limited to the reviewed campaign sources and is not approval. Personalized priorities do not change the published overall score.

- Review and source-check dates describe the research, not this deployment. Fetch current primary sources when the decision requires current verification.

Methodology: https://ethicalstack.ai/methodology/