---
title: "Chutes | Ethical Stack"
url: https://ethicalstack.ai/providers/chutes/
catalog_version: f6baff2119d0e9158520da7fbe4232011dac79f25bd08757436d7a1dbd593db2
---

# Chutes

Serverless inference platform for public model endpoints and user-created compute chutes.

Canonical profile: https://ethicalstack.ai/providers/chutes/

Scope: Public system-user LLM API inference only; first-party apps and user-created/private chutes have separate data handling.

Published overall: 4.4; 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: 3/10

Assessment: https://ethicalstack.ai/providers/chutes/#energy

For the Chutes public system-user LLM API inference; first-party apps and user-created/private Chutes are outside this assessment boundary, public documentation establishes the service or route but not its request-level physical footprint. The API exposes selectable models and request-generation parameters; these configure the workload, but no physical savings are measured. Electricity sourcing is not mapped to this route.

Reason: 1 + 1 reporting + 0 electricity + 1 controls = 3/10. This grades documented practices and transparency.

Argument: This practices/transparency assessment covers the Chutes public system-user LLM API inference; first-party apps and user-created/private Chutes are outside this assessment boundary. The sources establish how the service or endpoint is exposed and provide the request controls described in the demand component; they do not give a measured per-request energy, emissions or water value. The disclosure point reflects the stated operating boundary, not a product footprint, and company-wide electricity reporting is not attributed to this route.

Type: argument; confidence: medium

Scope (dimension): Public system-user LLM API inference only; first-party apps and user-created/private chutes have separate data handling.

Research reviewed: not recorded; evidence date: not recorded

Unknowns: The reviewed sources do not establish request-level electricity, emissions or water; the actual serving facility and its power mix are also not mapped to Chutes public system-user LLM API inference; first-party apps and user-created/private Chutes are outside this assessment.

```json
{
  "method": "practices-transparency-v1",
  "scope": "Public system-user LLM API inference only; first-party apps and user-created/private chutes have separate data handling.",
  "components": {
    "disclosure": {
      "score": 1,
      "reason": "The reviewed documentation defines the Chutes public system-user LLM API inference; first-party apps and user-created/private Chutes are outside this assessment boundary. It gives an operating-scope description, not a dated quantitative energy, carbon or water report for this route.",
      "sourceUrls": [
        "https://www.chutes.ai/privacy",
        "https://chutes.ai/terms",
        "https://www.chutes.ai/docs/help/faq"
      ]
    },
    "electricity": {
      "score": 0,
      "reason": "No reviewed source maps Chutes public system-user LLM API inference; first-party apps and user-created/private Chutes are outside this assessment to a disclosed electricity mix or time-and-location-matched clean-power supply. The route's actual electricity use and emissions therefore remain unestablished.",
      "sourceUrls": [
        "https://www.chutes.ai/privacy",
        "https://chutes.ai/terms",
        "https://www.chutes.ai/docs/help/faq"
      ]
    },
    "demand": {
      "score": 1,
      "reason": "The API exposes selectable models and request-generation parameters; these configure the workload, but no physical savings are measured.",
      "sourceUrls": [
        "https://www.chutes.ai/privacy",
        "https://chutes.ai/terms",
        "https://www.chutes.ai/docs/help/faq"
      ]
    }
  },
  "actualImpactMeasured": false
}
```

- [Chutes Privacy Policy](https://www.chutes.ai/privacy) (primary; checked not recorded)

- [Chutes Terms of Service](https://chutes.ai/terms) (primary; checked not recorded)

- [Chutes API FAQ: model selection and provider boundary](https://www.chutes.ai/docs/help/faq) (primary; checked 2026-10-24)

## Training data

Finding: Depends on the model

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

Chutes routes requests to many model families; prompt non-retention does not establish training rights for selected model weights.

Reason: Chutes routes requests to many model families; prompt non-retention does not establish training rights for selected model weights.

Argument: The provider documents how request content is handled in public inference, but its multi-model catalog does not have one corpus-rights basis. Check the exact model release and license; API privacy is not training-data permission.

Type: argument; confidence: medium

Scope (profile): Public system-user LLM API inference only; first-party apps and user-created/private chutes have separate data handling.

Research reviewed: not recorded; evidence date: not recorded

Unknowns: Exact model selection, release version, source inventory, permissions, post-training data, and creator-remedy scope remain unresolved.

```json
{
  "method": "training-rights-evidence-v1",
  "scope": "The stated training release or selected model",
  "components": null,
  "routeDependency": "Chutes routes requests to many model families; prompt non-retention does not establish training rights for selected model weights.",
  "modelReference": null
}
```

- [Chutes Privacy Policy](https://www.chutes.ai/privacy) (primary; checked not recorded)

- [Chutes Terms of Service](https://chutes.ai/terms) (primary; checked not recorded)

## Your data & privacy

Finding: 8/10

Assessment: https://ethicalstack.ai/providers/chutes/#privacy

Chutes says public system-model API prompts and responses are not persistently logged; request metadata remains, while first-party apps and user-created chutes have different guarantees.

Reason: No numerical score assigned because the reviewed evidence does not establish the route-specific controls needed for one.

Argument: The Chutes privacy policy carefully scopes its zero-content-retention statement to public LLM inference using system models. It describes transient in-memory processing, billing/operational metadata, and exceptions for applications or arbitrary third-party code, so the privacy grade does not apply to every chute.

Type: argument; confidence: medium

Scope (profile): Public system-user LLM API inference only; first-party apps and user-created/private chutes have separate data handling.

Research reviewed: not recorded; evidence date: not recorded

Unknowns: Check that the target is a public system-user model endpoint, not a private or user-created chute; account, billing, support, and infrastructure metadata still exist.

- [Chutes Privacy Policy](https://www.chutes.ai/privacy) (primary; checked not recorded)

- [Chutes Terms of Service](https://chutes.ai/terms) (primary; checked not recorded)

## Openness

Finding: 3/10

Assessment: https://ethicalstack.ai/providers/chutes/#openness

Chutes hosts many models and offers inference APIs, but its service platform and request routing are hosted rather than user-operated locally.

Reason: The score reflects service-level inspectability and control, not usefulness or model quality.

Argument: Users can choose public model deployments through a hosted API and review individual model releases, but the Chutes inference service itself is not documented as a self-hostable runtime. Model licensing is separate from platform control.

Type: argument; confidence: medium

Scope (profile): Public system-user LLM API inference only; first-party apps and user-created/private chutes have separate data handling.

Research reviewed: not recorded; evidence date: not recorded

Unknowns: Model availability, weight licenses, deployment controls, and exact serving configuration vary by model.

- [Chutes Privacy Policy](https://www.chutes.ai/privacy) (primary; checked not recorded)

- [Chutes Terms of Service](https://chutes.ai/terms) (primary; checked not recorded)

## Worker protections

Finding: 1/10

Assessment: https://ethicalstack.ai/providers/chutes/#labour

Company-level worker conditions and supply-chain safeguards are not assessed from product terms alone.

Reason: 1 + 0 + 0 + 0 = 1/10. Documented worker protections; missing disclosure is an evidence gap.

Argument: The reviewed product material describes access and features but does not establish labor conditions for the company’s model-development, data-labeling, moderation, or infrastructure workforce. This is an evidence boundary, not a claim about worker treatment.

Type: argument; confidence: low

Scope (profile): Public system-user LLM API inference only; first-party apps and user-created/private chutes have separate data handling.

Research reviewed: not recorded; evidence date: not recorded

Unknowns: Current worker conditions, voice, grievance outcomes, contractor scope, and independent assurance were not established in these product sources.

```json
{
  "method": "worker-safeguards-evidence-v1",
  "entity": "providers/chutes",
  "company": "",
  "scope": "The named developer or operator",
  "components": {
    "conditions": {
      "score": 0,
      "reason": "No qualifying worker-conditions disclosure was found in the reviewed product sources; this is a limited evidence gap.",
      "sourceUrls": [
        "https://www.chutes.ai/privacy",
        "https://chutes.ai/terms"
      ]
    },
    "voice": {
      "score": 0,
      "reason": "No worker-voice or grievance outcome evidence was found in the reviewed product sources.",
      "sourceUrls": [
        "https://www.chutes.ai/privacy",
        "https://chutes.ai/terms"
      ]
    },
    "assurance": {
      "score": 0,
      "reason": "No independent worker-safeguard audit was found in the reviewed product sources.",
      "sourceUrls": [
        "https://www.chutes.ai/privacy",
        "https://chutes.ai/terms"
      ]
    }
  },
  "disclosureLimited": true
}
```

- [Chutes Privacy Policy](https://www.chutes.ai/privacy) (primary; checked not recorded)

- [Chutes Terms of Service](https://chutes.ai/terms) (primary; checked not recorded)

## Military ties

Finding: 7/10

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

No military or intelligence relationship is established by the reviewed product sources.

Reason: 7 base + 0 safeguard adjustment = 7/10 (0 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: The official sources reviewed define product behavior and service terms but do not identify a military contract, deployment, or military-specific restriction for Chutes Global Corp.. This is “not established” within this source set, not evidence that no relationship exists.

Type: argument; confidence: low

Scope (profile): Public system-user LLM API inference only; first-party apps and user-created/private chutes have separate data handling.

Research reviewed: not recorded; evidence date: not recorded

Unknowns: Undisclosed contracts, downstream hosting, customer deployment, end-use, and enforcement are outside the reviewed product sources.

```json
{
  "method": "military-ties-priority-v2",
  "entity": "providers/chutes",
  "company": "",
  "scope": "The named developer or operator",
  "components": {
    "restrictions": {
      "score": 0,
      "reason": "No product-specific military-use restriction was identified in the reviewed materials.",
      "sourceUrls": [
        "https://www.chutes.ai/privacy",
        "https://chutes.ai/terms"
      ]
    },
    "oversight": {
      "score": 0,
      "reason": "No military-specific enforcement or oversight mechanism was identified in the reviewed materials.",
      "sourceUrls": [
        "https://www.chutes.ai/privacy",
        "https://chutes.ai/terms"
      ]
    },
    "transparency": {
      "score": 0,
      "reason": "The product sources provide no military customer or deployment disclosure.",
      "sourceUrls": [
        "https://www.chutes.ai/privacy",
        "https://chutes.ai/terms"
      ]
    }
  },
  "disclosureLimited": true,
  "relationship": "not-established",
  "relationshipReason": "No company military relationship was established in the reviewed product sources; the source set is not a customer-complete investigation.",
  "score": 7,
  "baseScore": 7,
  "safeguardPoints": 0,
  "safeguardAdjustment": 0,
  "rawScore": 7,
  "cap": null
}
```

- [Chutes Privacy Policy](https://www.chutes.ai/privacy) (primary; checked not recorded)

- [Chutes Terms of Service](https://chutes.ai/terms) (primary; checked not recorded)

## BDS targets

Finding: Not listed

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

Chutes Global Corp. was not named in the BNC consumer-target guide or its No Tech target list when checked on 2026-10-24. This status covers those named lists only.

Reason: Campaign category, excluded from numerical scoring.

Argument: I checked the BNC consumer-boycott and pressure guide and the No Tech target list for Chutes Global Corp.. The company was not named in either list on 2026-10-24; this does not assess unrelated suppliers, customers, investors, or every BNC publication.

Type: argument; confidence: medium

Scope (profile): Public system-user LLM API inference only; first-party apps and user-created/private chutes have separate data handling.

Research reviewed: not recorded; evidence date: not recorded

Unknowns: The result is limited to the two linked BNC target lists as checked on 2026-10-24; additions or other BNC publications may change the assessment.

- [BNC consumer boycott and pressure guide](https://bdsmovement.net/Guide-to-BDS-Boycott) (primary; checked 2026-10-24)

- [BNC No Tech campaign](https://bdsmovement.net/no-tech-oppression-apartheid-or-genocide) (primary; 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/