chutes.ai
Free LLM quota · free models · community reputation · risk review
Overview
Chutes is a decentralized serverless AI inference marketplace on Bittensor (Subnet 64) with an OpenAI-compatible API hosting many open-source models; its early free tier is largely gone, now shifted to pay-per-token/subscription.
Free quota
The early 'one-time $5 deposit for 200 requests/day' free tier has been removed; it is now pay-per-token with no subscription or minimum. Some models are near-free thanks to Bittensor TAO subsidies, but that depends on subsidies/miners and is unstable.
Free models
DeepSeek-V3.2Meta-Llama 3.1MistralPros
- OpenAI-compatible API with many open-weight models (DeepSeek/Llama/Mistral, etc.)
- Decentralized compute, pay-per-use with no minimum, often cheaper than mainstream clouds
- Already an upstream provider for aggregators like OpenRouter; sizeable (claims trillions of tokens/month)
Cons
- The original free tier is gone; it now essentially requires payment, and 'free' is no longer a stable selling point
- Pricing/free thresholds fluctuate with Bittensor subnet economics and TAO subsidies — unpredictable
- Execution by decentralized miners; the community has questioned model authenticity/quantization/consistency
- Privacy: requests pass through third-party miner nodes — be cautious with sensitive data
Risk flags
- Free tier removed; the probed $10 quota hint does not mean an ongoing free allowance
- Models executed by decentralized miners — quality/quantization/authenticity uncertainty
- Pricing and availability fluctuate with Bittensor subsidies — stability risk
- Requests transit third-party nodes — assess privacy/compliance yourself
Community reputation
Chutes is fairly well-known in AI/crypto circles and is listed by IQ.wiki, Bittensor research, and several review sites; but its decentralized model raises authenticity/stability questions, and after removing the free tier its reputation shifted from 'a free DeepSeek source' to 'a cheap paid inference marketplace'.
Recommendation
No longer qualifies as a 'free LLM API' (free tier removed); more of a cheap decentralized paid inference marketplace. Those willing to accept price volatility and model-authenticity risk can try pay-per-use — do not send sensitive data.
Latest probe
Mentions (2)
Sources
Community discussion · reputation check
Want to verify chutes.ai's real reputation before deciding? Read first-hand community discussion:
External discussion quality varies; judge together with the risk review on this page.
FAQ
Is chutes.ai safe and trustworthy?
Overall reputation 58/100, risk level Med. Chutes is fairly well-known in AI/crypto circles and is listed by IQ.wiki, Bittensor research, and several review sites; but its decentralized model raises authenticity/stability questions, and after removing the free tier its reputation shifted from 'a free DeepSeek source' to 'a cheap paid inference marketplace'.
Will chutes.ai shut down? Is the service stable?
Known risks: Free tier removed; the probed $10 quota hint does not mean an ongoing free allowance; Models executed by decentralized miners — quality/quantization/authenticity uncertainty; Pricing and availability fluctuate with Bittensor subsidies — stability risk; Requests transit third-party nodes — assess privacy/compliance yourself. Free and relay/public-benefit stations generally have no SLA and may shut down or change quotas at any time; don't use them for production or critical work — keep an official paid fallback for important use.
Which free models does chutes.ai offer? How much free quota?
Free quota: The early 'one-time $5 deposit for 200 requests/day' free tier has been removed; it is now pay-per-token with no subscription or minimum. Some models are near-free thanks to Bittensor TAO subsidies, but that depends on subsidies/miners and is unstable.. Available free models: DeepSeek-V3.2, Meta-Llama 3.1, Mistral.
Is chutes.ai worth using? How should I use it?
No longer qualifies as a 'free LLM API' (free tier removed); more of a cheap decentralized paid inference marketplace. Those willing to accept price volatility and model-authenticity risk can try pay-per-use — do not send sensitive data.
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