# Chutes vs LLMule

Chutes records 250,187 monthly visits with -15.2% month-over-month growth and Paid pricing. LLMule records 0 monthly visits with 0% growth and Free pricing.

| Metric | [Chutes](https://openaitools.app/tool/chutes) | [LLMule](https://openaitools.app/tool/llmule) |
| :--- | :--- | :--- |
| Website URL | https://chutes.ai | https://llmule.xyz |
| Categories | AI API, Open Source AI Models, Large Language Models (LLMs), AI Developer Tools | Large Language Models (LLMs), AI Models, AI Developer Tools, Open Source AI Models |
| Pricing Model | Paid | Free |
| Monthly Visits | 250,187 | 0 |
| MoM Growth Rate | -15.2% | 0% |
| Primary Market | United States | Global |
| Direct Visits | 187,042 | 0 |
| Review Score | 4.7 | 4.2 |
| Saved Count | 0 | 0 |
| Supported Platforms | Website | Website |

## Chutes

Chutes is a serverless inference platform built on decentralized GPU infrastructure. Instead of renting a fixed GPU allocation, you send requests to an OpenAI-compatible endpoint and the platform routes them to capacity that is already running. The team's pitch is speed of availability: when a new open-source frontier model drops, Chutes aims to have it live within minutes, so developers can test and ship against it early.

The catalogue is deliberately broad. Beyond the large language models you would expect, Chutes hosts image, video, speech, music, embedding, and content-moderation models, plus support for bringing your own code as a custom "chute". That makes it practical to build multi-modal products — a chat assistant that also generates images, transcribes audio, or moderates user content — without stitching together several vendors.

Operationally, Chutes targets production workloads: permanently warm models, batch and ephemeral jobs, per-second GPU billing for private deployments, and confidential inference inside trusted execution environments for privacy-sensitive use cases. Pricing is metered per token for public models, with a live cost estimator and a consumer chat app available alongside the API.

**Pricing & Plans:** Pay-as-you-go, no subscription or minimum commitment: public models are billed per token (per-million input, output and cached rates shown live per model) and private GPU chutes per second of GPU time. Enterprise billing with volume discounts is available by contacting sales.

- **OpenAI-compatible API:** Drop-in compatibility means you change the base URL and key, and your existing OpenAI-style SDK code keeps working.
- **Every open-source modality:** Run LLMs, image, video, speech, music, embedding, and moderation models, or bring your own code and fintune-style workloads.
- **No cold starts:** Models are held permanently warm, so production traffic does not wait for a container to spin up.
- **TEE-protected inference:** Selected models run inside trusted execution environments for private, isolated inference.
- **Pay-as-you-go billing:** Pay per token for hosted models and per second for private GPU chutes, with no subscription or minimum spend.

**Strengths:** OpenAI-compatible endpoint means minimal code changes; Very broad open-source model catalogue across modalities; Per-token and per-second billing avoids idle GPU costs; No cold starts and TEE options for privacy-conscious workloads

[Profile (Markdown)](https://openaitools.app/tool/chutes.md)

## LLMule

LLMule describes itself as an "underground P2P network of LLMs." The core idea is simple: people who have a capable GPU share the models running on their own machines, and other members of the network can call those models through a shared interface. That turns a scattered collection of local setups into something that behaves like a pool of compute.

From a user's point of view there are three ways in. You can chat with models in a web interface, call them through an OpenAI-compatible API, or contribute capacity by running the one-file provider that exposes your local models to the network. Because the API mirrors the OpenAI format, existing tooling and SDKs can often be repointed with nothing more than a new base URL.

The project is openly experimental, and that matters for expectations. Latency and model quality depend on which peers are online, and availability is genuinely variable. It suits people who enjoy decentralized computing and open models, and who are comfortable with a best-effort network rather than a supported commercial SLA.

**Pricing & Plans:** Free. LLMule is a free, community-powered network — there is no subscription or per-token charge. Costs are effectively shared with the peers whose hardware serves the requests.

- **Decentralized P2P model network:** Route requests to models hosted on other participants' machines instead of a single corporate data center.
- **OpenAI-compatible API:** Call remote open-source models through an endpoint that follows the familiar OpenAI chat completions shape.
- **Browser chat interface:** Try models straight from a browser chat window without installing anything.
- **One-file provider:** Join as a provider with a small, single-file program that publishes the models running on your own hardware.
- **Privacy-oriented by design:** Built for people who want to experiment with open models while keeping prompts and data away from centralized providers.

**Strengths:** Completely free to use as a consumer; OpenAI-compatible API keeps integration effort low; Decentralized design avoids relying on one provider; Provider software is a single file and easy to run

**Limitations:** Experimental and explicitly labeled as such, so reliability and uptime are not guaranteed; Response speed and quality depend entirely on which peers are online and how powerful their GPUs are; Sharing your own GPU is required to get the most out of the network, which raises electricity and security considerations; Model availability fluctuates based on what community members choose to host

[Profile (Markdown)](https://openaitools.app/tool/llmule.md)

## Frequently Asked Questions

### How do Chutes and LLMule compare in monthly traffic?

Chutes has 250,187 monthly visits, while LLMule has 0. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

Chutes has a month-over-month growth rate of -15.2%; LLMule has 0%.

### How does pricing compare?

Chutes: Paid. Pay-as-you-go, no subscription or minimum commitment: public models are billed per token (per-million input, output and cached rates shown live per model) and private GPU chutes per second of GPU time. Enterprise billing with volume discounts is available by contacting sales. LLMule: Free. Free. LLMule is a free, community-powered network — there is no subscription or per-token charge. Costs are effectively shared with the peers whose hardware serves the requests.

## Alternatives

| # | Tool Name | Monthly Visits | MoM Growth Rate | Pricing Model | Summary | Profile (Markdown) |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| 1 | [OpenRouter](https://openaitools.app/tool/openrouter-ai) | 19,941,437 | -6.2% | Paid | Aggregates hundreds of models behind one API gateway with intelligent routing, but focuses less on dedicated decentralized hosting. | [.md](https://openaitools.app/tool/openrouter-ai.md) |
| 2 | [Ollama](https://openaitools.app/tool/ollama) | 9,421,973 | +4.3% | Free, Freemium, Paid | Runs open-weight models locally on your own machine instead of providing a cloud-based serverless inference API. | [.md](https://openaitools.app/tool/ollama.md) |
| 3 | [Groq](https://openaitools.app/tool/groq) | 3,987,936 | +10% | Paid | Achieves unmatched inference speed using custom LPU hardware, but supports a more restricted selection of open models. | [.md](https://openaitools.app/tool/groq.md) |
| 4 | [fal.ai](https://openaitools.app/tool/fal-ai) | 3,093,684 | -3.7% | Paid, pay-as-you-go usage-based pricing | Specializes in high-speed serverless inference for generative media models rather than general text LLMs. | [.md](https://openaitools.app/tool/fal-ai.md) |
| 5 | [RunPod](https://openaitools.app/tool/runpod) | 2,881,043 | +5.9% | Paid | The AI developer cloud for GPU pods, serverless inference and clustering | [.md](https://openaitools.app/tool/runpod.md) |
| 6 | [Together AI](https://openaitools.app/tool/together-ai) | 1,645,930 | +64.9% | Freemium, Paid | Together AI is the AI Native Cloud — a full-stack platform for running, fine-tuning and training open-source models. It spans serverless inference APIs, dedicated endpoints, GPU clusters, post-training and storage, all b | [.md](https://openaitools.app/tool/together-ai.md) |
| 7 | [Vast.ai](https://openaitools.app/tool/vast-ai) | 1,257,554 | +2.8% | Paid | Rent cloud GPUs by the second from a global marketplace | [.md](https://openaitools.app/tool/vast-ai.md) |
| 8 | [Modal](https://openaitools.app/tool/modal-com) | 1,196,021 | +30.2% | Freemium, Contact for Pricing | Serverless cloud infrastructure for AI and data workloads | [.md](https://openaitools.app/tool/modal-com.md) |
| 9 | [Replicate AI](https://openaitools.app/tool/replicate-ai) | 904,038 | -8.4% | Paid | Excels at running and fine-tuning diverse machine learning models via API, but uses a distinct serverless architecture. | [.md](https://openaitools.app/tool/replicate-ai.md) |
| 10 | [Unsloth AI](https://openaitools.app/tool/unsloth-ai) | 887,457 | -17.6% | Free | Focuses on memory-efficient model fine-tuning and local execution rather than a hosted decentralized inference cloud. | [.md](https://openaitools.app/tool/unsloth-ai.md) |

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Source: https://openaitools.app/compare/chutes-vs-llmule

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