Petals vs Spider Cloud
Side-by-side comparison of features, pricing, and ratings
At a glance
| Dimension | Petals | Spider Cloud |
|---|---|---|
| Pricing | Free (p2p GPU sharing) | Freemium: $0.003/page; no fixed monthly plan |
| Primary Use | Decentralized LLM inference & fine-tuning | Web crawling & scraping for AI agents |
| Ease of Use | Requires Python, PyTorch, HuggingFace; technical setup | API + AI Studio, 1000+ scrapers |
| Performance | 6 tok/s (Llama 2 70B), 4 tok/s (Falcon 180B) | 99.9% success rate, $0.03/1k pages |
| Key Differentiator | Run large models on consumer GPU via p2p network | Browser AI commands, AI extraction, stealth |
| Best For | Devs/researchers running LLMs locally & privately | RAG pipelines, LLM agents needing web data |
Spider Cloud and Petals serve entirely different needs: Spider Cloud is a web data extraction API optimized for AI agents, while Petals is a decentralized LLM inference network. If you need structured real-time web content for RAG or AI tools, Spider Cloud's cheap, reliable API with recent Browser AI commands is the obvious choice. If you want to run large models like Llama 405B on modest hardware without paying per token, Petals is a free but technically demanding alternative.
AI web scraping API: crawl, scrape, search any site into markdown or JSON at 10k req/min.
Visit WebsiteWhat real users say: Petals vs Spider Cloud
Not marketing copy and not our opinion — a structured sweep of public discussion (reviews, forums, communities and video comments), showing what people praise and what they complain about for each tool.
Petals
43 mentions across 2 sources · 18% positive — critical
Hacker News, Lemmy
What users praise
- • Runs 100B+ parameter models on consumer GPUs via distributed sharding.
- • Free and open-source — no cloud subscriptions or API keys needed.
- • Privacy-preserving: models stay on local network, no central server.
- • Supports fine-tuning with PyTorch and Hugging Face Transformers.
What frustrates them
- • Repository hasn't been updated in over two years.
- • Inference speed is slow: 4-6 tokens/second on large models.
- • Performance degrades due to inter-node data transfer overhead.
- • Network availability is unreliable — depends on volunteer nodes.
Researched Jul 3, 2026
Spider Cloud
41 mentions across 2 sources · 0% positive — critical
YouTube, Lemmy
What users praise
- • Competitive pay-as-you-go pricing at $1/GB with no expiry.
- • Default rate limit of 10,000 requests per minute is generous.
- • Broad output formats (HTML, markdown, JSON, CSV) cover diverse needs.
- • Integrated Web Search API bundles SERP and extraction for AI agents.
What frustrates them
- • No community feedback to confirm reliability or performance.
- • Self-reported metrics lack independent verification.
- • Stealth browser success may vary across real sites.
- • Potential legal risks from scraping; compliance is user's responsibility.
Researched Aug 26, 2026
Who should pick which
- Solo founder building an AI agent with RAGPick: Spider Cloud
Spider Cloud provides cheap, reliable web crawling with structured output and direct integrations to LangChain/LlamaIndex. Its new Browser AI commands and AI Studio make it ideal for feeding real-time data into a RAG pipeline.
- Researcher needing to run Llama 405B locallyPick: Petals
Petals is the only free option that allows running extremely large models on consumer GPUs via distributed inference. It supports custom fine-tuning and hidden state access, crucial for deep learning research.
- Privacy-conscious developer avoiding cloud APIsPick: Petals
Petals runs models locally without sending data to any centralized server; the p2p network only shares model layers, not user data. This preserves privacy while still enabling use of large models.
- Team building a high-volume scraping pipelinePick: Spider Cloud
With 99.9% success, automatic retries, unblocker, and data connectors to cloud storage, Spider Cloud scales to millions of pages. The new scraper catalog reduces development time.
- Hobbyist experimenting with AI on a budgetPick: Petals
Petals is free and runs on a single consumer GPU or Google Colab. It allows experimentation with state-of-the-art LLMs without any financial commitment.
Frequently Asked Questions
Petals vs Spider Cloud: which should you choose?
Spider Cloud and Petals serve entirely different needs: Spider Cloud is a web data extraction API optimized for AI agents, while Petals is a decentralized LLM inference network. If you need structured real-time web content for RAG or AI tools, Spider Cloud's cheap, reliable API with recent Browser AI commands is the obvious choice. If you want to run large models like Llama 405B on modest hardware without paying per token, Petals is a free but technically demanding alternative.
Can Spider Cloud crawl JavaScript-heavy sites?
Yes, Spider Cloud has a Browser Cloud with stealth anti-detection and new Browser AI commands (Act, Extract, Observe) that can interact with dynamic pages via WebSocket.
Does Petals require a fast internet connection?
Yes, because model shards are served by peers over the internet, inference speed depends on network latency and bandwidth of participating nodes.
What models does Petals support?
Petals supports Llama 3.1 (up to 405B), Mixtral (8x22B), Falcon (40B+), BLOOM (176B), and other compatible HuggingFace models.
How does Spider Cloud charge?
Usage-based: average $0.03 per 1,000 pages crawled. AI Studio add-on costs $6/month. Failed requests are not billed.
Can I fine-tune models on Petals?
Yes, Petals supports fine-tuning with PyTorch and Hugging Face Transformers, using decentralized compute from the network.
Is Spider Cloud open source?
The core is open source on GitHub, but the cloud API and some features (AI Studio, Browser Cloud) are proprietary.
Is Petals suitable for production applications?
Not for latency-sensitive or high-throughput apps. It's best for experimentation, research, and hobbyist use where throughput guarantees are not required.
Which tool is better for RAG workflows?
Spider Cloud is purpose-built for RAG—it extracts structured data from the web and integrates directly with LangChain, LlamaIndex, and other frameworks.
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Last reviewed: July 3, 2026