Petals vs Spider Cloud

Side-by-side comparison of features, pricing, and ratings

Analysis reviewed Live tool data as of 2026-10-18
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At a glance

DimensionPetalsSpider Cloud
Primary UseDecentralized LLM inference & fine-tuningWeb crawling & scraping for AI agents
Ease of UseRequires Python, PyTorch, HuggingFace; technical setupAPI + AI Studio, 1000+ scrapers
Key DifferentiatorRun large models on consumer GPU via p2p networkBrowser AI commands, AI extraction, stealth
Best ForDevs/researchers running LLMs locally & privatelyRAG 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.

Petals
Petals

Run Llama 3.1 405B and other huge language models at home over a BitTorrent-style decentralized inference network.

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Spider Cloud
Spider Cloud

Spider Cloud is a web scraping, crawling, and search API that streams live pages as markdown or JSON for AI agents and RAG pipelines.

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Pricing
Free
Freemium
Plans
$0/mo
$1/GB + $0.0001/CPU-min
From $6/mo
From $40/mo
Custom
Popularity
24 views
7.5k views
Skill Level
Intermediate
Intermediate
API Available
Platforms
CLIAPI
WebAPIPluginCLIDesktop
Categories
🖥️ GPU Cloud & Model Inference💾 Local & On-Device AI
🌐 Web Scraping & Search APIs🖱️ Browser & Computer-Use Agents
Features
BitTorrent-style decentralized inference across a volunteer GPU network
Generate text with Llama 3.1 up to 405B parameters
Run Mixtral 8x22B
Run Falcon 40B+
Run BLOOM 176B
Single-batch inference up to 6 tokens/sec for Llama 2 70B
Single-batch inference up to 4 tokens/sec for Falcon 180B
Fine-tune models for your tasks with PyTorch and Hugging Face Transformers
Execute custom paths through the model
Inspect model hidden states
Custom sampling methods
Run on a consumer-grade GPU
Run in Google Colab
Contribute your own GPU to serve model parts for others
Open source, no centralized server dependency
Scrape a single page into markdown, JSON, HTML, raw, or plain text
Crawl entire sites with each page streamed as one JSONL line in order as it finishes
Web search endpoint returns SERP results plus the scraped pages behind them
Custom browser renders like a user: scripts run, lazy images load, infinite scroll reaches the end
Load protected pages through a real browser engine with geo checks, returning a 200
Run full browser sessions over WebSocket with anti-detection and rotating exits
Send AI commands (Act, Extract, Observe) over the Browser API WebSocket
Send a prompt on a scrape or crawl request and get named fields back as JSON
Two-phase AI extraction: a fast model for most pages, a stronger model for complex layouts
Provider router sends scrape and crawl requests to outside providers on your own keys
Data connectors pipe crawl results into S3, GCS, Google Sheets, Azure Blob, or Supabase
Proxy network with 215M+ residential and ISP exits in 199 countries, rotated per request
MCP server at mcp.spider.cloud for Claude Code, Codex, Cursor, and Claude Desktop
1,000+ ready-made scraper examples across 32 categories, each with working code
API responses support JSON, JSONL, CSV, and XML; up to 10,000 core API requests per minute by default
Integrations
PyTorch
Hugging Face Transformers
Google Colab
GitHub
Discord
LangChain
LlamaIndex
CrewAI
FlowiseAI
Langflow
Dify
Agno
MCP
Claude Code
Codex
Cursor
Claude Desktop
Amazon S3
Google Cloud Storage
Google Sheets

What 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 (averaged across 2 sources)

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

No verifiable community signal. We scanned public discussion on Oct 14, 2026 and found posts matching the name “Spider Cloud”, but could not establish that they are about this product rather than something else sharing its name. Rather than publish a score built on the wrong subject, we publish none.

Who should pick which

  • Solo founder building an AI agent with RAG
    Pick: 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 locally
    Pick: 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 APIs
    Pick: 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 pipeline
    Pick: 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 budget
    Pick: 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