Attention Sinks 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

DimensionAttention SinksSpider Cloud
Primary FunctionExtend LLM context window with constant memoryWeb crawling/scraping API for AI agents
Target UserDevelopers deploying long-running chatbots on limited hardwareDevelopers building RAG pipelines and AI agents
IntegrationHugging Face Transformers (drop-in)LangChain, LlamaIndex, CrewAI, etc.
Latest NewsNo direct product news; ecosystem updates onlyBrowser AI commands, scraper catalog, data connectors (2026)
Best ForInfinite chat without memory growthReal-time web data for AI agents

Spider Cloud and Attention Sinks solve completely different problems: one is a web scraping API for feeding live data into AI pipelines, the other is a library for extending LLM context windows with constant VRAM. Your choice depends on whether you need external data or longer conversations. If you're building a RAG agent that requires up-to-date web content, Spider Cloud is the obvious pick; if you're deploying a chatbot that needs to run indefinitely on limited hardware, Attention Sinks is the way to go.

Try Attention SinksTry Spider Cloud
Attention Sinks
Attention Sinks

An open-source Python package that keeps Llama, Mistral, MPT, Falcon and Pythia chat models fluent across hundreds of prompts on flat VRAM.

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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
$1/GB + $0.0001/CPU-min
From $6/mo
From $40/mo
Custom
Popularity
3 views
7.5k views
Skill Level
Intermediate
Intermediate
API Available
Platforms
—
WebAPIPluginCLIDesktop
Categories
📦 LLM App Frameworks & SDKs
🌐 Web Scraping & Search APIs🖱️ Browser & Computer-Use Agents
Features
Drop-in AutoModel replacement imported from attention_sinks instead of transformers
Window attention that permanently retains the first 4 attention sink tokens
Constant VRAM once generation passes the window size, instead of linear growth
Fluent generation across hundreds of sequential prompts in chat
Configurable window size, with 1024 used in the published experiments
8192-token window matches baseline perplexity at 8000 tokens near 14.85GB VRAM
Cache-relative position assignment keeping sink tokens adjacent to live context
Supports Llama, Mistral, MPT, Falcon and GPT-NeoX (Pythia) model families
No retraining required on supported pretrained checkpoints
Compatible with AutoModelForCausalLM and Transformers pipelines
Open-source Python package installable via PyPI and GitHub
Runs on your own GPU or on Hugging Face Inference Endpoints, Jobs or Spaces
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
Hugging Face Transformers
Hugging Face Hub
Hugging Face Inference Endpoints
Hugging Face Jobs
Hugging Face Spaces
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: Attention Sinks 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.

Attention Sinks

65 mentions across 4 sources · 51% positive — mixed (averaged across 4 sources)

Hacker News, YouTube, GitHub, Lemmy

What users praise

  • • Constant memory usage regardless of conversation length — a real fix.
  • • Works with Llama 2, Mistral, MPT, Falcon, Pythia out of the box.
  • • No retraining needed — drop into any pretrained chat model.
  • • One-line code change from standard Transformers integration.

What frustrates them

  • • Breaks with recent transformers versions (KeyError, etc.).
  • • No Flash Attention support for Qwen models.
  • • Qwen models throw TypeError — limited architecture compatibility.
  • • GPTQ quantized models not supported.

Researched Sep 1, 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

  • RAG Pipeline Developer
    Pick: Spider Cloud

    Needs to fetch and structure up-to-date web content for LLM context; Spider Cloud provides direct integrations with LangChain and LlamaIndex, plus structured output formats.

  • LLM Memory Researcher
    Pick: Attention Sinks

    Studying efficient attention mechanisms requires experimentation with transformer internals; Attention Sinks is open-source and built on Hugging Face for easy modification.

  • Chatbot Developer (limited GPU)
    Pick: Attention Sinks

    Deploying a long-running chatbot on a single GPU needs constant memory; Attention Sinks enables endless generation without increasing VRAM usage.

  • AI Agent Builder
    Pick: Spider Cloud

    Agents need real-time web data via search endpoint and structured output; Spider Cloud's Browser AI commands and unblocker support dynamic web interactions.

  • Budget-Conscious Hobbyist
    Pick: Attention Sinks

    Zero cost and minimal setup; free library extends existing models without extra fees, perfect for personal projects.

Frequently Asked Questions

Attention Sinks vs Spider Cloud: which should you choose?

Spider Cloud and Attention Sinks solve completely different problems: one is a web scraping API for feeding live data into AI pipelines, the other is a library for extending LLM context windows with constant VRAM. Your choice depends on whether you need external data or longer conversations. If you're building a RAG agent that requires up-to-date web content, Spider Cloud is the obvious pick; if you're deploying a chatbot that needs to run indefinitely on limited hardware, Attention Sinks is the way to go.

What is the main difference between Spider Cloud and Attention Sinks?

Spider Cloud is a web crawling/scraping API for extracting data from websites, while Attention Sinks is a library for extending LLM context windows with constant memory. They solve different problems.

Can I use Spider Cloud to improve LLM memory?

No, Spider Cloud extracts external data. For LLM memory issues, use Attention Sinks.

Does Attention Sinks work with any LLM?

It supports Llama, Mistral, MPT, Falcon, and Pythia via Hugging Face Transformers.

What is the cost of Spider Cloud?

Usage-based starting at $0.03/1k pages, with AI Studio add-on at $6/mo. Failed requests are free.

Is Attention Sinks free?

Yes, it is open-source and completely free.

Does Spider Cloud integrate with LLM frameworks?

Yes, it integrates with LangChain, LlamaIndex, CrewAI, AutoGen, and more.

Can Attention Sinks be used in production?

Yes, but it is a library without official support; stability depends on the underlying model and infrastructure.

Which tool is better for a RAG application?

Spider Cloud, because it provides web data retrieval and structured output essential for RAG pipelines.

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Explore each tool further

Attention Sinks
View Attention Sinks reviewAttention Sinks alternatives
Spider Cloud
View Spider Cloud reviewSpider Cloud alternatives

Browse these categories

Best AI LLM App Frameworks & SDKs toolsBest AI Web Scraping & Search APIs toolsBest AI Browser & Computer-Use Agents tools
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Last reviewed: July 3, 2026