TorchTPU 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

DimensionTorchTPUSpider Cloud
Primary UseRun PyTorch workloads natively on Google Cloud TPUsWeb crawling/scraping API for AI agents and RAG pipelines
Key TechnologyTorch-XLA backend, Fused Eager mode, XLA compilerRust engine, Silk AI extraction, Browser AI commands (Act/Extract/Observe)
Target AudiencePyTorch developers scaling training/inference on TPUsAI/ML developers needing real-time web data
Integration EcosystemJAX, vLLM, PyTorch Lightning, Hugging Face Transformers, XLA, GKELangChain, LlamaIndex, CrewAI, FlowiseAI, AutoGen, Agno, Dify, S3, GCS, Sheets, Supabase
Latest NewsNo recent news availableBrowser AI WebSocket commands, redesigned logs, scraper catalog (1,000+ examples), data connectors (Feb-Mar 2026)

If you need to feed AI agents or RAG pipelines with live web data, Spider Cloud is the clear choice with its low-cost crawling, structured output, and new Browser AI commands. If you're a PyTorch developer looking to leverage Google Cloud TPUs for large-scale training without model rewrites, TorchTPU is essential. These tools serve entirely different purposes — pick based on whether your bottleneck is data acquisition or model acceleration.

Try TorchTPUTry Spider Cloud
TorchTPU
TorchTPU

Run PyTorch natively on Google Cloud TPUs with eager-first execution and minimal code changes.

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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
Freemium
Freemium
Plans
$300 in free credits
Usage-based
Up to $350,000 in Cloud credits
$1/GB + $0.0001/CPU-min
From $6/mo
From $40/mo
Custom
Popularity
10 views
7.5k views
Skill Level
Intermediate
Intermediate
API Available
Platforms
WebAPICLI
WebAPIPluginCLIDesktop
Categories
⚙️ Developer Infrastructure
🌐 Web Scraping & Search APIs🖱️ Browser & Computer-Use Agents
Features
Run PyTorch eagerly on TPUs with minimal code changes
Fused Eager mode for measured 50-100%+ throughput gains
Distributed training with PyTorch DDP and FSDP
Run Ray on TPU: Ray Train, Ray Serve and Ray Data
FP8 mixed precision training on Ironwood, Google's first 8-bit float TPU generation
Open-source torch-xla backend developed by Google
vLLM unified backend for JAX and PyTorch inference on TPU
Day 0 Gemma 4 support through vLLM on TPU
Inference recipes for open-weight models on Ironwood, including Qwen3 and GPT-OSS
Optimization playbook for Qwen 3.5-397B MoE on Ironwood
MaxText for large-scale LLM pre-training on TPU
Elastic training with MaxText for fault tolerance on multi-node runs
Tunix for post-training and agentic RL on TPU
XProf kernel profiling and bottleneck analysis for TPU workloads
Compatibility with PyTorch Lightning and Hugging Face Transformers
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
vLLM
PyTorch Lightning
Hugging Face Transformers
JAX
XLA
MaxText
Tunix
Ray
Google Kubernetes Engine (GKE)
TensorBoard
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: TorchTPU 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.

TorchTPU

14 mentions across 3 sources · 57% positive — mixed (averaged across 3 sources)

Hacker News, Product Hunt, Lemmy

What users praise

  • • Minimal code changes to run PyTorch on TPU hardware.
  • • Fused Eager mode offers 50-100% speedup without code rewrites.
  • • Integrates seamlessly with PyTorch Lightning and HuggingFace.
  • • Supports distributed training with DDP and FSDP out of the box.

What frustrates them

  • • Past PyTorch/XLA implementations were unreliable with silent failures.
  • • Peak performance requires manual optimization beyond basic porting.
  • • TPU lock-in: models don't easily port to other hardware.
  • • Documentation and community support still maturing.

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
    Pick: Spider Cloud

    You need real-time web data for RAG or agent context; Spider Cloud's freemium model and low cost ($0.03/1k pages) are ideal for small budgets, and its Browser AI commands automate interactions.

  • ML engineer at a startup scaling LLM training
    Pick: TorchTPU

    You want to use PyTorch on Google Cloud TPUs without rewriting code; TorchTPU's Fused Eager mode and distributed training support large models cost-effectively.

  • Data scientist prototyping RAG pipeline
    Pick: Spider Cloud

    Spider Cloud's LangChain integration and structured output make it easy to fetch and feed web content into vector stores; free credits let you test.

  • Enterprise team migrating from GPU to TPU
    Pick: TorchTPU

    TorchTPU is a drop-in replacement for CUDA, so existing PyTorch code runs with minimal changes; supports FP8 and 100K+ chips for massive scale.

  • Researcher needing to scrape at high volume
    Pick: Spider Cloud

    Spider Cloud's Rust engine provides fast crawling with 99.9% success, and data connectors (S3, GCS) pipe results directly into storage; no billing for failed requests.

Frequently Asked Questions

TorchTPU vs Spider Cloud: which should you choose?

If you need to feed AI agents or RAG pipelines with live web data, Spider Cloud is the clear choice with its low-cost crawling, structured output, and new Browser AI commands. If you're a PyTorch developer looking to leverage Google Cloud TPUs for large-scale training without model rewrites, TorchTPU is essential. These tools serve entirely different purposes — pick based on whether your bottleneck is data acquisition or model acceleration.

Can Spider Cloud handle JavaScript-heavy websites?

Yes, Spider Cloud offers Browser AI commands via WebSocket (Act, Extract, Observe) that can interact with dynamic pages, plus stealth anti-detection measures.

Does TorchTPU require rewriting my PyTorch model?

No, it is designed as a drop-in replacement for CUDA devices; most existing code works with minimal changes (e.g., changing device to 'xla').

What is the cost of Spider Cloud per page?

Approximately $0.03 per 1,000 pages crawled; failed requests are not billed. AI Studio adds $6/month.

What TPU hardware does TorchTPU support?

It supports Google Cloud TPUs including v2-v5, and Ironwood with FP8 support for mixed precision training.

Can I use Spider Cloud without an API key?

Yes, the open-source core is available on GitHub for self-hosting, but cloud API requires registration for full features.

Is TorchTPU integrated with Hugging Face Transformers?

Yes, it integrates seamlessly, allowing you to train and run Transformers on TPUs with no code changes.

Does Spider Cloud offer a free tier?

Yes, it provides free credits to get started; exact amount not specified but typical for freemium services.

Can TorchTPU be used for inference?

Yes, it supports inference via vLLM unified backend, which works with both JAX and PyTorch models.

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Explore each tool further

TorchTPU
View TorchTPU reviewTorchTPU alternatives
Spider Cloud
View Spider Cloud reviewSpider Cloud alternatives

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Best AI Developer Infrastructure toolsBest AI Web Scraping & Search APIs toolsBest AI Browser & Computer-Use Agents tools
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Last reviewed: July 3, 2026