TorchTPU vs Spider Cloud
Side-by-side comparison of features, pricing, and ratings
At a glance
| Dimension | TorchTPU | Spider Cloud |
|---|---|---|
| Pricing | Paid (depends on TPU hardware rental; software free/open-source) | Freemium (free credits + pay-as-you-go ~$0.03/1k pages; AI Studio $6/mo add-on) |
| Primary Use | Run PyTorch workloads natively on Google Cloud TPUs | Web crawling/scraping API for AI agents and RAG pipelines |
| Key Technology | Torch-XLA backend, Fused Eager mode, XLA compiler | Rust engine, Silk AI extraction, Browser AI commands (Act/Extract/Observe) |
| Target Audience | PyTorch developers scaling training/inference on TPUs | AI/ML developers needing real-time web data |
| Integration Ecosystem | JAX, vLLM, PyTorch Lightning, Hugging Face Transformers, XLA, GKE | LangChain, LlamaIndex, CrewAI, FlowiseAI, AutoGen, Agno, Dify, S3, GCS, Sheets, Supabase |
| Latest News | No recent news available | Browser 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.
AI web scraping API: crawl, scrape, search any site into markdown or JSON at 10k req/min.
Visit WebsiteWhat 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
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
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 agentPick: 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 trainingPick: 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 pipelinePick: 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 TPUPick: 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 volumePick: 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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Last reviewed: July 3, 2026