Onnx vs Spider Cloud

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

Analysis reviewed Live tool data as of 2026-10-08
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At a glance

DimensionOnnxSpider Cloud
Primary FunctionML model interoperability formatWeb crawling, scraping, search API
Target UserML engineers, data scientistsAI agents, RAG pipelines, LLMs
Output TypeStandardized .onnx model filesStructured data (Markdown, JSON, etc.)
Ease of UseFramework integration requiredAPI-based, AI Studio for natural language
Best ForCross-framework model deploymentReal-time web data extraction

Spider Cloud and ONNX serve entirely different purposes. If you need to extract web data for AI agents or RAG pipelines, Spider Cloud is the obvious choice with its Rust-powered crawling, AI Studio, and low cost per page. If you're an ML engineer aiming to deploy models across frameworks without vendor lock-in, ONNX is essential. They're not directly comparable; pick based on your task: data acquisition vs. model interoperability.

Try OnnxTry Spider Cloud
Onnx
Onnx

ONNX is an open format for machine learning models, giving you a common operator set and file format so a model trained in one framework runs in another

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Spider Cloud
Spider Cloud

Spider Cloud is a web scraping and crawling API that turns live pages into markdown or JSON for agents and RAG pipelines.

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Pricing
Free
Freemium
Plans
—
$1/GB + $0.0001/CPU-min
From $6/mo
From $40/mo
Custom
Popularity
4 views
7.5k views
Skill Level
Intermediate
Intermediate
API Available
Platforms
CLIAPI
WebAPIPluginCLIDesktop
Categories
⚙️ Developer Infrastructure
🌐 Web Scraping & Search APIs🖱️ Browser & Computer-Use Agents
Features
Standardized .onnx model file format
Common operator set for deep learning and traditional ML models
Directed acyclic graph (DAG) model representation: nodes as operators, edges as tensors
Tensor and standard data type support across the graph
Model metadata carried alongside the graph for documentation and provenance
Framework-agnostic export and import across PyTorch, TensorFlow, scikit-learn, and Keras
Conversion tools including torch.onnx.export and tf2onnx
Compatibility with multiple runtimes and compilers such as ONNX Runtime and TensorRT
Hardware optimization through ONNX-compatible runtimes and libraries on CPU, GPU, and NPU
Extensible operator set for custom operators
Browser inference via ONNX Runtime Web (Inflect TTS v2)
Local agent inference on desktop (Screenpipe)
Community-built engine running Kimi K3 on consumer laptops
Open governance as an LF AI graduate project with Special Interest Groups and working groups
Public Slack community and published contribution guide
Scrape a single page into markdown, JSON, HTML, raw text, or plain text
Crawl entire sites with each page streamed as one JSONL line in order the moment it finishes
Web search endpoint returns SERP results plus the scraped pages behind them in one call
Custom browser renders like a user: scripts run, lazy images load, infinite scroll completes
Unblocker loads protected pages through a real browser engine with geo checks and a 200
Browser Cloud runs full sessions 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 the 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
Requests stream back as they land, in order, without waiting for the last URL
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
Integrations
PyTorch
TensorFlow
scikit-learn
Keras
ONNX Runtime
TensorRT
Caffe2
Slack
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: Onnx 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.

Onnx

71 mentions across 5 sources · 66% positive (averaged across 5 sources)

Hacker News, YouTube, Stack Overflow, GitHub, Lemmy

What users praise

  • • Framework-agnostic export from PyTorch, TensorFlow, scikit-learn.
  • • Hardware acceleration via ONNX Runtime across CPU, GPU, NPU.
  • • Runs in browser via ONNX Runtime Web (Inflect TTS v2).
  • • Boosts embedding inference 14×+ in Manticore.

What frustrates them

  • • Steep learning curve for export and compatibility issues.
  • • Operator gaps block conversion of models with custom ops.
  • • C++20 compile errors with ONNX Runtime headers.
  • • Slow CPU inference (~900ms per frame) without GPU.

Researched Aug 31, 2026

Spider Cloud

No verifiable community signal. We scanned public discussion on Oct 7, 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 a RAG chatbot
    Pick: Spider Cloud

    Spider Cloud provides real-time web crawling and structured data extraction via a simple API, ideal for feeding context into a RAG pipeline. Its low cost ($0.03/1000 pages) and LangChain integration make it easy for a solo developer to implement.

  • ML engineer deploying a PyTorch model to production
    Pick: Onnx

    ONNX standardizes the model format, allowing the engineer to train in PyTorch and deploy on any ONNX-compatible runtime (e.g., TensorRT, ONNX Runtime), avoiding vendor lock-in. It's free and widely supported.

  • Data scientist needing to scrape 10,000 competitor pages weekly
    Pick: Spider Cloud

    Spider Cloud's high success rate (99.9%) and low per-page cost make it cost-effective for large-scale scraping. The unblocker and rotating proxies help bypass anti-bot measures, and structured output simplifies analysis.

  • Startup wanting to run ML models on edge devices
    Pick: Onnx

    ONNX allows exporting models from any framework to run on diverse hardware (CPU, GPU, NPU) via optimized runtimes. This flexibility is crucial for edge deployment without rewriting models.

Frequently Asked Questions

Onnx vs Spider Cloud: which should you choose?

Spider Cloud and ONNX serve entirely different purposes. If you need to extract web data for AI agents or RAG pipelines, Spider Cloud is the obvious choice with its Rust-powered crawling, AI Studio, and low cost per page. If you're an ML engineer aiming to deploy models across frameworks without vendor lock-in, ONNX is essential. They're not directly comparable; pick based on your task: data acquisition vs. model interoperability.

Can I use ONNX to scrape web data?

No, ONNX is a machine learning model format standard, not a web scraping tool. For scraping, use Spider Cloud.

Is Spider Cloud open-source?

Its core is open-source on GitHub, but the cloud API and AI Studio are proprietary services.

Does ONNX support training?

ONNX is an inference format; training is done in frameworks like PyTorch or TensorFlow, then exported to ONNX.

What integrations does Spider Cloud offer?

LangChain, LlamaIndex, CrewAI, FlowiseAI, AutoGen, Agno, Dify, and data connectors for S3, GCS, Google Sheets, Azure Blob, Supabase.

Can I convert Spider Cloud output to ONNX?

No, that is not a supported workflow. Spider Cloud outputs text/structured data, not ML models.

Does ONNX have a visual interface?

ONNX itself is a format; tools like Netron provide visualizations of ONNX models.

What is the latest news about Spider Cloud?

March 2026: Browser AI commands added (Act, Extract, Observe) via WebSocket.

What is the latest news about ONNX?

July 2026: Manticore Search claims 14× faster ONNX embeddings with a rewritten inference path.

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Explore each tool further

Onnx
View Onnx reviewOnnx 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