Quivr vs Spider Cloud

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

Analysis reviewed Live tool data as of 2026-10-09
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At a glance

DimensionQuivrSpider Cloud
Ease of Use5-line RAG setup, developer-focusedAPI with curl, AI Studio for natural language
Core FunctionRAG framework for document Q&AWeb crawling/scraping API for AI agents
Data SourcesLocal files (PDF, TXT, Markdown, etc.)Live web pages, search queries
Output FormatsVector embeddings, chat responsesMarkdown, HTML, JSON, CSV, XML, plain text
Open SourceFull open-source (MIT)Open-source core available

Choose Quivr if you need to add document Q&A to your app fast with flexible LLM/vector store choices. Pick Spider Cloud if you need real-time web data for AI agents or RAG pipelines. They complement rather than compete: Quivr for local file ingestion, Spider Cloud for live web scraping.

Try QuivrTry Spider Cloud
Quivr
Quivr

Open-source Python framework that adds retrieval-augmented document Q&A to your app in five lines of code

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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
Freemium
Freemium
Plans
$0/mo
Contact
$1/GB + $0.0001/CPU-min
From $6/mo
From $40/mo
Custom
Popularity
21 views
7.5k views
Skill Level
Intermediate
Intermediate
API Available
Platforms
CLIAPI
WebAPIPluginCLIDesktop
Categories
📦 LLM App Frameworks & SDKs🗄️ Vector Databases & Retrieval
🌐 Web Scraping & Search APIs🖱️ Browser & Computer-Use Agents
Features
Five-line RAG setup with quivr-core
Brain.from_files() ingestion from a list of file paths
brain.ask() question answering over ingested files
Works with any LLM, including OpenAI, Anthropic, Mistral and Gemma
Works with vector stores including Faiss and PGVector
Ingests PDF, TXT and Markdown files
Custom parsers for additional file formats
Megaparse integration for advanced document parsing
Add internet search as a tool in the RAG workflow
Customizable RAG workflows via tools
StorageBase interface with LocalStorage for chat history
Transparent storage backend for chat history
Voice chatbot example built with Chainlit
Voice chatbot example built with Flask
Runnable examples for basic ingestion, basic RAG and RAG with web search
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
OpenAI
Anthropic
Mistral
Gemma
Megaparse
Faiss
PGVector
Chainlit
Flask
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: Quivr 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.

Quivr

6 mentions across 3 sources · 40% positive — mixed (averaged across 3 sources)

Hacker News, Product Hunt, GitHub

What users praise

  • • Five-line code setup for RAG integration is highly appealing for beginners.
  • • Support for any LLM and vector store provides flexibility without vendor lock-in.
  • • Open-source MIT license allows full customization for specific use cases.
  • • Modular design lets users swap parsers, LLMs, or storage without rewrites.

What frustrates them

  • • Setup process is buggy and lacks updated documentation for common Linux distros.
  • • Critical issues like 'Cannot add Brain' remain unresolved for years.
  • • Support response is slow or absent for open-source issues.
  • • Product Hunt reception was very low (3 upvotes) indicating limited buzz.

Researched Jul 3, 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 document Q&A bot
    Pick: Quivr

    Quivr's 5-line RAG setup lets you quickly add Q&A to internal docs using local files, with flexibility to choose free LLMs like Mistral or Groq.

  • AI agent developer needing real-time web data
    Pick: Spider Cloud

    Spider Cloud's API with stealth anti-detection and AI extraction is built for agents that need live web context, with low $0.03/1k pages cost.

  • RAG pipeline engineer combining internal + web data
    Pick: Spider Cloud

    Spider Cloud's data connectors (S3, GCS, Supabase) and AI Studio make it easy to feed web data into existing RAG pipelines alongside local files from Quivr.

  • Prototyper needing quick PoC
    Pick: Quivr

    Quivr's minimal setup and support for any LLM/vector store let you test RAG concepts fast without paying per request.

  • Team scraping thousands of pages monthly
    Pick: Spider Cloud

    Spider Cloud's usage-based pricing at $0.03/1k pages and unblocker with rotating proxies deliver reliable, cost-effective high-volume scraping.

Frequently Asked Questions

Quivr vs Spider Cloud: which should you choose?

Choose Quivr if you need to add document Q&A to your app fast with flexible LLM/vector store choices. Pick Spider Cloud if you need real-time web data for AI agents or RAG pipelines. They complement rather than compete: Quivr for local file ingestion, Spider Cloud for live web scraping.

Can Quivr scrape web pages?

Not natively. Quivr focuses on local files. You can add internet search as a tool, but it's not a web scraper.

Can Spider Cloud handle PDF files?

Spider Cloud crawls and scrapes web pages. For PDFs hosted online, it can download and extract text (via markdown or plain text).

Which is better for RAG?

It depends: Quivr for document Q&A from local files, Spider Cloud for retrieving live web data to augment RAG pipelines. They can work together.

Is either tool open-source?

Both: Quivr is fully open-source (MIT), Spider Cloud has an open-source core on GitHub.

Does Quivr support voice chatbots?

Yes, but via external integrations like Chainlit or Flask (examples provided).

Does Spider Cloud support real-time streaming?

No, it returns results after scraping. For real-time, you need WebSocket-based tools.

What is AI Studio in Spider Cloud?

A $6/mo add-on that lets you describe crawling tasks in natural language (e.g., 'extract all product prices').

Can I use my own LLM with Quivr?

Yes, Quivr supports OpenAI, Anthropic, Mistral, Gemma, Groq, and any LLM via integration.

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Explore each tool further

Quivr
View Quivr reviewQuivr 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 Vector Databases & Retrieval toolsBest AI Browser & Computer-Use Agents tools
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Last reviewed: July 3, 2026