Embedbase 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

DimensionEmbedbaseSpider Cloud
Best ForQuick LLM prototypes, simple RAG, hackathonsAI agents, RAG pipelines, high-volume web scraping
Key FeatureUnified API for vector search + LLM text generationRust-powered web crawling, Browser AI commands, 1000+ scraper catalog
IntegrationsOpenAI, Google, Zapier, NotionLangChain, LlamaIndex, CrewAI, FlowiseAI, S3, GCS, Supabase
Latest NewsNo recent news capturedBrowser AI commands, scraper catalog, data connectors (Feb 2026)
Open SourceNoCore open source on GitHub

Spider Cloud is the clear winner for AI agents needing real-time, structured web data at scale with transparent pay-per-use pricing and extensive integrations. Embedbase offers a simpler API but lacks pricing transparency, recent updates, and the breadth of features needed for production RAG pipelines.

Try EmbedbaseTry Spider Cloud
Embedbase
Embedbase

Embedbase is a single API that combines a semantic search vector store with LLM text generation.

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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
Contact Sales
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
API
WebAPIPluginCLIDesktop
Categories
🗄️ Vector Databases & Retrieval📦 LLM App Frameworks & SDKs
🌐 Web Scraping & Search APIs🖱️ Browser & Computer-Use Agents
Features
Unified API for vector search and LLM text generation
Semantic search over custom datasets with .search()
Document ingestion with .add() and POST /v1/{dataset}
Model selection via .useModel(), including openai/gpt-3.5-turbo-16k
google/bison as an alternative generation model
Stated catalog of 5+ supported LLMs
Automatic embedding management (no embedding model config)
Attach metadata (path, title) to each indexed document
Update documents by id
Delete individual documents by id
Clear an entire dataset via /v1/{dataset}/clear
Context-aware prompting by joining retrieved results into a prompt
Bing web search endpoint at /v1/internet-search
JavaScript SDK (embedbase-js, npm i embedbase-js)
Python SDK
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
Zapier
Notion
OpenAI
Bing
Google
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: Embedbase 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.

Embedbase

30 mentions across 3 sources · 47% positive — mixed (averaged across 3 sources)

Hacker News, YouTube, GitHub

What users praise

  • • Extremely fast to build RAG apps: ingest PDF and chat in two lines.
  • • Unified API eliminates managing separate vector DB and LLM endpoints.
  • • Supports multiple LLMs including GPT-3.5-turbo and Google Bison.
  • • SDKs for JavaScript and Python accelerate integration.

What frustrates them

  • • Recurring bugs like float JSON errors and async call failures.
  • • Playground crashes intermittently, especially with large contexts.
  • • Default timeouts set too short, causing backend crashes.
  • • No local database support; relies on third-party hosted backend.

Researched Aug 18, 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

  • AI agent developer
    Pick: Spider Cloud

    Spider Cloud's Browser AI commands and real-time structured data extraction integrate seamlessly with AI agents, and its pay-per-use pricing scales with usage.

  • RAG pipeline builder
    Pick: Spider Cloud

    Spider Cloud's data connectors to S3, GCS, and Supabase, plus 1000+ ready-made scrapers, make it ideal for feeding up-to-date web data into RAG systems.

  • Hackathon participant
    Pick: Embedbase

    Embedbase's simple unified API for vector search and LLM text generation allows rapid prototyping without managing infrastructure.

  • High-volume scraping team
    Pick: Spider Cloud

    Spider Cloud's Rust engine, 99.9% success rate, and low cost per page ($0.03/1000) make it cost-effective for large-scale scraping.

  • Prototype-first startup
    Pick: Embedbase

    Embedbase's quick setup and integration with Zapier and Notion enable fast MVP development for semantic search or Q&A features.

Frequently Asked Questions

Embedbase vs Spider Cloud: which should you choose?

Spider Cloud is the clear winner for AI agents needing real-time, structured web data at scale with transparent pay-per-use pricing and extensive integrations. Embedbase offers a simpler API but lacks pricing transparency, recent updates, and the breadth of features needed for production RAG pipelines.

Does Spider Cloud offer a free tier?

Yes, Spider Cloud is freemium. Specific free limits are not detailed, but failed requests are not billed and the core is open-source.

Does Embedbase support self-hosting?

No, Embedbase is a cloud API with no mention of on-premises or air-gapped deployment.

Which tool is better for RAG pipelines?

Spider Cloud is better for RAG because it provides real-time web data with structured output and connectors to vector databases like Supabase.

Can I use Embedbase for semantic search only?

Yes, Embedbase's .search() function allows semantic search on custom datasets without needing text generation.

Does Spider Cloud have a JavaScript SDK?

Spider Cloud integrates with LangChain, LlamaIndex, and other frameworks but its own SDK availability is not specified; the API is accessible via HTTP.

What LLMs does Embedbase support?

Embedbase supports models like openai/gpt-3.5-turbo-16k and google/bison for text generation.

Which tool has better anti-bot detection?

Spider Cloud features Browser Cloud with stealth anti-detection and an unblocker with rotating proxies and retries.

Is Embedbase's pricing publicly available?

No, Embedbase requires contacting sales for pricing; there are no public tiers.

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Explore each tool further

Embedbase
View Embedbase reviewEmbedbase alternatives
Spider Cloud
View Spider Cloud reviewSpider Cloud alternatives

Browse these categories

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