Notes from the engineering team.
Technical deep dives, benchmarks, and perspectives on web data collection and AI infrastructure.
How to Scrape the Web at Scale from Your Terminal
A hands-on guide to using the Spider CLI for web crawling, scraping, and data extraction. Real examples, every crawl mode explained, and how to go from one page to millions without leaving your terminal.
Spider Browser Scores 85% on Browser Use's Stealth Benchmark
Browser Use open-sourced a stealth benchmark testing cloud browsers against 80 anti-bot protected sites. We ran it with Spider Browser and scored 85%.
Real-Time Web Search for RAG: Stop Feeding Your LLM Stale Data
Static document stores go stale within days. Here's how to add live web search to your RAG pipeline so your LLM always answers with current information. Complete implementations in Python with LangChain and vanilla code.
Web Search API for AI Agents: Search, Scrape, and Extract in One Call
Most AI agents need live web data but stitching together a SERP API, a scraper, and a parser is fragile and slow. Spider's Search API combines all three into a single request. Here's how it works and why it matters for agent reliability.
Introducing Silk: Our Custom AI Model for Web Data Extraction
Spider runs Silk, a purpose-built extraction model that converts raw HTML into structured data and solves captchas on dedicated GPU infrastructure. No external API calls, no per-token billing, no data leaving our network.
Case Study: How a RAG Pipeline Went from 6 Hours to 15 Minutes
A Series A AI company replaced three Python microservices, a proxy provider, and half an engineer's time with a single Spider API call. Here's exactly what changed.
The 7 Best Web Scraping APIs for AI in 2026
A data-grounded comparison of the top scraping APIs for LLM pipelines, RAG, and AI agents. Covers Spider, Firecrawl, Crawl4AI, ScrapingBee, Apify, Bright Data, and Jina Reader with real pricing, benchmarks, and honest trade-offs.
Spider vs. Oxylabs: One API vs. a Proxy Empire
Oxylabs built world-class proxies and then bolted scraping APIs on top. Spider is a single API that does both. Real pricing, benchmark data, and an honest look at where each tool fits.
Spider vs. Apify: Compute Units, Expired Credits, and What You Actually Pay
Apify's compute unit model combines memory, time, and proxy bandwidth into a billing formula most teams can't predict. Spider charges bandwidth plus compute with no expiring credits and no hidden proxy fees.
Spider vs. Bright Data: Enterprise Infrastructure vs. a Single API
Bright Data operates the largest proxy network in the world and sells six separate scraping products. Spider does the same job through one API with no minimum spend.
Spider vs. ZenRows: Credit Multipliers, Expiring Plans, and the Real Cost Per Page
ZenRows advertises millions of API credits, but a 25x multiplier for JS rendering plus premium proxies turns 250,000 credits into 10,000 requests. Spider has no multipliers, no expiring credits, and no mandatory subscription.
Spider vs. Crawl4AI: Managed API vs. Self-Hosted Python
Spider's managed Rust API versus Crawl4AI's free Python framework. Performance benchmarks, total cost of ownership, and when each tool is the right choice for AI data pipelines.
Spider vs. Firecrawl: Speed, Cost, and What Matters for AI Pipelines
A direct comparison of Spider and Firecrawl across performance, pricing, licensing, and AI features. Benchmark data, code examples, and an honest look at where each tool fits.
Spider vs. Jina Reader: Full Crawling vs. URL-to-Markdown
Jina Reader converts single URLs to markdown with a simple prefix. Spider crawls entire sites with proxy rotation, anti-bot bypass, and a full API. A comparison of scope, cost, and when each tool fits.
Spider vs. ScrapFly: Credit Multipliers vs. Transparent Pricing
ScrapFly's credit multiplier system makes costs hard to predict. Spider charges flat bandwidth + compute with no multipliers. A detailed comparison of pricing, features, and the hidden math behind credit-based scraping APIs.
Spider vs. Crawlera (Zyte): Predictable Pricing, Full Browser Control
Zyte classifies websites into complexity tiers that determine your cost — and you can't control which tier a site falls into. Spider charges bandwidth + compute with no tiers.
Spider vs. NetNut: Why a Proxy Network Alone Isn't Enough in 2026
NetNut sells proxy bandwidth. Spider handles the entire pipeline: crawling, rendering, stealth, extraction. Here's why a proxy alone can't keep up with modern anti-bot systems.
Spider vs. ScraperAPI: What Credit Multipliers Actually Cost You
ScraperAPI's credit multipliers can push costs past $7 per 1,000 pages on their best plan. Spider averages ~$0.48 per 1,000 pages with no multipliers — markdown output, browser sessions, and AI extraction included.
Spider vs. ScrapingBee: No Hidden Credit Multipliers, Real Browser Control
ScrapingBee charges up to 75 credits per request with its stealth proxy multiplier. Spider bills bandwidth + compute with no credit multipliers, plus full browser automation and AI extraction.
Spider Browser vs. Kernel vs. Browserbase: 999 URLs, 100% Pass Rate
Kernel benchmarked cold start speed. We benchmarked what matters: reliability across 999 URLs, 254 domains, and 18 categories, with a 100% success rate and 2.5s median end-to-end latency.
Spider MCP v2: Browser Automation for AI Agents
Spider's MCP server now ships 22 tools, including 9 browser automation tools that give AI agents direct control of cloud browsers with anti-bot bypass, proxy rotation, and session management.
Build a Production RAG Pipeline with Web Data in Under 30 Minutes
A step-by-step tutorial showing how to crawl websites with Spider, chunk the markdown, embed it, store it in a vector database, and query it. Implementations in LangChain, LlamaIndex, CrewAI, and AutoGen.
Building AI Agents That Browse the Web
Architecture patterns and working code for web-browsing AI agents. Covers research, monitoring, and data extraction agents using CrewAI and AutoGen with Spider as the scraping backend.
Building an MCP Server for Web Scraping
Build a production-ready MCP server in TypeScript that wraps Spider's API, giving any AI model the ability to crawl, scrape, search, and extract structured data from the web.
How to Bypass Cloudflare, DataDome, and PerimeterX in 2026
A technical breakdown of how modern anti-bot systems detect scrapers, why manual bypass is unsustainable, and how Spider handles it automatically.
The Developer's Guide to Choosing a Scraping Stack in 2026
A staff-engineer-level breakdown of every major scraping approach in 2026: DIY libraries, open source frameworks, managed APIs, AI-native extractors, and browser automation. Includes a decision matrix, cost analysis, and hidden-cost audit so you can pick the right stack without wasting a quarter on the wrong one.
Firecrawl vs. Crawl4AI vs. Spider: The Honest Benchmark
A rigorous head-to-head benchmark of the three most-discussed open source scraping tools in the AI space, measuring throughput, success rate, cost, markdown quality, and time to first result across 1,000 URLs.
Open Source Web Scraping: Why MIT License Matters
A practical breakdown of how open source licenses (MIT, Apache 2.0, AGPL, BSL) affect your ability to build commercial products on top of web scraping tools, and why Spider chose MIT.
Rust vs. Python for Web Scraping: Why We Rewrote Everything
The engineering story behind Spider's decision to abandon Python scrapers and rebuild from scratch in Rust. Concrete benchmarks, architecture decisions, and lessons learned.
Scraping 1 Million Pages: What Actually Happens
An engineering log of crawling 1 million pages across 10,000 domains with Spider's cloud API. Throughput curves, failure modes, cost breakdown, and lessons learned.
How Spider Went to Market: What Worked, What Didn't, and What We'd Do Differently
A candid look at how we built Spider's go-to-market from zero: the distribution channels that worked, the pricing mistakes, the content that actually converted, and the playbook for developer tools in 2026.
Top 5 Data Collection Platforms for AI and Web Scraping in 2026
A practical comparison of the leading data collection SaaS platforms, covering cost, speed, reliability, and AI readiness for developers building RAG pipelines, agents, and LLMs.
The True Cost of Web Scraping at Scale
A detailed cost breakdown of web scraping at 10K to 10M pages per month, comparing self-hosted Scrapy, Firecrawl, Apify, Crawl4AI, and Spider across infrastructure, proxies, engineering time, and total cost of ownership.
From Web Page to Vector Database: The Complete Pipeline
A deep technical walkthrough of the full data pipeline from raw URL to queryable vector store, covering crawling, extraction, chunking, embedding, and indexing with working code and cost analysis.
Web Scraping for AI Training Data: Legal and Technical Guide 2026
A comprehensive guide covering the legal frameworks, compliance requirements, and technical best practices for collecting web data to train AI models in 2026.
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