Spider Blog
Technical deep dives, benchmarks, and perspectives on web data collection and AI infrastructure.
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.
A comprehensive guide covering the legal frameworks, compliance requirements, and technical best practices for collecting web data to train AI models in 2026.
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.
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.
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.
The engineering story behind Spider's decision to abandon Python scrapers and rebuild from scratch in Rust. Concrete benchmarks, architecture decisions, and lessons learned.
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.
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.
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.
A technical breakdown of how modern anti-bot systems detect scrapers, why manual bypass is unsustainable, and how Spider handles it automatically.
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.
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.
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.
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.
Join thousands of developers using Spider to power their data pipelines.