From the Spider Engineering Team

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.

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.

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.

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.

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.

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.

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.

ScraperAPI's credit multipliers can push costs past $7 per 1,000 pages on their best plan. Spider averages ~$0.65 per 1,000 pages with no multipliers — markdown output, browser sessions, and AI extraction included.

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.

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.

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.

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.

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'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.

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.

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.

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.

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.

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.