Scrapy alternatives: 11 web scraping tools compared for Python teams

Where it wins. Crawl, JavaScript rendering and typed schema extraction happen in a single API call, and robots.txt plus site Terms of Service are respected by default.

What to watch. There is no free plan, so it is priced for teams running real pipelines rather than one-off experiments.

Where it wins. Excellent developer experience, a well-loved open-source project, and markdown output tuned for token efficiency.

What to watch. The proxy mode decides the price: basic is 1 credit a page, enhanced is 5, and the default is auto, which retries a blocked page on enhanced and bills 5. Credits expire monthly on the self-serve plans and only roll over on Scale and Enterprise.

Where it wins. The largest proxy network in the category (150M+ residential IPs across 195 countries) and hundreds of prebuilt domain scrapers and ready-made datasets.

What to watch. It is a broad platform rather than a single LLM-ready endpoint, so output usually needs cleaning before you can embed it, and the pricing surface is complex.

Where it wins. A marketplace of thousands of prebuilt Actors, so common targets are already solved, plus a full automation and scheduling platform.

What to watch. Costs stack in layers: the plan buys a dollar allowance, Actor compute burns it at $0.13 to $0.20 per compute unit, and residential proxies add $7 to $8 per GB on top. Unused allowance expires monthly, and output is generic JSON rather than LLM-ready markdown.

Where it wins. Very easy to adopt, dependable rendering, and a Google Search API bundled into every tier.

What to watch. You mostly get HTML back, so the cleaning, chunking and structuring work for an LLM is still yours to do.

Where it wins. Strong price per request at volume and a very simple drop-in proxy API.

What to watch. It is proxy infrastructure first, so an LLM pipeline still needs its own parsing, boilerplate stripping and schema layer.

Where it wins. Focused on getting through Cloudflare, DataDome and PerimeterX where simpler fetchers fail.

What to watch. Protected requests consume far more credits than plain ones, so the effective price depends heavily on your targets.

Where it wins. Enterprise-grade unblocking, a large global proxy network, and dedicated parsers for major targets, plus a free Custom Parser for your own CSS or XPath rules.

What to watch. The headline result counts are best-case for a single cheap target: Oxylabs own FAQ notes the Micro plan's 98,000 results apply to Amazon, and spreading the same plan across mixed targets works out closer to 16,000 per target. Whole-site crawling means buying a second product.

Where it wins. No vendor bill at all, full control, deep crawling with BFS, DFS and best-first strategies, and output already shaped for RAG ingestion. It is the most popular open-source crawler in the category, with roughly 72,000 GitHub stars.

What to watch. You own the ops: proxy rotation, browser fleet, retries, blocks and upgrades. Free software is not free infrastructure.

Where it wins. A pre-built Knowledge Graph of more than 10 billion entities you can query instead of crawling, and computer-vision extraction that classifies and structures pages with no per-site rules to write.

What to watch. The first paid tier is $299 a month and credits are consumed fast (a Knowledge Graph record costs 25 credits, a data-center proxy request doubles the cost), so for plain RAG ingestion it is expensive and heavier than you need.

Where it wins. The open-source library (MIT, 28.4k GitHub stars) is a genuine option rather than a demo, it plugs into OpenAI, Groq, Azure, Gemini or a local Ollama model, and the managed API starts at $20 a month, below our own floor.

What to watch. The managed API bills per credit and the rate depends on the endpoint (extract costs 5 credits, stealth adds 5, a crawl adds 2 on top of per-page scrape cost), so cost per page is harder to predict. Self-hosting means you supply the LLM key and pay model tokens on every page.