Diffbot alternatives compared: clean page data for RAG without a knowledge graph bill

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 headline 1 credit a page is the plain scrape only. Asking for structured JSON adds 4 credits, so a page you want back as typed fields is 5 credits, which turns Standard from $0.83 into $4.15 per 1,000 pages. PDF parsing adds 1 credit a PDF page, a prompt injection check adds 4, zero data retention adds 1, Map is 1 credit a call, Search is 2 per 10 results, and Interact is 2 to 7 credits a browser minute. Credits are charged whenever the request is processed, regardless of what the target returns.

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. JavaScript rendering is on by default and costs 5 credits, so a Freelance plan is 50,000 rendered pages rather than the 250,000 the credit count suggests. Premium proxy is 10 credits alone or 25 with rendering, and stealth proxy is 75. You also 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. The multipliers decide the bill: a flat request is 1 credit, render adds 10, premium adds 10, premium with render is 25, ultra premium with render is 75, and Amazon, SERP and LinkedIn targets are 5, 25 and 30. 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. The multipliers set the real price, not the headline credit count: a plain fetch is 1 credit, a JavaScript-rendered page is 5, premium proxies are 10, and premium with rendering is 25, which is the ceiling. Residential bandwidth is billed at a flat 25,000 credits per GB, and Browser Sessions add 5 credits a minute on top of bandwidth. Launch at $57 therefore buys about 50,000 rendered pages. You also get HTML back, so the cleaning and structuring work for an LLM is still yours.

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. 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.