Quick answer: the best Diffbot alternative depends on which half of Diffbot you actually use. If you need entity-level data about companies and people, Zyte and Bright Data datasets get closest. If you need clean, structured content from web pages to feed an LLM, which is what most teams shopping for alternatives are really doing, then link.sc, Firecrawl, or Jina Reader will do the job for a fraction of the price.
Diffbot is genuinely good technology. It parses pages with computer vision and machine learning, maintains a knowledge graph with billions of entities, and has been doing "AI-powered extraction" since before that phrase was a marketing cliche.
It is also priced like enterprise software, because it is enterprise software. Plans start at $299 per month as of this writing, metered in credits, and the interesting features sit in higher tiers. If you came here because you got sticker shock, you are the person this post is for.
The Real Question: Knowledge Graph or Page Extraction?
Diffbot bundles two products that solve different problems, and untangling them tells you which alternatives are even relevant.
The Knowledge Graph is a pre-crawled database of organizations, people, articles, and products that you query like a search engine for facts. You are paying for Diffbot's crawl of the web, already structured and entity-resolved. Nothing cheap replaces this one-for-one, because the expensive part is the years of crawling behind it.
The Extract APIs take a URL and return structured JSON: article text, author, date, product price, and so on. This is classic data extraction, and it is the part most subscribers actually use day to day.
Here is the shift that changed the math. Diffbot's extraction pricing made sense when turning messy HTML into structured fields required a proprietary ML pipeline. In 2026 the LLM you already pay for does that transformation trivially well, provided you hand it clean page content instead of 200KB of raw HTML. So the job to be done moved from "extract structured fields for me" to "get me the readable page, cheaply and reliably, at scale."
That reframing is why pay-per-fetch APIs have eaten this category from below.
The Alternatives, Compared
| Tool | Best for | Pricing model | Entry cost |
|---|---|---|---|
| link.sc | LLM pipelines, agents, RAG ingestion | Per request | Free tier, 500 requests/month |
| Firecrawl | Crawling whole sites into Markdown | Credits | Free tier, then from $16/month |
| Jina Reader | Quick single-URL to Markdown conversion | Per token | Free tier with rate limits |
| Zyte API | Large-scale scraping with per-site pricing | Per request, varies by site | Pay as you go |
| Apify | Prebuilt scrapers for specific sites | Compute units | Free tier, then from $39/month |
| Bright Data | Proxy infrastructure and bulk datasets | Per GB / per record | Pay as you go |
| Diffbot | Entity data, knowledge graph queries | Credits | From $299/month |
A closer look at the ones I would shortlist.
link.sc: fetch-first, priced per request
link.sc takes any URL and returns the page as clean Markdown, with ads, navigation, and scripts stripped out. That format matters more than it sounds: Markdown cuts token usage by 60 to 80 percent versus raw HTML, which we broke down in HTML to Markdown for LLMs.
curl -X POST https://api.link.sc/v1/fetch \
-H "x-api-key: YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{"url": "https://example.com/article"}'
Pair the response with a cheap model call and a JSON schema, and you have rebuilt Diffbot's Article API for pennies. The part you cannot rebuild yourself is reliability on hostile pages, and that is where a fetch-first service earns its keep: link.sc runs an escalation ladder from plain HTTP up to stealth browser rendering, so JavaScript-heavy and bot-protected sites come back as content instead of errors.
Pricing is one request, one unit. No credit arithmetic, no per-feature multipliers. There is also a search API and an MCP server, so agents can find pages and read them through one provider.
Firecrawl: crawl a whole site into Markdown
Firecrawl's strength is the crawl endpoint: point it at a domain and it walks the site, returning every page as Markdown or structured JSON. If your Diffbot use case was Crawlbot, this is the closest modern equivalent, and its LLM extraction option maps loosely to Diffbot's automatic APIs. Credits meter everything, so heavy crawls need budgeting, but entry pricing is hobbyist-friendly.
Jina Reader: the quick and simple option
Prefix any URL with r.jina.ai/ and you get Markdown back. It is the lowest-friction reader API around and the free tier is generous. The trade-offs show up at production scale: token-based billing gets fuzzy on big pages, and hard anti-bot sites are not its focus.
Zyte and Apify: for scraping-shaped workloads
If your workload looks like traditional scraping, thousands of product pages from specific sites on a schedule, Zyte's per-site dynamic pricing and automatic extraction are strong, and Apify's marketplace of prebuilt actors can save you writing any code at all. Both are covered in more depth in our web scraping tools roundup. They are heavier to operate than a fetch API, and for feeding an LLM they hand you more machinery than you need.
Bright Data: when you need the firehose
Bright Data sells proxy networks and pre-collected datasets. Their company datasets are the nearest substitute for Knowledge Graph queries if you need bulk firmographic records rather than live page content. It is infrastructure pricing with infrastructure complexity, so I would only go here for genuinely large volumes.
The Pricing Math That Decides It
Run the numbers on a concrete workload: extracting 20,000 articles a month for a content intelligence pipeline.
On Diffbot's entry plan you pay $299 for a monthly credit pool, and every Extract call draws it down. Under that pool this workload fits, but you are paying roughly 1.5 cents per article whether or not you use the knowledge graph features that justify the price.
The pay-per-fetch route: 20,000 fetches through link.sc plus 20,000 cheap LLM calls for field extraction. Even paying separately for both, you land at a small fraction of $299, and the LLM step is fully under your control. You choose the schema, you change it tomorrow, and you are not locked into one vendor's idea of what an "article" object contains.
The objection worth taking seriously: Diffbot's extraction is deterministic and battle-tested, while LLM extraction can hallucinate a field. Fair. Mitigate it with schema validation and spot checks, which you should be doing with Diffbot output anyway. In my experience the failure mode you will actually hit more often is fetch failures on protected sites, which is an argument for whichever provider fetches most reliably, not for knowledge-graph pricing.
How to Choose
- You query entities, not pages: stay on Diffbot, or price out Bright Data datasets. The knowledge graph is the moat and nothing on this list truly replaces it.
- You feed pages to LLMs, agents, or RAG: use a fetch-first API like link.sc and let your model do the structuring. This is the majority case, and the savings are dramatic.
- You crawl whole sites: Firecrawl, or link.sc fetch plus your own URL discovery.
- You scrape specific sites at high volume: Zyte or Apify.
The pattern behind all of it: Diffbot charges knowledge-graph prices for everything, including the parts that no longer require a knowledge graph. Pay for the moat only if you use the moat.
Turn any URL into clean, LLM-ready Markdown: create a free link.sc account and get 500 requests a month to test with.