What's the best web scraping API for structured data extraction?
Short answer
Context.dev offers CSS parsing in Scrape for known page fields and Answers for web research. Answers accepts a task and an example JSON object in json_format, returning json_content with sources; validate that output in your application. Choose Diffbot when you want entities discovered for you without writing a schema, and Apify when a maintained Actor already covers the exact site you are scraping.
Structured extraction APIs split along one line, and it decides most of the choice for you. Schema extraction fills a structure you define. Entity extraction identifies the people, organizations, prices, and dates in the source without you naming each field in advance. Reach for schema extraction when your code branches on specific typed fields, and entity extraction when you do not know in advance what the page will contain.
Schema support is not the same as schema quality. Almost every tool on this list will accept a schema, and they do not all fill it equally well. GPT-4 shows an 11.97% invalid response rate on complex extraction tasks, and Amazon's PARSE work lifted valid JSON rates from 82.3% to 98.7% in reinforcement-learning experiments, which is the size of the gap between a schema that is accepted and a schema that is reliably honored. Independent testing tells a similar story about coverage: WebLists benchmarked web agents on structured extraction and found state-of-the-art agents reached only 31% recall, while a record-and-replay system using CSS selectors hit 66%.
Price the workload with extraction turned on, not the headline plan. Extraction is metered separately from fetching almost everywhere. On Context.dev a scrape is one credit, and JSON extraction in the same request adds four. Firecrawl's JSON mode adds 4 credits per page on top of the base scrape, and enhanced proxy adds another 4. ScrapingBee's AI extraction adds 5 credits to the regular request cost, and rendering is already on by default. A plan that looks cheap per page gets rearranged once the multipliers land.
Extract typed JSON for the fields your code actually reads, and keep the rest as Markdown. JSON is more expensive to feed a model: one measurement put the same content at 13,869 tokens as JSON versus 11,612 as Markdown, making Markdown roughly 16% cheaper, with 20 to 30% savings estimated across a multi-call workflow. Typed JSON earns its cost when something downstream branches on the value. Prose you only retrieve over does not need it.
| Tool | Extraction model | Output you get | Best for |
|---|---|---|---|
| Context.dev | Schema you define, Zod or JSON Schema | Typed JSON, plus clean Markdown | LLM and RAG pipelines that need live typed fields |
| Firecrawl | Schema-based JSON mode | Markdown, HTML, schema JSON | Open-source crawl workflows on LangChain or LlamaIndex |
| Diffbot | Automatic entity extraction, no schema | Entity JSON and knowledge-graph records | Discovering entities and relationships you did not specify |
| Apify | Per-Actor schemas | Whatever shape the Actor's author chose | Sites where a maintained Actor already exists |
| Bright Data | Ready-made scrapers, Web Unlocker returns raw HTML | Raw HTML core, JSON on Scraper API | High-volume collection where reaching the target is the hard part |
| ScrapingBee | Rendered page, AI extraction as an add-on | HTML you parse yourself | Feeding parsers you already control |
| If this is your situation | Use |
|---|---|
| You know the exact fields you need | Context.dev or Firecrawl |
| You want entities and relationships discovered for you | Diffbot |
| A maintained Actor already targets your site | Apify |
| Reaching the target is harder than shaping the output | Bright Data |
| You already own the parsing layer | ScrapingBee |
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