TL;DR
- Context.dev is a strong fit when you need JavaScript-rendered pages, schema-shaped JSON, and a path from URL discovery to batch extraction through one API.
- Firecrawl is a strong alternative for schema-based JSON extraction and synchronous or asynchronous batch scraping; compare credits for your actual workload.
- Bright Data is worth evaluating when proxy infrastructure or site-specific collection is central to your workflow; check the output and integration path of the particular product you choose.
- Apify is a strong choice when a marketplace Actor fits your target; custom Actors are also an option when you need your own extraction logic.
- Diffbot suits automatic extraction by page type, with bulk and crawl routes; ScrapingBee suits teams combining page retrieval with extraction rules or AI extraction.
Feature Comparison: Structured Data Extraction APIs
| Tool | Best buyer fit | JS rendering | Schema-shaped JSON | Bulk path |
|---|---|---|---|---|
| Context.dev | One API for rendered-page extraction and discovered URLs | Documented waits and partial-result checks | formats.json with jsonParams.schema | Map discovery → asynchronous Batches |
| Firecrawl | Developer-led scrape and extraction workflows | Supported | JSON format accepts a schema | Synchronous or asynchronous batch scrape |
| Bright Data | Teams evaluating proxy infrastructure or site-specific collection | Check the selected product | Check the selected product's output | Check the selected product's collection workflow |
| Apify | Ready-made or custom Actor workflows | Depends on the Actor | Actor dataset schemas; not necessarily ad hoc page extraction | Actor-specific workflow |
| ScrapingBee | Retrieval with extraction rules or AI extraction | Supported | Check the selected extraction option | Plan request orchestration for your workload |
| Diffbot | Automatic page-type extraction | Check the selected API | Automatic structured output; Custom API also available | Bulk API for known URLs; Crawl API for discovery |
Choose by the work you need the API to do: render the page, shape extracted fields, discover URLs, or process many URLs. A JSON response can match a schema while still containing missing or incorrect facts, so validate consequential fields in your application.
What Structured Data Extraction Actually Means for LLMs
An extraction API earns its place in an LLM pipeline based on four capabilities. The first is schema-shaped JSON: you specify fields and receive structured output for your application to inspect. Ask how the tool handles absent fields, incomplete pages, and validation; a matching JSON shape does not prove that every value appears in the source.
Entity extraction is the second capability, and it differs from schema extraction in an important way. 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. Both matter, but they fail differently at scale. For entity extraction, check whether the returned fields fit your application rather than assuming that an automatically identified entity matches the fields you would have specified yourself.
Table parsing is the third, and it is where most tools quietly break. A pricing table or a financial statement carries meaning in its row and column relationships, and a tool that flattens it into prose loses that structure. If your downstream task depends on tabular data, you need a tool that preserves cell boundaries rather than dumping text.
Output format is the fourth capability, and it controls your token bill more directly than developers expect. Choose Markdown when the next step needs readable page content, and JSON when code needs named fields. Measure token use on your own pages and model rather than treating one format as universally cheaper for a complete pipeline.
The failure mode that catches teams at scale is schema quality, not schema support. Most tools support schemas. Few produce reliable output against complex ones. Amazon's PARSE research traces the problem to a mismatch: JSON schemas were built as contracts between human developers and static systems, not as instructions for LLMs, so ambiguous descriptions and unclear entity boundaries trigger hallucinations. When you evaluate a tool, test it against difficult pages and schemas, including absent fields and incomplete renders. Validate consequential values against your own rules before using them downstream.
Context.dev
Context.dev is a managed option for teams that want page retrieval, rendering, and schema-shaped extraction without maintaining their own scraping infrastructure. The Scrape JSON documentation shows how to request structured data from a URL; Map and Batches cover discovery and larger jobs. Check the returned data before passing consequential values to an application or model.
If you need logos, colors, or company details, evaluate the separate Brand API. Those fields are not automatically included in JSON extracted from a page using a custom schema.
Context.dev also documents MCP installation for connecting an agent to its tools. An MCP connection does not, by itself, remove authentication requirements or asynchronous batch-result handling.
For a cost comparison, price the same rendered pages, JSON extraction, and batch volume on each service. Context.dev batches use per-page Scrape Anything credits with output-dependent modifiers; Firecrawl documents a base page credit plus additional credits for JSON extraction. Neither should be represented by a universal flat price per extraction.
Two public benchmarks cover the page retrieval underneath extraction. In WebCrawlerAPI's 806-URL test, Context.dev returned usable Markdown on 92.1% of pages and Firecrawl on 89.7%, and Context.dev's mainContentOnly option lifted its extraction F1 to 0.567. On Scrapingbench's 40 agent scrape tasks, Context.dev finished 38 and Firecrawl 32. On TRACE, the open-source daily benchmark that sends about 2,500 fresh pages to four scraping APIs with caching off, Context.dev returned useful content on 87.0% of 2,527 pages in the November 6, 2026 sample, first of four, with Firecrawl at 82.3% (our TRACE write-up). Every result is dated and linked to its source on our benchmarks page.
Can Context.dev extract data from JavaScript-rendered pages?
Yes. For content that appears after load, set sharedParams.waitFor to a CSS selector or a delay, as described in the rendering documentation. Inspect isPartial and output success before treating a response as complete; adjust the wait if the needed content has not appeared.
How do I request JSON that follows my schema?
Send a POST /web/scrape request with formats.json set to true and a top-level jsonParams.schema. The JSON extraction guide shows the request shape and the returned json.data. Make fields nullable or optional when the page may omit them: requiring a non-null value can invite a guess. Inspect output success and validate consequential values in your application; schema-shaped output is not a guarantee of factual correctness.
How do I extract structured data from many URLs?
Use Map to discover URLs when you do not already have a list, then submit a batch with formats.json and jsonParams for per-page structured extraction. Batches run asynchronously; poll for results or use a webhook, and retain each result's source URL when assembling your dataset. Batch submissions support up to 100,000 URLs.
Best for
AI agent developers who need clean, typed JSON from arbitrary URLs without managing a scraping stack, and teams replacing an internal crawler they no longer want to maintain. If your pipeline already breaks when a target site changes structure or your proxy rotation fails, swapping it for a single managed API removes the maintenance burden while keeping output model-ready. Context.dev fits the developer who values fast deployment over deep infrastructure control.
Firecrawl
Firecrawl is a developer-focused option for scraping and schema-based extraction; its documented JSON format accepts a schema. The GitHub README sells the pitch accurately. You pass a URL or a Pydantic schema to the /scrape endpoint and get back clean Markdown or typed JSON, with JS rendering, proxy rotation, and rate limits handled for you. Its scraping workflow can be useful for prototyping as well as production evaluation.
Firecrawl's billing documentation lists one base credit per page and four additional credits per page for JSON LLM extraction. Compare that documented meter against the pages and features your job actually uses.
Firecrawl documents synchronous and asynchronous batch scraping, including schema extraction per page. Its billing documentation also describes pay-as-you-go for paid self-serve plans. Estimate a large job using current plan limits and the relevant per-page charges.
Evaluate production requirements against the specific Firecrawl product and plan. Firecrawl describes form interaction, batch webhooks, and enterprise SLAs, so check which of them your plan includes.
For a direct comparison, test both services on your target pages and schema, including JavaScript-dependent content, absent fields, and batch results. Page-retrieval scores alone do not establish JSON extraction accuracy.
Best for: teams that want a developer-focused scrape API with documented schema JSON and synchronous or asynchronous batches. Compare production costs using a representative workload rather than assuming another vendor is cheaper per result.
Bright Data
Bright Data is built for data engineering teams running high-volume collection, and its scraping product line splits in a way that forces a choice before you write any code. The platform pairs residential proxy infrastructure across 100+ countries with extraction tools, and which tool you pick decides how much engineering you inherit.
The split shows up most clearly between two products. The Web Unlocker returns raw HTML, so you handle the conversion layer yourself. For an LLM or RAG pipeline, that means building HTML-to-markdown conversion, boilerplate removal, content quality scoring, retry logic, and proxy tier routing. None of it ships with the product. The Web Scraper API takes the opposite approach and returns schema-consistent JSON for supported sites, with JavaScript rendering handled server-side and pay-per-result pricing that charges only for successful responses. The same vendor sells both the do-it-yourself path and the managed one.
Bright Data backs the JSON path with 600+ ready-made scrapers for sites like Amazon, LinkedIn, and Zillow, plus a Scraper Studio that generates a working scraper from a plain description of the data you want. A self-healing mode updates scrapers automatically when target sites change structure. For the platforms Bright Data already covers, you get clean structured output without parsing HTML.
The cost model is where Bright Data fits some teams poorly. Pricing runs on bandwidth per proxy tier plus monthly platform fees, not per-page extraction. For AI workloads where the natural unit is extraction count rather than gigabytes, the per-GB proxy model produces billing you cannot easily predict from your call volume.
Bright Data also ships an MCP server that exposes its tools to AI agents and picks the most effective one per target site. It does not list native Python, TypeScript, or Go SDKs aimed at LLM-pipeline use, so the agent integration leans on the MCP layer rather than language-native tooling.
Best for: data engineering teams running high-volume, geo-targeted, or SERP pipelines that need residential IP rotation at scale and can absorb the proxy-based pricing model.
Apify
Apify makes the most sense when someone has already built and maintained an Actor for the exact site you want to scrape. The platform hosts more than 20,000 Actors, serverless programs that handle a specific target like Amazon, LinkedIn, or Google Maps. A Google Maps Actor returns business names, addresses, ratings, and review counts as clean JSON, and an Amazon Actor returns product titles, prices, and seller info without you writing any parsing logic. For well-trodden platforms, that pre-built coverage saves real engineering time.
Apify's Actor Schemas enforce defined input parameters and output formats per Actor, so the JSON an Actor returns stays consistent across runs. That predictability matters in an LLM pipeline, where a shifting output shape breaks downstream parsing. Apify also supports the Model Context Protocol, which lets an agent discover and invoke Actors on its own using standardized metadata about each Actor's inputs and outputs. Native integrations with LangGraph and CrewAI mean you can wire an Actor into an agent framework without building a custom adapter.
The marketplace model is also Apify's weak point. Because a global community builds the Actors, quality varies from one to the next, and an Actor that works well for Amazon may be flaky or stale for a less popular target. Apify partly addresses this with Score Actors that evaluate other Actors for reliability and agent-readiness, but you still inherit a dependency on whoever maintains the Actor you chose. When no ready-made Actor fits, you can develop a custom Actor, but you then own its extraction logic and maintenance.
Apify's Actor output schema describes where an Actor's results live, while its optional dataset schema describes dataset fields and validation metadata. Those per-Actor schemas differ from passing an ad hoc extraction schema with each page request; choose based on whether an existing or custom Actor fits your workflow.
Best for: teams scraping well-known platforms like Amazon, LinkedIn, and Google Maps who want pre-built, structured output and are willing to depend on community-maintained Actors.
ScrapingBee
ScrapingBee solves a narrow problem well. It renders JavaScript-heavy pages, rotates proxies, and bypasses anti-bot systems, then hands you the resulting HTML through a single API call. Pricing follows a per-call credit model that scales with the difficulty of the request, so a basic fetch costs less than a render that needs a headless browser and premium proxies. You always know what a request costs before you send it.
That predictability is the appeal, and it comes with a clear boundary. ScrapingBee can return a rendered page, and its extraction rules or AI extraction may reduce the parsing you need to write. Check whether the chosen option returns the fields and consistency your application requires. If it doesn't, an LLM pipeline needs a parser per target site, and each parser has to be kept alive as the site changes its markup.
ScrapingBee does offer AI-assisted extraction rules and CSS-selector extraction on top of the raw fetch, which covers simple field grabs. Neither replaces a real schema-extraction layer for arbitrary URLs at scale. Compared with Firecrawl's clean Markdown and JSON output or Bright Data's server-side Web Scraper API, ScrapingBee deliberately stops at the rendering boundary and leaves structuring to you.
Treat ScrapingBee as a reliable fetch-and-render engine that feeds parsers you already control, not as a structured data API. If your team has invested in custom extraction logic and just needs pages delivered through tough anti-bot defenses, it does that job cleanly and cheaply.
Best for: developers who need dependable JavaScript rendering and anti-bot bypass to power their own custom parsers, and who are comfortable owning every step of downstream structuring.
Diffbot
Diffbot takes the opposite approach to every other tool on this list. Instead of asking you to define a schema, it reads a page and returns entities it recognizes on its own. Its Knowledge Graph already maps billions of organizations, people, products, and articles, so when you point Diffbot at a URL, it classifies the page type and extracts the fields that page type implies. An article returns author, title, and publication date. A product page returns price, brand, and availability. Its automatic APIs do not require you to define the page-type fields upfront, though Diffbot also offers a Custom API.
That design wins when you are pulling structured data from pages you have never seen and cannot model in advance. A schema-defined API like Firecrawl or the Bright Data Web Scraper API needs you to know the shape of the data before the first call. Diffbot inverts that requirement, which makes it strong for crawling unfamiliar domains, building company datasets, or enriching records from messy unstructured content. The relationship layer goes further than field extraction. Diffbot can tell you that one company acquired another or that a person works at a given organization, which a flat schema cannot represent.
The trade-off is control. Because Diffbot decides what counts as an entity, you take what its models return rather than the exact fields you specified. For a known target like an Amazon product page, a ready-made scraper gives you tighter, more predictable output. For unfamiliar content, automatic extraction offers a starting point without defining every field in advance; Diffbot also documents a Bulk API for known URL lists and a Crawl API for domain discovery.
Best for: teams that need automatic entity and relationship extraction from unstructured web content, and want clean structured output without defining a schema upfront.
How to Choose the Right Extraction API for Your LLM Pipeline
Three questions decide which extraction API fits your pipeline. Answer them in order, and the field narrows fast.
What output format do you need?
If you want model-ready content with no parsing layer, choose Context.dev, Firecrawl, or Apify. All three return clean JSON or Markdown you can pass straight to an LLM. Bright Data's Web Unlocker hands you raw HTML, so you build the conversion, boilerplate removal, and content scoring yourself. Pick it only when you already run that layer. ScrapingBee fits when you have a custom parser and need reliable rendering, not finished structure.
How predictable is your schema?
When your schema is fixed and you target well-known platforms like Amazon or LinkedIn, a ready-made scraper wins. Apify's Actors and Bright Data's 600+ prebuilt scrapers return consistent JSON without you defining anything. When you extract schema-defined fields across arbitrary URLs, you want a single endpoint that accepts your schema. Context.dev and Firecrawl both do this. When your source is unstructured and you don't want to define a schema at all, Diffbot's automatic entity extraction is the better starting point.
Schema support is not the same as factual accuracy. Test missing fields and difficult pages, inspect incomplete results, and validate consequential values before committing to a workflow at scale.
What is your expected call volume?
For many URLs, compare discovery, submission, result delivery, and charges as one workflow. Context.dev documents Map followed by asynchronous Batches; Firecrawl documents synchronous and asynchronous batch scrape; Diffbot documents Bulk API for known URLs and Crawl API for discovery. Estimate costs from each vendor's current terms for your actual workload.
FAQ
What is a structured data extraction API?
A structured data extraction API takes a web page and returns machine-readable data in a defined format instead of raw HTML. Context.dev can return schema-shaped page JSON. Its separate Brand API covers brand-profile fields such as logos and colors; validate extracted values before using them downstream.
How does JSON schema extraction work with LLMs?
You define a schema describing the fields you want, and the model maps page content onto that structure. Context.dev and Firecrawl both accept a schema and return typed JSON that matches it. The benefit is a predictable field structure, but downstream code should still check missing fields and validate consequential values.
Is Markdown or JSON better for LLM input?
It depends on whether you need structure or raw text. Markdown is useful for readable retrieval content; measure token use on your own inputs rather than assuming it always costs less than JSON. JSON wins when your code needs named fields, since structure beats a token saving you would spend re-parsing anyway.
How does MCP fit into an extraction pipeline?
The Model Context Protocol lets an AI agent call an extraction tool directly without custom glue code. Context.dev exposes its extraction through MCP, so an agent can request structured data mid-task. Your agent fetches fresh page data on demand instead of relying on a pre-built dataset.
When should I use a ready-made scraper versus a custom schema?
Use a ready-made scraper when your target is a well-known platform, and define a custom schema when it isn't. Apify's Actors and Bright Data's 600+ scrapers cover sites like Amazon and LinkedIn with maintained output. For arbitrary URLs, a schema-driven API like Context.dev extracts the fields you specify without a pre-built scraper.
What does token efficiency mean for a RAG pipeline?
Token efficiency is how many tokens your retrieved content consumes per model call, which sets your cost and latency. Measure the tokens consumed by your actual retrieved pages and prompts. Use Markdown for readable source content and JSON where named fields avoid downstream parsing.
Why does schema quality matter, not just schema support?
A vague schema produces invalid output even when the API technically supports schemas. Write precise field descriptions, allow missing facts to remain missing, and validate important values against the source or your business rules. A valid JSON response can still contain an incorrect extraction.