TL;DR
- Context.dev gives you the fastest path from URL to LLM-ready JSON, with scraping, crawling, and structured delivery behind one API and no proxy infrastructure to maintain.
- Firecrawl has the cleanest developer experience for prototypes, but its dual credit-and-token pricing breaks down at production scale.
- Bright Data runs enterprise proxy and SERP pipelines well, though its Web Unlocker hands you raw HTML and a conversion layer to build yourself.
- Apify wins when a ready-made Actor already covers your target site, and loses when you need custom schemas across arbitrary URLs.
- Diffbot extracts entities automatically without a schema; ScrapingBee handles rendering and anti-bot bypass for parsers you write. The table and technical section below show which fits your pipeline.
Feature Comparison: Structured Data Extraction APIs
| Tool | Output Format | Schema Extraction | JS Rendering | LLM-Ready Output | Pricing Model | API Simplicity |
|---|---|---|---|---|---|---|
| Context.dev | Clean JSON, brand data | Yes, real-time | Yes | Yes, native | Single API | One call: scrape, crawl, structure |
| Firecrawl | Markdown, JSON, HTML, screenshots | Yes, via schema | Yes, zero config | Yes, explicit | Credits plus separate AI token subscription | Single-call, SDKs poll async |
| Bright Data | Raw HTML (Unlocker), JSON (Scraper API) | Yes, 600+ ready scrapers | Yes, server-side | Partial, you build conversion | Bandwidth tiers plus platform fees; pay-per-result on Scraper API | Split product line, more setup |
| Apify | JSON, CSV, HTML, Excel | Per-Actor schemas | Yes | Yes, when Actor exists | Credit-based per Actor | CLI and MCP, 20,000+ Actors |
| ScrapingBee | HTML, you parse | No | Yes | No, you structure downstream | Per-call credits | Single endpoint, render-focused |
| Diffbot | Structured JSON, knowledge graph | Automatic entity extraction | Yes | Yes, entity-based | Per-call plus credits | Automatic, no schema required |
The field splits along one line. Some tools hand you model-ready JSON on the first call, and others hand you raw HTML or a proxy connection and leave the structuring work to you.
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 JSON schema extraction, where you hand the tool a target structure and it returns data that conforms to it. OpenAI, Anthropic, Gemini, and Mistral all offer native structured output that constrains a model to a JSON schema, so the question for an extraction API is whether it enforces your schema at the token level or merely asks the model to follow it and hopes for the best. Simon Willison calls structured extraction from unstructured text the single most commercially valuable application of LLMs, which is why every serious tool now claims to support it.
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. WebLists benchmarked web agents on structured extraction tasks and found state-of-the-art agents reached only 31% recall, while a record-and-replay system using CSS selectors hit 66%. General LLM agents cap output at a few thousand tokens, which makes large-scale extraction infeasible without a programmatic loop.
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. A community benchmark on the OpenAI Developer Forum converted the same data to four formats and counted tokens with tiktoken. JSON came in at 13,869 tokens versus 11,612 for Markdown, making Markdown roughly 16% cheaper to feed a model. In a multi-call workflow the author estimated overall savings of 20 to 30%. When you process millions of pages, that gap is the difference between a viable pipeline and one that prices itself out.
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. GPT-4 shows an 11.97% invalid response rate on complex extraction tasks. PARSE closes much of that gap by refining schemas for model consumption and adding reflection-based validation, lifting valid JSON rates from 82.3% to 98.7% in reinforcement-learning experiments. When you evaluate a tool, test it against your hardest schema, not your simplest, because the simple one tells you nothing about where it will break.
Context.dev
Context.dev turns a URL into LLM-ready structured JSON through a single API, with no proxy infrastructure, crawler, or parsing layer for you to build or maintain. You send a request, and you get back clean JSON that drops straight into a prompt or a vector store. Scraping, crawling, and structured delivery sit behind one endpoint, so you don't stitch together a proxy provider, a rendering service, and a markdown converter the way a Bright Data setup demands.
The structured output goes beyond page text. Context.dev extracts brand and company data such as logos, brand colors, and company descriptions as typed fields, which matters when an AI agent needs to identify or enrich the entity behind a page rather than summarize its content. For an agent building a company profile or scoring a lead, that structured brand data arrives ready to use instead of buried in raw HTML you have to mine yourself.
MCP integration connects Context.dev directly to your agent runtime. An MCP-compatible client can call extraction as a tool without you writing glue code for authentication, polling, or response parsing. The agent describes what it needs, Context.dev returns structured JSON, and the result flows into the next step of your pipeline.
Cost stays predictable because you pay for real-time structured extraction per request, not per gigabyte of proxy bandwidth and not split across separate scrape credits and a token subscription. Firecrawl charges five credits per agent action and seven credits per page for a combined crawl-and-extract job, which makes a 500-page run hard to forecast. Context.dev's per-extraction model keeps the unit of billing the same as the unit of work you actually care about.
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 gives you the cleanest developer experience of any tool here for small-to-medium workloads, and its cost model breaks down the moment you scale schema extraction. 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. A reported P95 latency of 3.4 seconds and a single-call SDK pattern make it genuinely fast to ship a prototype.
The pricing structure is where production teams hit a wall. Firecrawl runs two meters at once. Credit-based scraping plans sit separate from a token-based AI extraction subscription that starts at $89 per month for 18 million tokens annually. A basic page scrape costs one credit, but the /agent endpoint charges five credits per action, and a combined crawl-and-extract workflow runs seven credits per page.
That math turns ugly fast at volume. A single 500-page extraction job can exceed an entire Hobby plan in one run, and standard plans offer no credit rollover and no pay-as-you-go fallback. Lower tiers also cap crawls at 50 pages, so you cannot stretch a small plan across a large site. When you need schema-defined data across thousands of URLs, the per-action billing makes your monthly spend hard to predict and harder to justify.
Firecrawl also leaves real gaps in production tooling. There is no native form filling, authentication, or CAPTCHA solving, and the open-source AGPL build is not production-ready. You also get no SLAs or managed delivery, so Firecrawl handles extraction but not the pipeline around it.
Best for: prototyping and early-stage LLM apps where you want to validate an idea quickly without building scraping infrastructure. Once your schema extraction reaches production scale across thousands of pages, the credit math pushes you toward a tool with predictable per-result pricing.
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 your target is an arbitrary URL with no matching Actor, you fall back to a generic crawler and lose the structured-output advantage that makes the platform attractive in the first place.
Apify is the wrong tool when you need custom schema extraction across URLs nobody has built an Actor for. In that case a schema-driven API that works on any page fits better, since you define the output shape once rather than searching the marketplace for coverage.
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 returns the rendered page, not structured data. You get HTML, and the work of turning that HTML into clean JSON, removing navigation and boilerplate, and mapping fields to a schema lands entirely on you. For an LLM pipeline, that means writing and maintaining a parser per target site, then keeping each parser 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. You write no schema and no selectors.
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 arbitrary web content where you cannot predict the structure, automatic extraction is the only approach that scales without constant schema maintenance.
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 schema quality. GPT-4 returns invalid responses on 11.97% of complex extraction tasks when schemas are ambiguous, so factor in validation and retry behavior before you commit at scale.
What is your expected call volume?
Firecrawl is the cleanest choice for prototypes and small workloads, but its credit math turns punishing at scale. A combined crawl-and-extract job costs 7 credits per page, and a 500-page site can exhaust a Hobby plan in one run. For steady production volume without infrastructure to manage, Context.dev's single API is the lower-overhead path. For bandwidth-heavy proxy pipelines across many countries, Bright Data earns its complexity.
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 delivers clean JSON, including brand fields like logos, colors, and company descriptions, from a single endpoint. You skip writing parsers and feed model-ready output straight into your pipeline.
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 practical benefit is predictable output your downstream code can trust without defensive parsing.
Is Markdown or JSON better for LLM input?
It depends on whether you need structure or raw text. A community benchmark found Markdown roughly 16% more token-efficient than JSON, so Markdown wins for retrieval and summarization. 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. The same benchmark author estimated that converting JSON to Markdown could save 20-30% across multi-call workflows. Cleaner output from Context.dev means each retrieved chunk carries content rather than markup, so you fit more signal in the context window.
Why does schema quality matter, not just schema support?
A vague schema produces invalid output even when the API technically supports schemas. Amazon's PARSE research measured an 11.97% invalid response rate from GPT-4 on complex extraction. Clear entity descriptions and validation rules cut those errors, so write precise schemas rather than relying on the model to guess.