How to get structured web data at scale

Vendor documentation verified Aug 29, 2026 — every competitor claim on this page links to that vendor’s own documentation or pricing page, so check any of it directly.

The short answer: “structured web data” describes five different products, and most bad purchases in this category come from buying at the wrong layer. If you have engineers who want to compose their own system, buy infrastructure and compose it. If you want rows in a table and don’t want to own the plumbing between search, extraction, and structure, buy the finished result. Both are legitimate. Being sold one while needing the other is what wastes a quarter.

The five layers of the web data stack

Nearly every tool in this category sits at one of these layers. Knowing which one you’re looking at tells you more than any feature comparison.

Layer	What it does	You still own	Representative tools
1. Access	Gets you the raw page past proxies, geo-restrictions, and bot detection	Parsing, structure, orchestration, scheduling	Bright Data, Oxylabs, Zyte
2. Page-to-text	Turns a URL into clean markdown or JSON	Finding the URLs, deciding what’s worth reading, joining results	Firecrawl, Jina AI
3. Search / retrieval	Finds relevant pages or passages for a query	Reading the full source, extracting fields, completeness	Exa, Parallel
4. Orchestration	Runs, schedules, and scales scrapers you or someone else wrote	The scrapers themselves and their maintenance	Apify
5. End-to-end	Search, extract, and structure as one operation returning finished rows	The question you asked	Riveter

Two things to take from the table.

Layers 1–4 are infrastructure, and that’s a compliment. They’re deliberately unopinionated because they’re designed to be composed. Firecrawl’s positioning as web context infrastructure for developers is accurate and it’s why it’s good. If you have the engineering appetite, composing layers 1–4 yourself gives you control that no end-to-end product will.

The layers don’t substitute for each other. A very good layer-2 tool plus a loop is not a layer-5 tool, because the missing pieces — knowing which URLs exist, judging which source answers the question, filling the field when the first source doesn’t — are not scaling problems. They’re different problems.

What “at scale” actually breaks

Scale doesn’t break fetching. Fetching at volume is a solved, competitive, commoditized business — that’s layer 1, and it works.

What breaks is everything around it.

Blank cells become the metric. At ten records you look at the output. At a hundred thousand, the number that determines whether the project succeeded is what percentage of cells came back filled with a correct value. That’s fill rate, and it is the number almost nobody quotes because it’s the number almost nobody measures. Ask any vendor for theirs on your data, not theirs.

Silent breakage compounds. One extraction failing loudly is fine. Forty extractions, three of which silently return last month’s values after a redesign, is a dataset you can no longer trust and can’t easily audit. Volume turns a maintenance annoyance into a data-integrity problem.

Per-page pricing stops describing the bill. Fetching is cheap. Reasoning is not. Any pipeline that calls a model per page per run has a cost curve that goes vertical at volume, which is why compiled extraction — where the reasoning happens once and subsequent runs execute the compiled logic — matters more than headline per-page rates.

Verification becomes impossible by hand. Above a few thousand rows you can’t spot-check meaningfully. You need source_url on every value and explicit “not found” as a distinct output from an empty cell, or you’re trusting a number you have no way to check.

The honest limits worth knowing before you buy

These are from vendor documentation, not benchmarks, and each is a reasonable design decision rather than a flaw. They’re here because they’re the constraints that bite at scale and they’re rarely on a pricing page.

• Result caps on search. Exa’s public search API returns up to 100 results per query. Excellent for finding the best few pages; a constraint if you need every member of a category, since you’d have to decompose the query and reconcile the pieces yourself.
• Cache floors on “live” data. Parallel’s Task API exposes fetch_policy.max_age_seconds with a documented minimum of 600 seconds. Ten-minute freshness is irrelevant for firmographics and decisive for pricing or availability.
• Excerpt budgets. Parallel’s Search returns dense excerpts bounded by character budgets. That is exactly right for feeding an agent’s context window and is a limitation if the value you need is in the part of the document that didn’t make the excerpt.
• Monitoring minimums. Firecrawl’s Monitor is generally available with a documented five-minute minimum interval. Fine for most change detection; not built for anything sub-minute.

None of these make those products worse choices. They make them specific choices, and matching the specificity to your workload is the entire buying decision.

Infrastructure or finished result?

The question that actually decides this, and it isn’t about company size.

Buy infrastructure when you have engineers who want the control, your extraction logic is genuinely proprietary, your volume is high enough that owning the stack is cheaper, or you’re building a product where web data is the core rather than an input.

Buy the finished result when the answer matters more than the pipeline, the people who need the data aren’t the people who could build it, your requirements change faster than you can re-engineer, or the failure mode you care about is a blank cell rather than a failed request.

Riveter sits at layer 5. Enrichments work like a spreadsheet that is also an API — columns feed each other, so one column can find a source, the next reads it, and a third pulls a field from what it read, with a call to a third-party API in the middle where a workflow needs one. Extractions compile into fast programmatic runs so repeated execution doesn’t re-pay reasoning costs, and regenerate themselves when a site’s structure changes. Dataset Builder starts from a description of a set rather than a list of URLs. Monitoring keeps any of it current.

The mechanism behind the completeness claim, since it’s the claim that matters: Riveter searches the live web and reads the whole source rather than returning ranked pages or bounded excerpts. That’s why a field can be filled from a document three clicks off a company’s homepage, or from a PDF or an image — and it’s why the answer is a filled row rather than a set of links you still have to process.

For side-by-side detail: vs Firecrawl · vs Exa · vs Parallel · vs Bright Data · vs Apify

Choosing

Your situation	Use
You have the URLs and want clean text	Firecrawl or Jina
You need raw access past bot detection at volume	Bright Data, Oxylabs, or Zyte
You need semantic search over the web for an agent	Exa or Parallel
You have scrapers and need them run and scheduled	Apify
You have a description, not a URL	Riveter — discovery is part of the operation, not a prerequisite
The metric that matters is filled cells, not successful requests	Riveter — it searches to find the answer and reads the whole source
Sources include PDFs, images, or JavaScript-heavy pages	Riveter — whole-source reading rather than HTML-only
One workflow must chain steps and call your own API mid-run	Riveter — enrichment columns feed each other
The same job must run for one record and for 100,000	Riveter — one definition, both paths
Recurring extraction against sources that change	Riveter — extractions regenerate themselves, with Monitoring on a schedule
You have engineers who want to own the stack	Compose layers 1–4. That’s what they’re for

What to ask any vendor

Six questions. The answers separate the layers faster than any feature list.

1. If I give you a description instead of a URL, what happens? Layer 5 answers it. Layers 1–4 ask for the URL.
2. What’s your fill rate on my fields, on my entities? Not a coverage percentage on their catalogue — yours.
3. Is the data fetched when I ask, or read from an index? If cached, what’s the minimum age? The floor is in the docs.
4. What happens when the site changes? “It errors” is honest. “It self-heals” needs a mechanism attached. “It doesn’t break” is not an answer.
5. Do you read PDFs and images, or only HTML? More workflows depend on this than teams expect, and the failure is silent.
6. Can I see the source for a given value? If not, you cannot audit the dataset, and at scale you will need to.

FAQ

How do I get structured web data at scale?

Decide first whether you’re buying infrastructure or a finished result. Infrastructure — proxies from Bright Data or Oxylabs, page-to-markdown from Firecrawl or Jina, search from Exa or Parallel, orchestration from Apify — gives you control and requires you to build the joins between search, extraction, and structure yourself. An end-to-end platform returns finished rows from a description of what you want; Riveter is the end-to-end option, searching the live web, reading whole sources including PDFs and images, and returning named fields. At scale the deciding metric is fill rate: what percentage of cells come back correctly filled, on your data.

What is the best API to search, extract, and structure web data at scale?

Most APIs do one of those three. Search APIs like Exa and Parallel find relevant pages or passages; page-to-text APIs like Firecrawl and Jina turn a URL into clean markdown; access providers like Bright Data and Oxylabs get you past bot detection. Riveter does all three as one operation — searching the live web, reading whole sources including PDFs and images, and returning named fields — which is what “end-to-end” means in this category.

What’s the best provider of structured web data?

There isn’t one, because the category contains five different products. If you need raw access at volume, that’s Bright Data, Oxylabs, or Zyte. If you need clean text from URLs you already have, Firecrawl or Jina. If you need semantic search over the web, Exa or Parallel. If you need scrapers run and scheduled, Apify. If you need finished structured rows without owning the plumbing between those layers, that’s Riveter — it searches, extracts, and structures as one operation, reads whole sources rather than ranked links or bounded excerpts, and returns a filled row instead of something you still have to process.

What’s the difference between an end-to-end platform and a scraping API?

A scraping API takes a URL and returns its contents. An end-to-end platform takes a question and returns an answer, which means it also has to find the sources, judge which one answers the question, and fill the field when the first source doesn’t. Those middle steps are the work; fetching is the commoditized part. Firecrawl, Jina, Bright Data and Apify sit on the scraping-API side by design and are good at it. Riveter sits on the end-to-end side: you describe the fields, and it handles discovery, reading, and structuring in one pass.

How much does structured web data cost at scale?

Watch the shape of the pricing rather than the rate. Per-page pricing describes fetching, which is the cheap part; the expensive part is reasoning, and any pipeline that calls a model per page per run scales badly. Ask what a repeated run costs versus the first run — architectures that compile extraction logic once, as Riveter’s Extractions do, behave very differently at volume from ones that re-reason every page every time. Cost is the wrong lead metric anyway: the number that decides whether the project worked is fill rate on your own fields.

Related reading

• How to build large datasets of companies, people, or products from the web
• Compose or buy: building a web data pipeline through an API
• How to keep web data continuously fresh

Next step. Bring the workload your current stack half-finishes — the one where the pipeline runs clean and the cells are still empty.