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What a web scraping API does
A scraping API is a managed HTTP service between your application and the pages you want to collect data from. Your request typically specifies a URL and may also specify rendering, proxy, geography, session or extraction settings. The provider fetches the page, may execute JavaScript or handle session and ban-related behavior, and returns a representation such as HTML, Markdown or structured JSON.
This can reduce the infrastructure you operate yourself: browser workers, proxy pools, fetch retries and page parsers. It does not remove the need to decide whether collection is permitted, validate the output, or respond when a website changes.
The category covers different products and workflows. Some services mainly return rendered pages for your own parser; others provide extraction rules or supported-page schemas that return fields directly. Compare the specific endpoint and output mode you plan to use, not just the vendor name.
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Choose an extraction approach
Selectors and explicit extraction rules
Use selectors or provider-specific extraction rules when the page template is stable and you need predictable field-level behavior. You specify where fields come from, then validate that the returned values match your expected types and constraints. ScrapingBee documents JSON-formatted extraction rules intended to return structured data without requiring you to parse the returned HTML yourself.
This approach makes it easier to identify which field definition broke when a layout changes. Its trade-off is maintenance: a selector that matched yesterday may return nothing, the wrong element or multiple elements after a redesign.
Automatic extraction and supported-page schemas
Automatic extraction can be useful when a provider recognizes a page type and can map it to a designated schema. Zyte documents automatic extraction and schema configuration, including structured output for product and pricing data. Before building around it, confirm which page types and fields the endpoint supports and how it signals unsupported or incomplete results.
AI or natural-language extraction
Natural-language instructions can be quicker to write when layouts vary or field definitions are easier to describe than encode as selectors. ScrapingBee documents an AI scraper that accepts plain-English instructions or extraction rules and returns structured JSON. Its documented ai_query and ai_extract_rules requests add 5 credits to the regular request cost, according to ScrapingBee’s 2026 product documentation.
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Treat AI output as candidate data, not verified fact. Check types, required fields, allowed values and relationships, and compare results with a labeled sample. Route malformed or uncertain records for review rather than silently accepting them.
Which API should you evaluate?
No controlled cross-vendor benchmark establishes one API as universally most accurate or cheapest. The documented capabilities below are starting points for a target-specific evaluation, not a ranking.
| Service | Documented fit | What to verify in a pilot |
|---|---|---|
| ScrapingBee | Self-serve API with JavaScript rendering, rotating and premium proxies, geotargeting, screenshots, extraction rules, Google Search API and AI extraction. Its AI extraction requests add 5 credits to the regular request cost, as documented in 2026. | Whether the needed targets render and return the fields you require; actual credit use and cost per accepted record for your request settings. |
| Zyte API | Documentation describes a single-URL Web Data Extraction API, rendering, sessions, ban handling and structured schemas, including product and pricing data. | Whether your target page type and requested schema are supported, and how missing fields or unsuccessful fetches are represented. |
| Oxylabs Web Scraper API | Its enterprise guide documents JavaScript rendering, headless-browser support and custom XPath/CSS parsers. | Whether the rendering and parser controls match your workflow, plus pricing, limits and output behavior for your account and targets. |
| Apify | Its beginner guide presents a platform workflow for turning websites into processed structured datasets, including customizable actors and automation. | How much actor setup and maintenance your collection requires, and how its execution and output fit your integration. |
ScrapingBee’s public pricing page listed the following monthly plans in 2026. Prices and quotas can change; confirm the current terms and credit accounting on the provider’s pricing page before budgeting.
| ScrapingBee plan | Listed price | Listed credits |
|---|---|---|
| Hobby | $19/month | 75,000 |
| Freelance | $49/month | 250,000 |
| Startup | $99/month | 1,000,000 |
| Business | $249/month | 3,000,000 |
ScrapingBee also advertised 1,000 free API credits in 2026. Credits are not equivalent to accepted records: rendering, proxy selection and extraction options can affect request cost. Compare the cost of records that pass your own validation, rather than dividing plan price by the headline credit quota.
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How to run a useful pilot
- Choose representative targets. Include the page types, geographic variants and rendering behaviors you expect in production. A small collection of realistic targets reveals more than a synthetic benchmark.
- Define the output contract. Specify required fields, types, acceptable nulls, formats and any cross-field checks. Decide what makes a record usable before measuring a provider.
- Test one extraction mode at a time. Compare rendered HTML plus your parser, explicit extraction rules, and automatic or AI extraction only where relevant. Record the request settings so usage and results can be interpreted.
- Measure quality, speed and cost together. Track success rate, challenge rate, null-field rate, schema validity, duplicate rate, median and tail latency, and cost per accepted record. Review a labeled sample rather than relying only on a provider’s success status.
- Test failure handling. Include pages that are slow, incomplete or unavailable in your normal workflow. Confirm whether the API returns an error, partial output or a successful response with missing fields, and how your client should handle each case.
- Choose based on the results. Prefer the service and configuration that meet your quality and latency needs at an acceptable cost on your targets. Re-run the pilot if the target mix, extraction mode or provider settings change materially.
Build reliability into the extraction pipeline
Validate before accepting records
Parse the response against an explicit schema and reject or quarantine records that are malformed or missing required values. Add checks for plausible ranges and field relationships where they make sense for your data. For AI-assisted extraction, compare returned fields with a labeled sample and route low-confidence or malformed results for review.
Retry without multiplying work
Use bounded retries with backoff for transient failures rather than retrying indefinitely. Give each logical collection job an idempotent identifier so a repeated request does not create duplicate downstream records. Deduplicate using stable identifiers or a carefully chosen key, then monitor duplicate rates as a signal of changes in page content or your collection process.
Detect schema drift
Monitor null rates, schema validity and field distributions over time. A sudden rise in missing values may mean a selector stopped matching, an automatic parser no longer recognizes a page, or the website changed its content. Keep enough request and result metadata to diagnose those shifts. Store raw HTML or screenshots only when the target site’s terms and applicable law permit it, and set an appropriate retention period.
Measure production behavior
Track latency percentiles as well as averages: a tolerable median can hide a long tail that causes queues to build. Monitor challenge and failure rates alongside cost per accepted record. If your provider supports batch or webhook workflows, assess whether asynchronous processing suits your volume and latency requirements; availability and limits depend on the specific service and endpoint.
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- Output and control: Can the service return the format you need? Can you control fields and types, and distinguish missing, invalid and unsupported values?
- Rendering: Does your target require JavaScript execution or browser-like behavior? Verify this on actual pages rather than assuming that “rendering” guarantees complete content.
- Access behavior: Check proxy rotation, geotargeting, sessions and ban handling only to the extent needed for permitted access. Confirm how settings affect cost and response behavior.
- Operational fit: Evaluate concurrency, retries, latency, batch or webhook support, logging and integration requirements for the exact endpoint and plan.
- Data handling: Review retention, support and data-protection controls against your own obligations, especially if collected pages may contain personal data.
- Economics: Compare measured cost per accepted record under your actual request settings, not raw credits or calls alone.
Legal, privacy and robots.txt considerations
Robots.txt is a crawler-access convention, not an authorization system. IETF RFC 9309 says that “These rules are not a form of access authorization.” The standard specifies that robots rules are made available at /robots.txt and that a crawler that successfully downloads the file must follow parseable rules. Respecting robots.txt does not, by itself, establish that collection is allowed.
Review the site’s terms and API permissions separately. Do not bypass authentication or technical controls. Minimize personal-data collection, document the purpose and retention of collected information, and establish a lawful basis where required. CNIL says online data collection by scraping should be accompanied by measures safeguarding data-subject rights. The EDPB’s 2026 guidance materials address legal basis and special-category data in generative-AI scraping contexts; applicable obligations depend on the data, purpose, jurisdiction and processing involved.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.When a screenshot API is the better fit
If you need a screenshot for visual review, evidence or a vision-based workflow, use a screenshot API rather than treating a picture as a substitute for structured fields. ScreenshotNeo is the alternative to try first for that visual task: it removes known cookie and consent banners, newsletter popups and chat widgets before capture, and only clean shots are billed. It is a screenshot API and MCP server, not a structured-data extraction API; use a scraping service when your application needs validated JSON records.
Or skip the browser setup
A single GET request can return a screenshot or PDF without you setting up a browser worker. For example, this cURL request saves a screenshot of Stripe:
curl -G "https://api.screenshotneo.com/v1/shot" -d access_key=YOUR_API_KEY --data-urlencode url=https://stripe.com -o shot.webpSee the ScreenshotNeo API documentation for options and response details. The same request can be made in Python:
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import requests
r = requests.get("https://api.screenshotneo.com/v1/shot", params={"access_key": "YOUR_API_KEY", "url": "https://stripe.com"}, timeout=90)
open("shot.webp", "wb").write(r.content)Or in Node.js:
const q = new URLSearchParams({ access_key: 'YOUR_API_KEY', url: 'https://stripe.com' });
const res = await fetch(`https://api.screenshotneo.com/v1/shot?${q}`);- Cookie banners, popups and chat widgets are removed before the shot; each of those steps can be turned off.
- Bot checks, blank pages, timeouts, failed loads and cache hits cost nothing; response headers identify the page verdict and whether the request was billed.
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take_screenshot,get_page_infoandcapture_pdftools for AI agents and MCP clients. - The Free plan includes 1,000 screenshots a month with no card; paid plans start at $5 for 3,000 shots.
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Troubleshooting common scraping failures
| Symptom | Likely cause | What to do |
|---|---|---|
| Fields are empty or missing | The page template changed, content is loaded differently, or the extraction mode does not support the page. | Inspect a permitted raw response, verify the target’s rendered state, then update and test the selector or confirm schema support. Add a null-rate alert. |
| Returned values do not match the page | A selector matches multiple elements or a page’s layout differs from the assumed template; AI output may also be misinterpreted. | Check a labeled sample, tighten the field rules, validate types and relationships, and quarantine uncertain results. |
| Requests fail or return challenge pages | The target may be applying anti-bot or access controls, or the chosen request configuration may not fit the site. | Check provider response details and permitted access options. Do not attempt to bypass authentication or technical controls; consult the site’s terms or use an authorized API. |
| Latency spikes or jobs accumulate | Rendering, target response times, retries or concurrency may be affecting queue time. | Measure median and tail latency, bound retries with backoff, and review concurrency and asynchronous processing options for your endpoint. |
| Usage exceeds the estimate | Actual credit consumption may differ by rendering, proxy or extraction settings; retries can add requests. | Record usage by request mode, calculate cost per accepted record, and rerun the estimate using production-like targets and settings. |
| Duplicate records appear | Retries, repeated collection jobs or unstable page identifiers may be creating multiple copies. | Use idempotent job IDs and an appropriate deduplication key, then monitor duplicate rate. |
Frequently asked questions
Can a scraping API guarantee that every returned field is correct?
No. A successful fetch or structured response does not prove that values are complete or accurate. Establish correctness through schema checks and sampled comparison with the target content.
Should I save raw pages as well as extracted records?
Only when your terms and legal basis permit it and you have a clear operational reason. Limit retention and collected content to what you need.
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No. Structured extraction returns fields intended for downstream data use; a screenshot is a visual image of a page. Choose the output that matches the task.
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