Web scraping remains useful in 2026, but operating it is getting more expensive and contested. In a December 2025 survey, many Apify and The Web Scraping Club respondents reported higher proxy and infrastructure costs; HUMAN Security’s platform observed a rising share of traffic attempting scraping attacks; and AI is entering some teams’ workflows without becoming universal. At the same time, public access to a page does not by itself establish that its contents are lawful or appropriate to reuse. The practical direction for teams is to build collection around clear permission, controlled costs, verifiable data quality, and governance—not simply to collect more.
What has changed compared with last year?
The clearest 2026 signals are pressure on collection infrastructure, more attention to scraping-related traffic by site defenders, and growing—but uneven—use of AI. These findings come from different kinds of evidence: a practitioner-community survey, a security vendor’s platform telemetry, and policy guidance. They describe those populations and observations, not a census of every scraper, website, or internet user.
Apify and The Web Scraping Club surveyed hundreds of people in their communities for their 2026 report, asking, among other things, “what has changed compared to last year?” The responses indicate what practitioners in those communities are experiencing. HUMAN Security reports activity observed by its own platform. OECD and European Data Protection Board (EDPB) materials address governance and legal considerations rather than measuring industry adoption.
What are the biggest web scraping challenges in 2026?
Proxy and infrastructure costs
In the Apify and The Web Scraping Club 2026 survey, 65.8% of respondents reported using more proxies, 58.3% reported increased proxy spending year over year, and more than 62% reported increased infrastructure spending. These are respondent-reported changes from a survey of practitioners recruited from the two communities, not estimates for all scraping teams. The report associates cost pressure in part with stronger anti-bot protections.
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The figures matter operationally because proxy spend is only one part of the bill. Teams also need to account for browser or rendering capacity, engineering time spent responding to site changes, monitoring, retries, data validation, and incident response. A low per-request price can still produce an expensive pipeline if it repeatedly fetches pages that cannot be used or requires constant repair.
Defenses, access, and the two-sided automation problem
HUMAN Security’s 2026 benchmark reports that the median global share of traffic attempting scraping attacks on its platform was 19.26% in 2025, compared with 10.03% in 2022. It also reports attempted attack volume almost 47% higher than in 2024 and 138% higher than in 2022. These are platform-observed attempted attacks, not a measure of all automation or all traffic on the internet.
The same benchmark reports a 43.38% median for EMEA in 2025, compared with 19.26% globally. HUMAN’s geographic analysis uses presented IP. Its report also says American threat actors accounted for almost two-thirds of attacks blocked in 2025; that statement concerns attributed attack origin, not the location of the targeted sites or a complete classification of all bots.
Those measurements describe defensive telemetry, not a verdict on every crawler. A crawler operating under a documented agreement is different from automation that extracts data at scale while evading controls or imposing costs. Security data alone cannot determine whether a particular collection project is lawful, ethical, or wanted by the site owner.
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Reliability and maintenance
Collection can fail even when a page appears publicly accessible: a page may depend on client-side rendering, change its structure, return a challenge, load incompletely, or expose information in a format that does not match the expected schema. In practice, the cost of a pipeline includes detecting these failures and preventing bad or partial results from silently entering downstream systems.
Zyte’s 2026 report landing page frames manual management of proxies, browsers, and access logic as increasingly unsustainable, and describes AI use in extraction, code generation, validation, and maintenance. That is a vendor’s account of current practice, not an independent industry benchmark. Treat it as one perspective on the operational problem, not proof that managed services or AI are the right answer for every team.
How is AI changing web scraping?
Adoption is mixed, and interest in trying tools is ahead of universal use. In the Apify and The Web Scraping Club 2026 survey, 45.8% of respondents said they used AI in scraping workflows and 54.2% said they did not. Meanwhile, 66.2% said they planned to try AI-assisted tools. Among respondents already using AI, 72.7% reported productivity advantages. Each figure describes survey respondents, not all scraping practitioners.
Reported reasons for not using AI included trust in outputs, cost, integration difficulty, unreliable performance on some sites, and uncertainty about practical benefits. Those concerns point to a useful boundary: AI can assist with parts of a workflow, but it does not make a collection pipeline self-validating.
Where AI can help—and where review remains necessary
Bounded uses include extracting fields from pages with variable layouts, generating or updating code, checking results against expected values, and helping maintain collection logic as pages change. These tasks can reduce repetitive work when a person or deterministic process verifies the result.
- Validate extracted values against schemas, allowed ranges, and known relationships before accepting them.
- Keep provenance: record where and when data was collected and which transformation produced the stored result.
- Use retries and failure handling for transient errors, but do not treat repeated failures as a reason to bypass access controls.
- Review changes to collection code and prompts, especially when outputs affect decisions or include personal data.
AI does not remove the need for selectors or other extraction rules, stable schemas, retries, provenance, or human oversight. A model that produces plausible-looking output can still misread a page or invent a value; validation should be designed into the pipeline rather than left to visual spot checks.
Does public access mean scraped data is free to reuse?
No. OECD’s 2025 analysis notes that online accessibility alone does not make data open for unrestricted reuse. A scraped dataset can also contain personal information about people who did not themselves post the material. Privacy, intellectual property, cybersecurity, and governance are all relevant considerations—not just whether a page can be fetched.
For EU-facing AI training, the EDPB adopted Guidelines 03/2026 on web scraping in generative AI on 8 July 2026. Its announcement says that processing personal data may require a lawful basis under GDPR Article 6 and, where special-category data is involved, an exception under Article 9(2). The EDPB page states that feedback is open through 30 October 2026. This is an active guidance and consultation development, not a complete legal analysis or a universal conclusion about every scraping activity. Requirements depend on the data, purpose, jurisdiction, site terms, access controls, and other facts; consult the regulator’s current text and qualified counsel for a specific project.
A governance check before collection
- Purpose: Document why the data is needed and whether a less intrusive source or method would meet that need.
- Permission and access: Review applicable agreements, site terms, access controls, and robots directions. Where authorization is needed, obtain and document it.
- Personal data: Identify whether records can relate to individuals, including people mentioned by someone else, and minimize collection to what the purpose requires.
- Reuse and retention: Review intellectual-property and privacy questions for the intended use; define access, retention, deletion, and onward-sharing controls.
- Security and provenance: Protect collected data, track source and collection time, and record transformations so decisions can be audited.
How should teams choose an approach?
There is no defensible universal ranking of scraping approaches in the available evidence. Choose based on the workload and its constraints, then include the full operating and governance cost in the decision. The framework below compares common routes rather than ranking providers.
| Approach | Often a fit when | Trade-offs to evaluate |
|---|---|---|
| Build and operate your own collector | You need control over a specific collection workflow, and have the engineering capacity to maintain it. | Budget for rendering, access management, retries, monitoring, page-change maintenance, validation, and governance. Make sure the access is authorized and the data can be used for the intended purpose. |
| Use a managed collection API or platform | You want to reduce the amount of browser, proxy, or collection infrastructure your team operates directly. | Compare task coverage, data quality, transparency into failures, control over collection behavior, total cost, and how the service handles access and governance. Vendor claims are not a substitute for workload-specific evaluation. |
| Obtain licensed or directly supplied data | A suitable dataset or authorized feed exists and meets your freshness and coverage needs. | Check license scope, permitted uses, coverage, provenance, update cadence, and the process for correcting or removing records. A supplied dataset still needs privacy, security, and quality review. |
Across all three, assess task fit (static HTML, rendered pages, structured API output, or ongoing monitoring), reliability and schema stability, permission, cost, operational burden, and governance. Include staff time and recovery work alongside infrastructure spend; do not compare only headline request rates or subscription prices.
What should a resilient collection workflow include?
1. Define acceptance criteria before fetching
Specify required fields, acceptable freshness, completeness, and what counts as a failed or incomplete result. Set a clear stop condition for access challenges or authorization concerns. This makes it possible to distinguish an expected empty result from a broken collector.
2. Separate fetching, extraction, and acceptance
Keep acquisition separate from parsing and validation. Record the response outcome and source context, parse into a versioned schema, then reject or quarantine records that fail checks. This helps identify whether a failure comes from access, page rendering, extraction logic, or changing source data.
3. Measure quality as well as volume
Track successful, partial, and failed jobs; field-level validation failures; freshness; duplicate rates; and the share of records that require repair. Monitor infrastructure and engineering cost per accepted record, not just pages requested. Set alerts around meaningful changes rather than relying on a rising fetch count as evidence of success.
4. Plan for change and recovery
Version selectors, schemas, code, and AI-assisted transformations. Test changes on representative pages before broad deployment, and preserve enough source context to investigate errors without retaining more personal data than necessary. Use bounded retries for transient problems and make backfills idempotent so a retry does not create duplicate records.
Troubleshooting common collection failures
- Pages load but expected fields are missing: Check whether content is rendered after the initial response, whether the page structure changed, and whether the parser is selecting the correct elements. Compare a small, authorized sample with the schema before running a larger job.
- Results become partial or stale: Inspect job timing, source changes, and validation metrics. A successful HTTP response does not prove that all expected content was rendered or extracted.
- Challenges or blocks increase: Pause and review authorization, terms, rate behavior, and access controls. Do not treat a challenge as a technical obstacle to evade; obtain permission or use an authorized source.
- Infrastructure cost rises faster than accepted output: Break cost down by retries, rendering, proxy use, and records rejected in validation. Reduce unnecessary fetches, improve acceptance checks, or reconsider whether a supplied dataset or managed route better fits the need.
- AI extraction looks plausible but is inconsistent: Add field-level validation and examples of valid and invalid outputs; send uncertain cases to review. Keep the original source context needed for verification, subject to minimization and retention limits.
Capture rendered pages without building a browser service
For workflows that need a visual record of a page rather than structured extraction, a screenshot API can handle the capture step. ScreenshotNeo is a website screenshot API and MCP server for developers. It can return PNG, JPEG, WebP, or PDF output from a GET request; it is a capture component, not a complete data-scraping or governance solution.
For example, this Python request saves a WebP capture of a page you are authorized to access. See the ScreenshotNeo documentation for request details and options.
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What comes next?
The evidence points to continued effort on efficiency, but not to a single winning technology. Teams will keep weighing the cost of operating collection against managed services or supplied data, while site operators continue responding to automation they consider harmful. AI may take on bounded extraction and maintenance tasks, but adoption figures and reported concerns suggest that review and integration remain part of the work. Governance is not a final checkbox: as policy develops, teams need to revisit purpose, data handling, and permitted reuse as projects and rules change.
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Does a higher scraping-attack share mean all automated traffic is malicious?
No. HUMAN Security’s benchmark measures attempted scraping attacks observed by its platform. It does not classify all crawlers or determine whether a particular project is authorized or harmful.
Are the survey figures representative of all scraping teams?
No. Apify and The Web Scraping Club surveyed people in their own communities. The results describe those respondents’ reported experiences and plans.
Does AI eliminate the need to validate scraped data?
No. AI-assisted extraction can still produce incorrect or inconsistent fields. Schema checks, provenance, and appropriate human review remain necessary.
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