DEV Community: Astro

Mobile proxy growth in 2026: What market forecasts imply for 4G and 5G demand

Astro — Enterprise Data Gathering Infrastructure — Fri, 23 Jan 2026 16:01:30 +0000

Mobile proxy growth in 2026: What market forecasts imply for 4G and 5G demand

When teams consider 4G or 5G mobile proxies, they are usually answering a budgeting question: how much capacity will the workflow actually consume, and what will drive that consumption over the next 12 months. The problem is that there is no shared metric across providers that would let you compare “active IPs”, “requests”, or “traffic” in a consistent way.

That leaves two usable inputs: your own workload profile and external market forecasts. This article shows how to translate forecast language into concrete assumptions you can test: where 4G and 5G are used in practice, which tasks expand first, and what indicators matter when you plan capacity.

Why usage is not a single number

Depending on the task, “usage” can mean request volume, transferred data, concurrent sessions, or completed jobs. Those metrics are vendor-specific and rarely comparable. A mobile proxy online comparison page usually shows tiers and pool claims, not a shared definition of usage.

Mobile proxy server market size forecast 2025 to 2030

Two published outlooks define a corridor:

Mordor Intelligence: about USD 0.75B in 2025 and USD 1.12B by 2030 (about 8.34% CAGR).
Knowledge Sourcing (KSI): about USD 687.443M in 2025 and USD 982.644M by 2030 (about 7.41% CAGR).

On endpoints alone, the implied expansion from 2025 to 2030 is roughly +43% to +49%. For teams building a 4G or 5G proxy workflow that supports treating mobile routing as a capacity line, you monitor over time.

CAGR context across related proxy markets

Alongside the mobile proxy outlooks above, KSI publishes a broader Proxy Servers market outlook at about 7.42% CAGR for 2025 to 2030, and Zion Market Research projects a proxy server market CAGR of about 7.50% over its stated period.

What growth looks like in real workflows

Most demand for mobile-origin traffic comes from measurement tasks where carrier infrastructure and session continuity affect what you observe:

Mobile-first QA for region-specific pages and in-app flows.
Pricing and availability checks across cities and countries.
Longer user journeys where session stability matters.

Teams that use private mobile proxies typically value stable session handling and consistent throughput during busy hours. Mobile 4G and 5G proxy setups are often chosen when measurements vary by geography or carrier profile, and you want results that behave like real mobile connections.

How to evaluate a proxy provider without guessing

If you are trying to decide what the best 4G and 5G mobile proxy is for your workload, use a benchmark you can rerun and compare.

A practical checklist:

Coverage depth: countries, cities, and carrier mix.
Session controls: rotation triggers and sticky session options.
Performance signals: success rate by task, time-to-first-byte, retry rate, and variance across time windows.
Operations: dashboard controls, API hooks, and exportable logs.
Data handling: retention and access controls stated in plain terms.

If a vendor offers a 4G or 5G proxy free trial, run the same scripted test twice: once during peak local hours and once off-peak. Do not rely on a second mobile proxy online page; require a benchmark run and compare outcomes against your task metrics. This is also where private mobile proxies can help, because you can isolate variables and see whether stability improves completion rates.

Buying and scaling questions

Define one unit that matches your workload: cost per successful job, cost per 1,000 completed actions, or cost per GB. Before you buy a mobile proxy, document your tasks, pass / fail thresholds, and the regions you must cover.

When you scale a 4G and 5G proxy workload across teams, enforce a shared test set so results remain comparable. If you need a 4G or 5G residential proxy for certain routes, validate it with the same scripts and compare variance as well as averages.

2026 watchpoints to include in planning

Mordor Intelligence highlights North America as the largest market and Asia Pacific as the fastest-growing region in its outlook. For teams expanding proxy mobile 4G and 5G coverage, thin supply can show up as higher variance and more retries.

If there is no 4G or 5G proxy free trial, ask a provider for a short evaluation window with capped spend and a clear exit path. Then, once you buy a mobile proxy at scale, rerun your benchmarks quarterly, so the same tasks stay repeatable.

A checklist you can rerun quarterly

Forecasts don’t tell you how your exact workflows will behave, but they help you pick the right level of rigor. Use the market corridor as a planning input, then let repeatable tests decide the configuration.

A simple way to operationalize this:

Define one unit metric before you commit (cost per completed workflow, cost per 1,000 actions, or cost per GB).
Run the same benchmark set twice (peak vs off-peak) and record variance, not just averages.
Treat “4G and 5G residential proxy” vs “best 4G or 5G mobile proxy” as a control question: session behavior, rotation options, and consistency under load.
Re-test on a schedule (for example, quarterly) so the setup stays aligned with changing routes, regions, and demand.

That’s the difference between buying access and building an instrumented routing layer: you end up with numbers you can defend, not just a plan you can hope will hold.

Proxy market forecast 2026: How regulation (GDPR, CCPA) is reshaping the landscape

Astro — Enterprise Data Gathering Infrastructure — Tue, 16 Dec 2025 09:00:00 +0000

As the demand for data-driven decisions explodes, the proxy market is growing at double-digit rates. Brands, security teams, and researchers all lean on large pools of residential and mobile IPs to see the real web. At the same time, high-profile breaches and enforcement actions are forcing companies to rethink how they use proxies and GDPR together, and to move away from shady IP sources toward transparent, compliant, truly ethical proxies.

By 2026, this tension between growth and regulation will decide who survives in the proxy industry. The key question is now: what is KYC for proxy providers in 2026? Buyers are already looking for KYC GDPR compliant proxies and for every serious ISO 27001 proxy provider before they dare to buy residential and mobile proxies on scale, hoping to stay ahead of both competitors and regulators. According to recent forecasts, the global proxy server service market is set to grow from around USD 2.51 billion in 2024 to more than USD 5 billion by 2033.

The proxy server service market is expected to more than double between 2024 and 2033, even as GDPR / CCPA enforcement keeps tightening around how those IPs can be used.

From raw IPs to regulated assets: Proxies and GDPR / CCPA basics

Under GDPR and CCPA, IP addresses, cookies and device fingerprints are treated as personal data rather than harmless technical metadata. For proxy vendors and their customers, proxies and GDPR are now inseparable: every request routed through a proxy, from log retention to profiling and geo-targeting, must be designed with data-protection rules in mind.

CCPA adds a US layer: California users can see what was collected about them, request deletion and opt out of “selling” their data, including information gathered via proxy-based tracking and scraping. Providers must distinguish California traffic, honour these rights and prove who is behind each stream of requests, which is why KYC GDPR compliant proxies are quickly becoming the default for sensitive use cases.

What has changed for proxy providers after GDPR / CCPA:

Logs and IP addresses are no longer “purely technical”; they are treated as personal data that must be minimised and protected.
Transparent Terms of Service and a detailed Privacy Policy are mandatory, not optional extras.
Data subject access and deletion requests can now include information obtained via proxies.
Vendor contracts and sub-processors must explicitly cover proxies and GDPR obligations and incident handling.

Ignoring proxies and GDPR / CCPA in 2026 means accepting higher chances of fines, lost partnerships and reputational damage. The market is therefore shifting toward providers that can prove ethical sourcing of IPs, enforceable KYC processes and audited security controls like ISO 27001 turning proxies from raw infrastructure into fully regulated assets.

KYC proxy provider and ethical proxies

Today, the core question for any serious buyer is what is KYC for proxy providers in practice? It means treating every new client like a regulated partner: verifying company details, checking documents, validating payment methods and screening use cases. Instead of letting anyone spin up traffic for any purpose, every serious KYC proxy provider builds policies to filter out fraud, credential stuffing, and carding.

This is where KYC GDPR compliant proxies become the new baseline. Logs are collected and stored according to GDPR principles: minimisation, limited retention and strict access control. In practice, KYC- and GDPR-compliant proxies let both the client and the KYC proxy provider demonstrate that they know who is behind the traffic, why it is being sent and how related data will be handled throughout its lifecycle.

Ethical proxies: Meaning in 2026

Residential and mobile IPs are sourced with explicit opt-in and fair compensation, not bundled from shady apps or malware. The provider enforces a list of forbidden scenarios, actively monitors traffic patterns and is transparent about partners across the whole supply chain. In other words, ethical proxies combine KYC, consented IP sourcing and continuous monitoring.

How to spot an ethical ISO 27001 proxy provider:

The website openly states that the company is an ISO 27001 proxy provider, with a certificate number or link to an audit summary you can verify.
There is a detailed description of the KYC flow: which data is collected, how it is stored, how long it is retained and under which conditions it is deleted.
Privacy and security sections explicitly cover proxies and GDPR: who is the data controller, how logs are handled and how data subject requests are processed.
The provider mentions regular external audits, penetration tests or bug bounty programmes, reinforcing the picture of an ISO 27001 proxy provider with an actively maintained ISMS.
Support can explain how they apply KYC checks in practice; if a supposed KYC proxy provider is vague about red flags or escalation paths, that’s a warning sign.

Taken together, these elements show how the market is shifting away from cheap, opaque IP pools toward KYC GDPR compliant proxies operated by verifiable, ISO 27001 proxy provider-level players.

How to buy residential and mobile proxies in 2026 without breaking GDPR?

In 2026, it’s no longer enough to simply buy residential and mobile proxies from the cheapest vendor and hope for the best. Each purchase decision now needs to factor in the provider’s jurisdiction, its logging and retention policy, the presence of KYC checks, whether it operates as an ISO 27001-level player. In other words, when compliance is on the line, due diligence matters just as much as IP pool size or pricing.

Quick checklist before you commit:

Confirm that it is a real ISO 27001 proxy provider with a certificate number or public reference to an accredited audit.
Read the Privacy Policy and DPA sections that cover proxies and GDPR and, ideally, CCPA, to see how they treat IPs, logs and data subject rights.
Make sure your intended use case sits on the “allowed” side of their AUP, and that they explicitly talk about running ethical proxies, not turning a blind eye to abuse.
Look at which data is collected and stored as part of KYC GDPR compliant proxies: is it minimised, encrypted and time-bound, or hoarded indefinitely?
Understand how the provider sources its residential and mobile IPs (opt-in, compensation, consent flows) before you buy residential and mobile proxies at scale.

Citations

[1] “Proxy infrastructure transparency checklist”, Astro (2025)
[2] “GDPR data subject rights: An in-depth guide with examples”, Celestine Bahr, Usercentrics (2025)
[3] “What Is the California Consumer Privacy Act (CCPA)?”, Palo Alto Networks (n.d.)
[4] “Data protection explained”, European Commission (n.d.)
[5] “Data protection and privacy laws now in effect in 144 countries”, Aly Apacible-Bernardo & Kayla Bushey, IAPP (2025)
[6] “Data protection in development: Where are we headed?”, Nay Constantine, World Bank (2025)
[7] “Countries with Data Privacy Laws – By Year 1973–2016 (Tables)”, Graham Greenleaf, Macquarie University / Privacy Laws & Business International Report (2017)
[8] “Proxy Server Service Market Size & Forecast [2033]”, Market Growth Reports (2025)

New AI web standards and scraping trends in 2026: rethinking robots.txt

Astro — Enterprise Data Gathering Infrastructure — Tue, 09 Dec 2025 12:38:39 +0000

For three decades, robots.txt has been the main mechanism websites use to signal how automated crawlers should behave. It was created in 1994 for a very different web made of lightweight HTML pages, predictable automation tools, and straightforward indexing needs.

Scraping trends in 2026 are changing rapidly. The AI systems don’t just fetch pages, they extract text, summarize content, crop images, and feed data into training pipelines. What's more, they do it automatically, without human interference as a part of the emerging agentic AI trend. The need for new standards for scraping data with AI is clear.

Why the use of robots.txt is no longer enough

As of now, robots.txt scraping policy has a few structural limitations that become more obvious in the context of modern AI:

It only controls access, not usage: The file can tell a bot whether it may fetch a URL, but it cannot distinguish between different purposes. A site owner cannot express something like “index this for search, but don’t use it for model training”.
It has no semantic layer: Robots.txt treats an entire URL the same way, even though a page may contain text, code snippets, images, or user-generated content that the owner would prefer to handle differently.
It relies on voluntary compliance: Traditional search engines generally respect robots.txt. Many newer AI scrapers do not. A recent Duke University study (2025) found that several categories of AI-related crawlers never request robots.txt at all.

Because of these issues site owners want clearer ways to express what is allowed and what is not. This shift has led researchers and developers to explore alternatives or supplements to robots.txt, including ai.txt and llms.txt.

ai.txt: a potential new standard for scraping data with AI

The ai.txt proposal (“A Domain-Specific Language for Guiding AI Interactions with the Internet”, 2025) introduced a domain-specific language for declaring what kinds of AI interactions are allowed on a site. The goal is not just to block or allow URLs, but to describe permitted actions in a more detailed way.

With ai.txt, a website could specify rules at different levels: for example, allowing summarization of an article while disallowing image extraction, or permitting use of one section for training but restricting another. The format also supports natural-language instructions aimed at compliant AI agents, which adds flexibility that robots.txt could never offer. In short, it's used to:

Specify what types of content can or cannot be used (e.g., text allowed, images forbidden).
Define what actions AI systems may perform, such as summarizing but not training.
Set rules for specific sections or elements of a page.
Provide usage terms or licensing notes directly in the file.

There are two proposed paths for enforcement:

Programmatic parsing, where AI agents read a structured representation of ai.txt (such as XML) and enforce rules automatically.
Prompt-based enforcement, where the ai.txt file is read as plain text and incorporated into the agent’s instructions.

llms.txt: a content guide rather than a permission file

While ai.txt focuses on restrictions, llms.txt focuses on clarity.

Originally proposed by Jeremy Howard, llms.txt is a simple Markdown file placed at a site’s root. Instead of forcing AI systems to scrape entire pages, llms.txt gives them a concise overview of the site’s key content.

A typical file might include a short summary of the project, followed by links to documentation, examples, reference pages, or other important sections. Because it uses Markdown, it is easy to read and easy for models to parse. In short, it can:

Give AI a clean summary of what the site is and what it contains.
Point to the most important pages (docs, guides, reference material).
Mark which URLs are the “canonical” sources to rely on.
Help models avoid scraping noisy or irrelevant pages by offering a curated list.

It is not a replacement for a robots.txt scraping policy, and it does not regulate access. It plays a different role: offering a clean entry point so that AI systems can rely on a curated outline rather than full scraping. This can reduce hallucinations and improve the accuracy of responses when an LLM references a site’s content.

Current status and future opportunities: why do we use robots.txt instead of ai.txt and llms.txt?

Various alternative and complementary tools for robots.txt are voluntary conventions, mostly adopted in tech / SEO communities. For example, a Yoast SEO plugin added a feature that auto-generates a llms.txt at a site’s root.

However, actual AI providers have not universally honored these files so far. SearchEngineLand reported (2025) that “there’s no clear evidence that AI companies follow llms.txt” rules, and notes that Google has explicitly said it does not support llms.txt. Rather than using ai.txt or llms.txt, large platforms have provided developer docs or crawler rules to implement similar controls and adapt robots.txt scraping policies.

Governments are only beginning to look at this issue. In the UK, publishers such as Guardian News & Media have asked policymakers to support an ai.txt-style standard. Their submission to a UK Parliament committee described ai.txt as a broad, industry-friendly solution and noted that current proposals from major tech companies don’t fully meet publishers’ needs.

For now, though, no country requires ai.txt or llms.txt. Despite the evolution of AI scraping, 2026 trends in robots.txt are yet uncertain. In the U.S., regulators focus on voluntary disclosure and transparency guidelines. As of late 2025, both formats are not yet ready to become a new AI standard for scraping data. It's unlikely that things will change as early as 2026, but it's possible that a unified system will eventually emerge.

Citations

[1] “Scrapers selectively respect robots.txt directives: Evidence from a large-scale empirical study”, Taein Kim, Duke University, 2024
[2] “Exclusive – Multiple AI companies bypassing web standard to scrape publisher sites, licensing firm says”, Reuters, 2024
[3] “LLMs.txt Directory”, llmstxt.cloud, 2024
[4] “What Is llms.txt? How the New AI Standard Works”, Bluehost, 2025
[5] “Google AI and LLMs.txt Not Yet Implemented”, Search Engine Roundtable, 2024
[6] “Written evidence on AI and web standards (on the importance of robots.txt and llms.txt)”, UK Parliament Committees, 2024

Scraping predictions for 2026: agentic workflow and AI

Astro — Enterprise Data Gathering Infrastructure — Tue, 02 Dec 2025 13:11:00 +0000

What are AI agents for scraping
Agentic AI are autonomous systems based on large language models (LLM) that can plan, execute and adapt using external tools or APIs to complete tasks without human micromanagement. Compared to traditional methods, they dynamically adapt to new work scenarios and solve problems while re-evaluating decisions.
AI agent's workflow:

A user gives an LLM prompt for scraping.
Agent breaks it down into subtasks and organize the work.
Agent automatically asks for additional information if needed.
The task is completed.

AI agents for web scraping can perform assignments that previously required manual scripting. Separate tools already exist for searching, loading webpages, clicking buttons, and filling forms. Instead of executing them by hand, they can be combined and integrated into a unified research agent.

AI agents use automatic scraping tools to:
Navigate to a website.
Handle interactions (clicks, scrolling, waiting for JS).
Fetch HTML or rendered content.
Parse and clean data.
Output structured data (JSON, CSV, etc.).

What makes agentic workflow superior to AI workflow
Traditional AI workflows are usually linear and static:

You send a prompt.
The model answers.
The process ends. Even if you wrap multiple prompts into a pipeline, the system still follows a predetermined sequence created by a developer.

Agentic workflows, on the other hand, introduce autonomy, feedback loops, and decision-making. Instead of simply producing outputs, an agent continuously evaluates its progress, chooses the next action, and adapts when something unexpected happens (a changed webpage, missing data, failed request, etc.).

A standard LLM can help generate an XPath or a parsing rule.

An agentic workflow can run automatic scraping tools back to back: plan navigation, fetch pages, detect failures, replan around CAPTCHAs or broken selectors, and return structured results.

Why agentic scraping matters in 2026
In 2026 the web will outgrow the scraping methods most teams rely on. Pages load data through JavaScript, hide content behind interactions, and change layout frequently enough that traditional scrapers will be more and more costly. Even LLM prompts for scraping depend on manual scripts to navigate pages, handle errors, or make decisions.

AI agents for web scraping makes a difference because they can observe and adapt in real time. Agents can automatically:

Slow down or change their request pattern when they detect rate limits.
Switch from aggressive crawling to incremental, human-like interaction.
Recognize when a site requires authentication and follow the proper flow.
Detect when a CAPTCHA appears and escalate for human intervention instead of failing silently.
Use alternative, allowed data sources (APIs, feeds, cached snapshots) when available.

That’s why agentic AI is a core part of scraping predictions for 2026. It's the next step in the evolution of AI-assisted scraping. The cost of using traditional methods and even non-agentic LLMs will rise and make past methods obsolete.

“Web of agents": a new landscape for automatic scraping tools
According to the 2025 research paper “Internet 3.0: Architecture for a Web-of-Agents", autonomous software agents may become the primary interface points for data and services. It answers the question of how to scrape with AI in the future.

Scraping interactions become protocol-driven: Instead of parsing DOMs, agents request data from other agents that expose defined actions and schemas. This removes the constant break-fix cycle.
Agents automatically discover the best data sources: Discovery / orchestration mechanisms let scraping agents find and switch to whichever peer agent provides the cleanest data.
Reliability is measurable through agent reputation: Scrapers can rely on agent scores to choose trustworthy peers and avoid noisy or outdated sources.
Defenses are handled through collaboration, not brute force: Instead of a single scraper trying to bypass advanced detection, a scraping agent can delegate tasks to specialized peers: CAPTCHA solvers, behavioral simulators, DOM diff analyzers, session-handling agents, etc.
Data quality improves through cross-agent validation: Multiple agents can independently extract or verify the same data with different automatic scraping tools.

Final words
Web scraping and prediction of trends in 2026 is becoming increasingly complex due to dynamic content, interactive elements, and advanced defenses. Traditional scrapers and even LLM-powered parsers struggle to keep up. Agentic workflows address these challenges by combining autonomy, planning, adaptive execution, and cross-agent collaboration.

Looking ahead, as the web evolves toward agent-friendly architectures. Scraping predictions for 2026 also include a shift toward AI agents for web scraping that increasingly rely on collaboration. Teams that explore LLM prompts for scraping and learn how to scrape with AI need also to think in terms of agentic models for long-term results.

Citations
[1] “A Comprehensive Review of AI Agents: Transforming Possibilities in Technology and Beyond”, Xiaodong Qu, George Washington University (2025)
[2] “Internet 3.0: Architecture for a Web-of-Agents with Its Algorithm for Ranking Agents”, Rajesh Tembarai Krishnamachari, New York University (2025)
[3] “AI Browser Agents: Automating Web-Based Tasks with Intelligent Systems”, Amplework (2025)
[4] “What Are Agentic Workflows? Architecture, Use Cases, and How to Build Them”, Orkes (2025)

Effectiveness of traditional and LLM-based methods for web scraping

Astro — Enterprise Data Gathering Infrastructure — Tue, 25 Nov 2025 13:45:42 +0000

Web scraping in 2025 sits at an interesting crossroads. Traditional tools are still widely used and capable, but maintaining large scraping pipelines has become more demanding. Layouts change frequently, defensive systems are being improved, and HTML adjustments that break parsers are implemented faster than before.

At the same time, AI-driven techniques are maturing. Large language models (LLM) don’t replace the fundamentals of crawling, but they do change how we interpret page content and handle structured extraction. According to a 2025 McKinsey research, companies adopting generative AI jumped from 33% to 71% in a single year. Scraping is one of the areas where this shift was expected. More teams explore web scraping LLM methods and AI data scraping to reduce manual maintenance.

In this article we discuss a breakdown of the main AI-powered approaches based on scientific research, what is AI data scraping, and how they compare when tested on real webpages. It assumes that the reader already knows the answer to the question how does scraping work in general.

Where LLMs excel

GenAI gives scrapers three advantages:

Resilience to layout changes: LLMs can be trained to not rely on CSS selectors. They “understand” patterns in HTML or screenshots, making them far more tolerant of renamed classes, reordered and other elements, slight design changes.

Natural language extraction: Instead of writing parser logic, developers can roughly request: “Extract product title, price, rating, and features.” And the model returns structured JSON.

Understanding unstructured content: LLMs prompts for web scraping with ChatGPT, Gemini, DeepSeek and other models can be used to analyze sentiment, tone, classification, and semantic grouping. Dynamic content and JavaScript rendering become less of an obstacle.

Where LLMs fall short

Despite the impressive results, AI-powered approaches have clear pitfalls:

Costs scale with LLM calls.
Latency is higher than local parsing.
Validation is mandatory.
Vision models may hallucinate details.
“URL-driven” method is statistically unreliable.

Where web scraping with LLMs fit into the workflow

Instead of manually defining extraction rules, engineers can use models to interpret HTML or screenshots directly. Depending on the method, the effort involved can range from “just run the code the model wrote for you” to “send cleaned HTML and let it return a JSON structure.”

There are three dominant approaches today.

1. AI-generated code from snippets
With an HTML snippet, an LLM can infer the structure of the page and produce a functional scraper. This is a practical example of AI data scraping in day-to-day workflows.

Typical workflow:

Provide a small HTML sample from the target site.
Describe the extraction task in natural language.
The LLM writes the scraper code.
You review the output and adjust where needed.

If the target website changes, the script can be regenerated or patched with another short prompt. This method doesn’t eliminate per-site customization, but it makes the process faster.

Benefits:

Accuracy is close to a manual scraper for specific websites' structures. It's one of the best scraping tools for experienced teams.
Code is generated once (or more for maintenance) and then LLM doesn't create additional costs.
Generated code is customizable and can be improved manually.

Trade-offs:

Must be maintained for each specific website, same as traditional methods.
High maintenance costs for websites that frequently change CSS classes or layout.

2. Structured data extraction from HTML

Instead of generating code, it’s possible to send raw or cleaned HTML of a page directly to an LLM for web scraping and ask it to produce structured output.

Preprocessing and cleaning the code helps reduce costs in data extraction pipelines: removing navigation, boilerplate, scripts, and irrelevant sections can cut token usage by orders of magnitude.

Benefits:

A single LLM web scraping prompt with ChatGPT can parse pages with different layouts.
No XPath or CSS selectors required.
The model identifies patterns for automated data parsing directly from the HTML.

Trade-offs:

Token usage grows with page size.
Throughput depends on API latency.
Very large pages must be cleaned aggressively.

3. Vision-based extraction using screenshots (computer vision)

A newer but rapidly improving technique that uses screenshots of a rendered page instead of LLM prompts for web scraping. Vision-based LLMs can interpret text, layout, and visual patterns. This approach is especially useful for websites that rely heavily on JavaScript or use markup obfuscation.

Benefits:

Extracts exactly what a user sees.
Can handle dynamic elements, banners, overlays.
Costs are predictable (fixed number of screenshots).

Trade-offs:

Vision models have a higher chance of hallucinations.
Heavier computations.
Sensitive to small visual ambiguities.

4. URL-driven extraction

Some LLMs can browse the web and fetch data directly via a URL passed in the prompt. Its ease of use is appealing for people who don't have a technical background to learn how does scraping work. That said, the method is not yet stable enough for production use.

In testing across thousands of pages, the accuracy fluctuated dramatically. With the same URL, same model, and same prompt, results varied anywhere from 0% to 100% correct. This unpredictability makes URL-to-LLM scraping unsuitable for real-world workloads.

Performance comparison of the methods

Research from McGill University (3,000 pages: Amazon, Cars.com, Upwork) provides a clear comparison across key metrics. There is no clear answer on what are the best scraping tools, but some have an advantage in cost, speed, or accuracy.

The future of AI data scraping

Emerging models, particularly in OCR and 2D layout understanding, show that compressed visual representations can sometimes be more efficient than raw HTML. As these technologies evolve, vision-based extraction may become a standard component of scraping pipelines.

There is also an interesting aspect of URL-driven extraction: while it's too unstable today, the LLM evolution can make it viable for AI data scraping. It's unlikely to happen soon, but it's something to look for in the future, especially with a widespread use of AI agents.

It's unclear what AI data scraping will look like in 2026 and beyond, but even today we look at a tangible improvement in:

Resilience to layout changes.
Development speed.
Cross-site generalization.
Handling unstructured content.

In terms of market forecast, analysts from Technavio predict the AI-based web scraping market will rise to USD 3.16 billion by 2029, growing at a 39.4% CAGR from 2024.

Final thoughts

AI does not eliminate the need for traditional scraping techniques, but it meaningfully enhances them. Automatic code generation, HTML parsing, and screenshot-based extraction each provide reliable ways to interpret complex webpage data with minimal manual logic.
There is no single best scraping tool. Instead, the right choice depends on the complexity of the target site, the acceptable latency, and operational cost constraints. By combining traditional tools like Playwright, Selenium, and Beautiful Soup with modern LLM-based solutions, engineers can improve workflows.
The combination of LLMs for web scraping and rendering tools signals the beginning of more autonomous extraction systems that maintain accuracy while reducing the ongoing maintenance burden that has traditionally dominated web scraping work.

Citations

[1] “Generative AI for Data Scraping", Maxime C. Cohen, McGill University (2025)
[2] “Software Architecture for Improving Scraping Systems Using Artificial Intelligence", Bogdan-Stefan Posedaru, Bucharest University of Economic Studies (2024)
[3] “The state of AI in 2025: Agents, innovation, and transformation”, McKinsey (2025)
[4] “AI Driven Web Scraping Market Analysis”, Technavio (2025)
[5] “AI-Powered Web Scraping in 2025: Best Practices & Use Cases”, Expert Beacon (2023)
[6] “From Manual to Machine: How AI is Redefining Web Scraping for Superior Efficiency”, Enrique Ayuso, CCIS (2025)

Can proxies improve accuracy of keyword rank tracking?

Astro — Enterprise Data Gathering Infrastructure — Tue, 18 Nov 2025 15:11:53 +0000

If you run SEO campaigns, keyword rank tracking is still one of the core ways you measure visibility, growth, and report back to clients. The problem is that there is no single “true” ranking anymore: different users see different SERPs depending on their location, device, language, search history, and account settings. That’s why the position you see in your browser often doesn’t match what your SEO tool reports, even when both are technically “right.”

Why keyword rank tracking is harder than it looks
On paper, keyword rank tracking sounds simple: you pick a keyword, run a check, and log the position. There isn’t just one SERP for each query. Results change based on where a user is sitting (country, city, even neighborhood), their language settings, and the long trail of clicks, logins, and preferences that power personalization. Two people can search for the same keyword at the same time and see very different pages.

Example of daily SERP volatility for a single keyword over 10 days. Even with no SEO changes or proxies involved, the same query can jump up or down by several positions.

Technical factors add another layer of noise before keyword rank tracking proxies or SEO proxies for rank tracking even enter the picture. Modern SERPs are full of maps, video carousels, short-form content, and “People Also Ask” boxes. Layout and rankings also shift between mobile and desktop, and algorithm updates or fresh news results can reshuffle positions many times a day. In practice, “accuracy” means approximating what a specific audience sees in a specific place on a specific device.

Different locations and languages return different SERPs.
Devices and SERP features change what “position” really means.
Constant tests and updates make rankings a moving target.

Where rank tracking data gets skewed
Before we even talk about proxies for rank tracking, it’s worth seeing where the numbers go wrong in the first place. A lot of the “inaccuracies” people blame on rank tracking proxies.

You check positions in your own browser while logged into Google, with a long search history and personalized settings. That’s why you might see your site at #3 while your tool reports #7: you’re both looking at different, equally “valid” SERPs.
Many tools run all their checks from a single IP in a single country, so they simply don’t see all your target regions and devices.
Without solid proxies for keyword rank tracking or proxies for SERP tracking, tools hit rate limits, CAPTCHAs and partial result sets, which quietly introduces gaps and bias into your reports.

When considering how to choose the right proxy for tracking positions in SERP, the key is balancing geo‑precision with technical reliability.

How proxies improve ranking accuracy
Once you understand where rankings get distorted, it’s easier to see how proxies for rank tracking can raise the ceiling on accuracy. The goal isn’t to “game” Google, but to get closer to what real users in specific locations see when they search.

Geo-targeted SERPs With the right rank tracking proxies, you can route checks through IPs tied to specific countries, cities, or even neighborhoods. Instead of one generic datacenter IP, you see the SERP as local users do, which makes proxies for SERP tracking essential for multi-location SEO.
Less personalization, cleaner data Neutral IPs from SEO proxies for rank tracking aren’t tied to your personal account or history, so they strip out a big chunk of personalization noise. Many tools deliberately fan out requests across different keyword rank tracking proxies to avoid your own browsing behavior leaking into the data.
Stability and scale Rotating pools of proxies for keyword rank tracking reduce rate limits and CAPTCHAs. That means fewer gaps in your dataset and the ability to monitor more keywords, markets, and devices on a regular schedule.
Specialized SEO / rank tracking proxies Purpose-built keyword rank tracking proxies are tuned for SERP scraping, with robust geo-targeting, performance guarantees, and monitoring. They’re very different from a random one-off proxy that just happens to work today.

Knowing how to choose the right proxy for tracking positions in SERP means prioritizing providers that offer verified IP locations, low latency, and anti-detection measures.

Proxy setups for different SEO scenarios
Not every team needs a huge infrastructure of proxies for rank tracking. A freelancer or in-house specialist can do a lot with a handful of well-chosen rank tracking proxies, while an agency or enterprise setup needs something closer to a mini data platform. In all cases, a stable setup beats any fragile free proxy that might disappear tomorrow.
Before choosing how many proxies you need for your workflow, it helps to have a checklist for choosing reliable proxies for rank tracking instead of guessing based on price alone. The table below summarizes the core criteria that matter for keyword rank tracking proxies and proxies for SERP tracking:

1. Solo SEO / small in-house team
If you’re running one or two projects in a couple of countries, keep it simple. Grab a small bundle of geo-targeted proxies for keyword rank tracking in your main locations, enable basic rotation, and plug them into your preferred rank tracker. For occasional manual checks, route your browser through the same proxies for rank tracking so what you see matches the tool’s data as closely as possible.
2. Agency with multi-location clients
Agencies handling many cities and markets need a broader pool of rank tracking proxies distributed across key regions. Set per-project limits and rotation rules and let your tools fan out requests through SEO proxies for rank tracking and proxies for SERP tracking that mimic real local users. This keeps keyword rank tracking proxies fresh and makes location-specific reporting much more defensible.
3. Custom tools and APIs / enterprise setup
If you’re building your own dashboards on top of SERP APIs, pair them with a serious provider of proxies for rank tracking, not a random free proxy. Here, accuracy hinges on how your code handles JavaScript rendering, query parameters, throttling, and how intelligently you allocate rank tracking proxies across markets and devices.

Do proxies improve keyword rank tracking accuracy?
In short: yes. Well-implemented proxies reduce a lot of technical noise that makes keyword data unreliable. With a simple checklist for choosing reliable proxies for rank tracking, you can avoid flaky providers and end up with neutral IPs and proper geo-targeting that give you cleaner snapshots of what searchers in specific locations see instead of whatever your logged-in browser happens to show that day. But proxies don’t replace solid SEO fundamentals. You still need a sensible keyword set, a good grasp of local intent, and context from Search Console, analytics, and real user behavior. Accurate rankings are just one piece of that bigger picture.

Use proxies responsibly: respect ToS and local laws, throttle your checks, and don’t build mission-critical reporting on a flaky free proxy. Aim for reliable, transparent data rather than artificially “perfect” reports.

Citations
[1] MarketBrew — “Rankings from Search Engine Personalization” (2025).
[2] SEO Singapore Services — “Dominate Search Results: Understanding SERPs in 2024” (2024).
[3] DesignRush — “Why SEO Tracking Tools Are Struggling to Reflect Real Google Search Results” (Last updated May 1, 2025).
[4] Astro Blog — “Proxy Infrastructure Transparency Checklist” (Nov 4, 2025).
[5] KeyGroup / Smart Tips for Free Advertising — “Proxy Servers for SEO: Best Practices for Enhanced Rankings” (Mar 25, 2025). Overview of proxy types, SEO use-cases and risks of low-quality or free proxy services.
[6] Ranktracker — “Working Smarter in SEO: How SEO Proxies Boost Accuracy and Efficiency” (Nov 5, 2025).
[7] Ethical Web Data Collection Initiative — “Is Web Scraping Legal? Navigating Terms of Service and Best Practices” (Jan 7, 2025).

Build vs Buy for AI-Driven Scraping in 2026: Costs, Compliance, Velocity

Astro — Enterprise Data Gathering Infrastructure — Tue, 30 Sep 2025 08:35:41 +0000

AI scraping isn’t “selectors + retries” anymore. Self-healing extraction, audit-ready logging, and jurisdiction controls changed the math. If you need time-to-value, compliance evidence and global coverage, buying a managed platform often wins; if you need bespoke control and can staff it, building can still pay off. kadoa.com

Why 2026 is different
● Self-healing extraction goes mainstream: LLM-assisted parsers adapt to layout/DOM drift instead of constant manual fixes.
● Provenance & observability are table stakes: per-request traces/session logs show how each record was fetched and transformed.
● Compliance by design (EU AI Act): purpose limits, human oversight and reproducible evidence move from “nice to have” to mandatory. kadoa.com
● Anti-bot escalation: stronger fingerprinting/headless checks and dynamic challenges demand active mitigation — not just bigger pools.
● Cost model shifts: brittle rules → model inference + evaluation budget; different spend profile, lower break-fix toil, new skills needed.
● GenAI-assisted extraction: LLMs can generate scrapers from small HTML samples, clean pre-processed markup, and even interpret page screenshots via CV, reducing manual fixes and improving resilience to layout changes.

A quick decision matrix

Time-to-value. If a business outcome depends on next quarter’s data, buying a managed pipeline usually wins: you get coverage in days and a predictable rollout path. Building pays off when data is so bespoke that generic platforms stall — or when you want scraping to become a core capability, not a means to an end.

Maintenance burden. Build means owning drift: schema changes, login flows, and mitigation tactics. Self-healing reduces toil but doesn’t remove it — you’ll still need tests, canaries and owners on call. Buying shifts that burden to a vendor, so your team focuses on validation and downstream impact.

Observability & reliability. In-house: budget time for traces, metrics, structured logs, and runbooks. Managed: demand per-session evidence (request, headers, location, status), correlation IDs and SLAs tied to your error budgets.

Compliance & ethics. If you build, implement purpose limits, deletion pathways and audit trails; nominate a reviewer for “red-lines” (what you will not collect). If you buy, verify those controls, who sees the logs, and how long evidence is retained; insist on jurisdiction filters and a data-processing addendum.

Flexibility & lock-in. Build maximizes edge-case control; buy maximizes coverage velocity. Either way, plan your exit: define portable schemas, keep your extraction logic/versioning in Git (even when you buy) and isolate vendor SDKs behind a thin adapter layer.

Hybrid reality. Most teams land here: a managed backbone for 70–90% of sources; custom actors for the hardest targets. Review monthly drift, source health and cost per successful record; prune what no longer moves the metric you care about.

Illustrative 3-year TCO (Build vs Buy)

What the chart suggests. The steep part of “build” is not only initial engineering — it’s the compounding cost of drift handling, on-call and upgrades to the fetch/anti-bot stack. “Buy” front-loads less capex and shifts more to usage-based opex; your main variables become volume, concurrency and SLA tiers. Neither path is free: the question is which set of uncertainties you want to manage.

Compliance & ethics (what “good” looks like in 2026)
● Risk-based controls aligned with the EU AI Act: clear purpose limits, audit trails and human oversight.
● Jurisdiction filters & KYC/AML for traffic sources; request-level logging for explainability.
● Prohibited practices awareness (e.g., biometric/facial scraping bans in the Act).
If you build, you must implement these controls; if you buy, verify that they’re first-class features. digital-strategy.ec.europa.eu
artificialintelligenceact.eu

Architecture that holds up in production

Orchestration: DAGs give you explicit schedules, dependencies and callbacks for failure pathways. Apache Airflow
Observability: Use OpenTelemetry to correlate traces (per-request path), metrics (SLA/error budgets) and logs (session-level evidence). Scrub PII at the collector. OpenTelemetry
Self-healing loop: Retrain/adjust extractors from drift signals (failed selectors, layout diffs), not only HTTP codes. Keep golden tests for high-value pages.

Practical guidance
Choose “buy” when time-to-value is critical, coverage is broad, compliance needs are strict and your team is <3 FTE for data collection.
Choose “build” when you need bespoke logic, strict cost ceilings at scale or deep integration with internal feature stores/LLM tooling — and can staff platform/DevOps/ML ownership.
Hybrid works best for many teams. Use a managed backbone for the easy 80%; build custom actors for the hardest 20%. Budget a monthly “drift & debt” day to keep error budgets honest. Harvard Business Review

Citations
[1] Kadoa — “Build vs Buy: LLM Adoption for Web Scraping in Finance” (Oct 28, 2024). Interviews with 100+ data leaders; self-healing & buy-vs-build drivers.
[2] European Commission — “AI Act (Regulation (EU) 2024/1689) overview.” Risk-based framework and timelines.
[3] ArtificialIntelligenceAct.eu — “Article 5: Prohibited AI Practices” + high-level summary.
[4] Apache Airflow Docs — DAGs & Scheduler (stable). Orchestration concepts for repeatable scraping jobs.
[5] OpenTelemetry Docs — Collector & Traces overview; correlated logs/metrics/traces and PII scrubbing.
[6] Harvard Business Review — “It’s Time to Invest in AI: Here’s How” (Jul 2, 2025). Governance-first framing for build/buy decisions.
[7] GDPR — Article 5 principles + overview (gdpr-info.eu; gdpr.eu). Data minimization and lawfulness/transparency.
[8] Justia — hiQ Labs, Inc. v. LinkedIn case materials (9th Cir.). Boundary conditions for “public” scraping in U.S. law.
[9] AP/Reuters — EU implementation guidance & timelines for AI Act in 2025–2026.
[10] Astro Blog — Astro slide set AI scraper cycle 2025–2026 (2025).

Geo-restricted AI Video Generators: Is It Possible to Use Google's Veo3 Outside the US?

Astro — Enterprise Data Gathering Infrastructure — Mon, 29 Sep 2025 09:29:28 +0000

In May 2025, Google launched Veo3, an AI generator capable of producing videos with synchronized native audio output. It quickly gained attention as a reliable tool for creative projects. The problem is that users can't access Veo3 in Europe, Asia and other countries outside the US.
This is to do with geo-restriction policies. In this article we’ll look into why these policies exist and what practical options are available for regular users. The solution is a little more complex than using a random VPN for Google products.

Why Google Applies Geo-restrictions for AI Services
Limiting access to Veo3 is not an arbitrary decision. It's a common practice for dealing with potential problems and limitations, such as:
● Regulatory frameworks: Different countries regulate AI-generated media in their own way.
● Infrastructure capacity: Rolling out the product step by step helps keep systems stable.
● Security and oversight: Launching in a controlled manner helps the provider monitor usage.

Compliance with regulations is the main reason for geo-restrictions. Keep in mind that it's about demonstrating control at the policy level, not monitoring every single connection attempt. If you decide to connect through another access method in good faith, it won't draw attention from Google.

Using Veo3 in Europe and Other Regions
Aside from relocating physically, you can redirect internet traffic through an intermediary — a proxy server. This way Google sends data to the server, and the server passes it on to the user. As we've mentioned, picking any cheap VPN service is not a sustainable solution. Google monitors unusual network behavior.

A more stable approach involves using whitelisted IP addresses that create conditions for stable access. These are sourced with consent and linked to verified users. Therefore, it's important to choose a trustworthy company that offers such solutions.

Exploring Access Options for Veo3
Once geo-restrictions issue is resolved, you can move to choosing the plan. The prices range from $37.5 to $250 a month depending on your needs. Veo3 can be accessed through the Gemini app, Flow, Google AI Pro or Ultra, and Vertex AI.

If you're not ready to commit to paid options yet, you can explore free Veo3 versions:

Time-Limited Trial (30 days)
Steps:

Apply for a trial period on the official Gemini page.
Provide payment details together with a valid US address and ZIP code. The card itself does not need to be issued in the US.

Free Credits with Google Cloud ($300 credit for 90 days)
Steps:

Create a Google Cloud account.
Enable the Vertex AI API and choose the model veo-3.0-generate-preview.
Use REST API calls to submit prompts and render videos. Refer to Google’s official documentation for further instructions.

Student Plan Access (12 months)
Steps:

Obtain an .edu email address (you'll likely need to inquire a school or university).
Visit the Google AI Pro Student Portal and request access.

Getting Started with Veo3
After you choose a suitable plan, you can get started with Veo3. A fleshed out guide for Veo3 is out of scope of this article, but we will provide you with general directions:
● Official guide from Google DeepMind.
● Official guide from Vertex AI.
● Official guide from Gemini API.
Make sure to also check detailed tutorials on YouTube from various video creators, and other articles on Dev.

Conclusion
AI video generation is quickly moving forward, but geo-restriction policies are becoming a common practice as well. Even though people in most countries can freely create a Google account, they still can't register for Veo3.

It's a perfect time for international users to look for responsible ways to access restricted content, mainly through whitelisted proxies. Staying mindful of ethical usage will help creators and researchers make the most of this technology.

SEO Proxies for Search Engine Monitoring

Astro — Enterprise Data Gathering Infrastructure — Thu, 18 Sep 2025 10:26:47 +0000

Search engine monitoring measures what users see in each locale. SERPs differ by location, language, device, and settings; AI Overviews/AI Mode further change page composition and above-the-fold real estate.

Proxies provide controlled vantage points to reproduce those conditions. Use datacenter, residential, ISP (static residential) or mobile IPs as needed; rotate for page-one sampling and keep sticky sessions for pagination and stable local packs.

Work within platform terms and anti-automation controls. Prefer official channels (Search Console, Programmable Search JSON API) where applicable; for observational checks, pair proxies with realistic browsers and capture organic, local, ads, and AI sections.

Why Proxies Matter for Search Engine Monitoring in 2025

Google’s results are not universal. Composition and ranking change with location, language, device, and settings, and 2025 rollouts of AI Overviews / AI Mode further alter what appears above the fold. A snapshot from a single office IP or VPN cannot represent what users in another city or on another device see. To measure real visibility, teams need controlled vantage points that reproduce user context per locale and device — i.e., proxies with predictable geo and network traits.

Operationally, this means standardizing the viewing environment you measure fix UI language and SafeSearch, emulating the intended device (desktop vs. mobile) and route requests through country/city-targeted IPs that reflect the audience you care about. Without the correct IP and device profile, “page-one” and “above-the-fold” metrics are distorted, because AI surfaces can occupy primary screen real estate and push the first organic result downward (Google Help).

Monitoring should explicitly capture both classic SERP modules (organic results, local packs, ads) and AI surfaces (presence/absence of AI Overview, entities/links it promotes, citations shown). Treat the screenshot checklist as your capture spec: detect whether AI Overview renders (①), persist the items it recommends (②) and record ancillary modules in the right rail that vary with locale/device (③). Proxies are therefore not a growth hack; they are baseline instrumentation to audit what real users see in each market while keeping the environment reproducible and auditable.

Proxy Options & Sessions: Datacenter, Residential, ISP, Mobile; Rotating vs. Sticky

When selecting proxies for SERP observation, evaluate them as capabilities across seven dimensions: IP origin, detectability, stability, cost, geo-targeting, session behavior and fit for monitoring tasks. The matrix below summarizes those trade-offs and is intended to be read while following this section:

Sessions control state. Rotating assigns a new IP per request — best for breadth and minimal fingerprint persistence. Sticky holds the same IP for a defined window (commonly ~5–10 minutes or until idle) via vendor session IDs or sticky ports; Mobile/ISP pools are usually sticky by default, while Datacenter/Residential can be run in either mode. Sticky is required for pagination, “More results” or keeping a local pack constant across interactions.

Apply a simple rule: pick the least-detectable pool that meets your geo precision and session needs. For city fidelity or stable local modules, prefer Residential (Sticky) or Mobile; for economical breadth with country accuracy, Datacenter (Rotating) suffices; for durable identity without mobile costs, use ISP/Static (Sticky). The table encodes these choices so teams can standardize configurations instead of tuning by trial and error.
Beyond Traditional Search - Proxies for SEO and GEO

Compliance & Official Alternatives You Should Not Ignore

Google ToS & robots.txt. Google’s Terms prohibit using automated means to access content in conflict with machine-readable instructions (e.g., robots.txt) and historically warned against “automated querying” to determine ranking. Practically: scraping google.com without permission risks violating contractual terms. Always honor robots.txt, rate-limits, and product-specific policies. (policies.google.com)

2. Sanctioned Google access.
• Programmable Search (Custom Search) JSON API — paid, permissioned access to your configured search engine(s); not a general Google Search API. Pricing: $5 per 1,000 queries, 100 free/day, hard cap 10k/day per project. Use for programmatic querying within its scope.
• Google Search Console (UI/API) for your own site only. As of 2025, the API exposes hourly data for ~10 days (plus daily aggregates), including documented metrics such as position, queries, pages and countries. Prefer this for ranking/visibility analysis of your properties.

3. Microsoft/Bing transition. Bing Search APIs retire on 11 Aug 2025. Microsoft directs customers to Azure AI Agents with Grounding with Bing Search as the migration path. Teams relying on Bing’s legacy endpoints should plan replacement now.

Bottom line: use official channels where they cover the use case; if observational SERP checks are necessary, implement legal review, respect robots.txt, and isolate them from any systems that must remain strictly ToS-compliant.

Ethical proxy providers note that some websites may apply access limitations based on IP address or location, so when restrictions occur it’s important to review the IP’s status with diagnostic tools and contact support for case-by-case evaluation. This helps determine whether the limitation is technical in nature and prevents distorted monitoring results.

Implementation Playbook: Locale & Device Repro, Anti-Bot Realism, and What to Capture

Reproduce user context first. Fix language and region parameters (e.g., hl, location), keep a signed-out baseline and set SafeSearch explicitly. Drive requests through proxies that match the audience’s country — and city when local packs matter — then emulate the intended device: viewport/DPR, client hints or a stable UA and realistic mobile bandwidth. Without this, “page one” and above-the-fold metrics skew, especially where AI Overviews/AI Mode reshuffle layout.

Choose proxy and session behavior to match the task. Use datacenter (rotating) for economical, broad Page-1 sampling at country level. Use residential or mobile for city fidelity and tougher locales; keep sticky sessions to preserve state across pagination and “More results.” ISP/static residential sits between — durable identity at country level without mobile costs. Log proxy metadata on every run (IP, ASN, country/city, session ID, stickiness window) so results are auditable.

Render like a real browser and respect controls. Run a modern Chromium/WebDriver-BiDi/Playwright stack instead of raw HTTP and expect defenses such as Cloudflare Bot Management and reCAPTCHA Enterprise. Keep interaction pacing human-like, cap concurrency, warm sessions before deep navigation and back off on challenges rather than forcing solves. Separate any observational pipelines from systems that must remain strictly ToS-compliant; enforce robots.txt and product-specific policies upstream.

Capture what affects visibility and makes it measurable. Persist the viewport-1 screenshot and a full HTML/DOM snapshot; record presence and positions for AI Overview/AI Mode, ads (top/bottom), local pack, organic and right-rail modules. Define scoring rules that account for AI displacement (e.g., “visible in viewport-1” vs. “rank index including AI”). Sample per locale×device on a fixed cadence and validate directionally against your own site’s Google Search Console data (hourly/daily). Version all configs, store artifacts with manifests, and keep running reproducible end-to-end.
Astro — On the Top of Geo-Targeted Proxy Services in 2025

Citations:
[1] Google Help — Why your Google Search results might differ (lists location, language, device as variation factors).
[2] Google Help — Understand & manage your location when you search (how location is determined/used).
[3] Google Policies — How Google uses location information (sources incl. IP/device).
[4] Google Blog — Expanding AI Overviews and introducing AI Mode (Mar 5, 2025).
[5] Google Blog — AI Mode adds agentic features & expands (Aug 21, 2025).
[6] Google Terms of Service — prohibition on using automated means contrary to robots.txt.
[7] Google Developers — robots.txt intro (machine-readable instructions).
[8] Google Developers — Custom Search JSON API (pricing/limits). [9] Google Developers — Search Analytics API now supports hourly data (Apr 2025).
[10] Microsoft Learn — Bing Search APIs retirement (Aug 11, 2025). l
[11] Cloudflare Docs — JA3/JA4 fingerprint (fingerprinting in Bot Management).
[12] Cloudflare — Bot Management product page (ML, behavior, fingerprinting).
[13] Google Cloud — reCAPTCHA Enterprise quotas (rate/usage controls).
[14] Oxylabs Dev Docs — Session Control (sessid; default ~10-min or idle timeout).
[15] Oxylabs Dev Docs — Sticky proxy entry nodes (same IP up to ~10 minutes).
[16] Bright Data Proxy Manager — session header (x-lpm-session to persist IP).
[17] Oxylabs — ISP vs Residential proxies (2025) (type definitions, detectability trade-offs).
[18] Astro Blog — Astro presentation 2025 Empowering innovation with public data (2025).

ElevenLabs & proxies: essential integration guide

Astro — Enterprise Data Gathering Infrastructure — Thu, 18 Sep 2025 09:54:28 +0000

ElevenLabs has transformed AI audio generation with their Eleven v3 model, supporting 70+ languages and delivering human-like speech with emotional depth. When integrating ElevenLabs into applications, proxy services offer critical advantages for security, performance and scalability.

Why use proxies with ElevenLabs?
Proxies provide multiple benefits when working with ElevenLabs.

Security protection
From a security standpoint, they ensure API key safety by keeping sensitive credentials on the server side rather than exposing them in client applications. They allow authentication control through custom user validation without compromising API access and reduce the attack surface by centralizing security management through a single proxy endpoint.

Traffic management
Proxies play a key role in managing traffic when integrating with ElevenLabs. Instead of overwhelming the API with a flood of requests, a proxy can intelligently throttle them to prevent rate-limit errors. During peak demand, traffic can be buffered and released in a controlled way, ensuring stability. This also allows multiple users or processes to run simultaneously without breaching ElevenLabs’ concurrency limits.

Performance optimization
Performance optimization is another major advantage. Proxies can cache audio files to minimize redundant API calls, reduce latency by serving cached content instantly and cut costs by eliminating unnecessary API usage through smart caching strategies.

Monitoring & analytics
Monitoring and analytics are enhanced with proxies, as they make it possible to track request patterns, identify optimization opportunities, implement automated retry logic, manage errors efficiently and maintain detailed audit logs for compliance and troubleshooting.

Geographic flexibility
Finally, proxies provide geographic flexibility. They can bypass corporate firewalls, route requests through optimal regions for better performance and support compliance with data residency requirements across different jurisdictions.

Key proxy features for ElevenLabs

When selecting a proxy for ElevenLabs integration, several quality standards are essential. These include whitelisted IPs, global coverage spanning over 100 countries, dynamic rotation for reliability and ISP diversity for optimal routing.

The infrastructure capabilities of a suitable proxy should include session control, support for HTTP(S) and SOCKS protocols, seamless VPN integration and real-time IP switching.

Implementation architecture

Recommended setup

Essential components
Essential components include an authentication gateway to validate users, a cache layer to store frequently requested audio, a rate limiter to prevent overuse, a monitoring system to track errors and performance, and a proxy router to manage IP rotation and geographic targeting.

Best practices

Security implementation
For security, API keys should always remain on server-side infrastructure, per-user authentication and authorization should be enforced, traffic patterns should be monitored for abuse and cache keys should use content hashing to prevent data leakage.

Performance optimization
Performance is another area where proxies make a noticeable difference. By caching frequently requested audio, they reduce the number of API calls needed, which not only saves money but also speeds up delivery. For repeated requests, cached content can be served instantly, creating a smoother and more efficient user experience.

Compliance & monitoring
From a compliance and monitoring perspective, all requests and responses should be logged, alerts should be set up for traffic spikes and high error rates, circuit breakers should be implemented for automated failure handling and proxy infrastructure should undergo regular security assessments.

Choosing the right proxy service

Quality indicators
🔍IP reputation: Verified whitelist status with major platforms
🔍 Geographic coverage: Comprehensive country and city-level targeting
🔍Uptime reliability: Proven track record of consistent availability
🔍 Support quality: Responsive technical assistance and troubleshooting

Service features
● Instant testing: Free trial options to validate performance
● Flexible pricing: Usage-based models starting from small data packages
● API integration: Programmatic IP management and rotation controls
● Compliance standards: KYC/AML procedures and abuse prevention measures

Common use cases

Enterprise applications
● Multi-tenant SaaS: Serve multiple customers with isolated proxy instances
● Global platforms: Optimize performance across different geographic regions
● High-volume processing: Manage thousands of concurrent audio generation requests

Development scenarios
● API Key Protection: Secure development environments without credential exposure
● Testing & QA: Simulate different network conditions and geographic locations
● Load Testing: Validate application performance under various traffic patterns

Specialized applications
● Corporate Networks: Ensure reliable API access through restrictive firewalls
● Compliance Requirements: Meet industry-specific data handling regulations
● Cost Optimization: Reduce API expenses through intelligent caching strategies

Technical considerations

Successful integration requires server infrastructure capable of handling proxy middleware, SSL/TLS termination for secure connections, database storage for caching and sessions and monitoring tools for performance and analytics.

Scaling factors include request volume, geographic distribution needs, cache storage capacity and optimization of bandwidth and latency.

Getting started
1. Assess requirements: Determine traffic volume, geographic needs and security requirements
2. Select proxy provider: Choose service with whitelisted IPs and appropriate geographic coverage
3. Design architecture: Plan authentication, caching and monitoring components
4. Implement gradually: Start with development environment then scale to production
5. Monitor & optimize: Continuously track performance and adjust configurations

Proxy integration transforms ElevenLabs from a simple API service into a robust, enterprise-ready audio generation platform capable of serving global audiences with optimal performance, security and cost-efficiency.

Citations
[1] ElevenLabs, “Introducing Eleven Multilingual v3 – 70+ Languages, Audio Tags, and More,” ElevenLabs Blog, July 2024 (elevenlabs.io)
[2] ElevenLabs Help Center, “What Languages Do You Support?” Accessed September 2025 (help.elevenlabs.io)
[3] ElevenLabs Docs, “Eleven v3 Model (Alpha) – Features and Latency Considerations,” Accessed September 2025 (elevenlabs.io/docs)
[4] TechCrunch, “Why Startups Are Embedding AI Voice Tech into Their Apps,” March 2024 (TechCrunch)
[5] Cloudflare, “How Proxies Improve API Security and Performance,” July 2023 (Cloudflare Blog)
[6] OWASP Foundation, “API Security Best Practices: Authentication, Rate Limiting, and Caching,” May 2024 (OWASP)

From manual to autonomous: How AI-driven scraping saves 40% of time

Astro — Enterprise Data Gathering Infrastructure — Fri, 04 Jul 2025 14:48:22 +0000

Collecting internal and external data along with further analysis and insights’ extraction simplifies real-time decision-making. In 2025, the main competitive advantages are not about accessing information, but getting and leveraging it before competitors. While for businesses this means higher ROI, in healthcare or surveillance such timing saves lives.

Traditional web scraping has become inadequate to the dynamic structure of target websites and their bot-detection protection, petabytes of information appearing yearly, and other features of the Information Age. That’s why two thirds of companies (65%) leverage AI-based techniques (or are close to it) in collecting and studying data.

Deploying artificially intelligent frameworks as routine scraping tools is not just about obtaining the information faster. It's about creating self-healing, adaptive systems which integrate seamlessly and ethically through geo targeted proxies into RAG-powered chatbots and LLMs. They turn raw web data into contextualized intelligence, easing critical decision-making and sparing time and resources.

From HTTP requests to headaches: what is scraping?

Web scraping requires a set of programs which automatically extracts data at scale through:
● Preliminary steps, such as buying residential and mobile proxies or setting up cloud storage.
● Active phase — sending programmatic HTTP requests, performing HTML parsing, gaining required content, and providing some structured content at the output.

A typical scraping workflow looks like this sequence:
HTTP Request → HTML Response → DOM Parsing → Data Extraction → Storage/Processing.

A configured framework (requests, axios, Scrapy, urllib3, ZenRows, etc.) sends GET/POST calls to target URLs. The target site checks queries’ authenticity and provides the required information, if the location meets metadata. To pass this check, use rotating geo targeted proxies. A free test of API and URL commands lets data experts find an ethical infrastructure for gathering complex data.

Then BeautifulSoup or Cheerio traverse the DOM (Document Object Model) using predefined selectors, acquire structured and unstructured info, and present it as datasets.

The global scraping market is estimated at more than $1 billion in 2025, with potential to double by 2030. The driver of such a rapid expansion is the need for tools, which can handle dynamic, JavaScript-heavy sites and unstructured data. There are niche-market products, but the most popular data collection solutions* are presented by BeautifulSoup, Selenium, Playwright, and Puppeteer.

*according to the Apify 2025 report.

9 common web scraping challenges: how to overcome them

Among the reasons explaining the scraping tools’ industry development is a desire to cope with challenges of finding and getting public internet information.
A scraping architecture faces two main bottlenecks:

IP (account, hardware) freezing — when defensive systems associate automated activity with a single IP address. The best solution is to connect datacenter proxies, 4G/5G or residential IPs with dynamic addresses to the pipeline, and imitate real-user behavior.
Selector breakage, caused by changes of site layout or HTML structure. XPath strategies and selectors with unique IDs, like data-* attributes, raise the success info collection rates up to 43.5% for BeautifulSoup users.

The whole info collecting pipeline relies heavily on fixed selector logic. The system looks for specific HTML elements through hard-coded CSS selectors or XPath expressions. And when a website updates its layout, even minor changes (e.g. renaming) can lead to silent failures or even halt data collection.
Tools like Playwright, Selenium, and Puppeteer:

Automate recurrent tasks in single-page apps based on React, Vue, or Angular.
Spoof User-Agent headers and fingerprints through stealth plugins.
Operate dynamic or JavaScript-heavy sites.
Access content rendered on a client side.
Mimic real-user behavior.
Maintain the requests frequency through time.sleep(), asyncio libraries.

To process data retrieval without limitations, restrictions or flagging, a pipeline relies on ethical and transparent solutions — from buying residential IP to scheduling tasks, tracking scraper health, and overcoming other obstacles. As the number of elements is growing, their limitations accumulate, and lead to budget and time losses.

Neural networks address non-AI constraints thanks to adaptive logic, intelligent error recovery, and seamless integration with supplementary software.

AI data collection: what is it?

AI-enabled data collection is regarded as a scraping pipeline with integrated machine learning (ML) tools. They don't just collect data, but understand it and adapt to the changing sites’ structures. Then intelligent systems enrich the information and transfer it into RAG frameworks if needed, or offer informed business decisions.

Artificial intelligence boosts data collection up to 40%, because NLP-driven systems:

Self-diagnose pipeline malfunctions.
Adapt to dynamic site structures.
Provide seamless access to target platforms for automation scripts.
Prioritize tasks and manage their sequence.
Detect site’s elements to retrieve through ML cycles.
Buy residential and mobile proxies according to the task, test, and maintain the intermediate infrastructure.
Extract and clean raw HTML or JSON into structured formats.
Check the information’s quality to eliminate the outliers.
Run feedback loops for more accurate results and lesser time losses. Rising volume of investment in AI-based scraping solutions with projected $8 billion market size by 2032 makes sense.

Capabilities for AI and ML from Astro - Part 1

How does AI data scraping work: 5 basic stages

DeepSeek, Gemini, Claude, Perplexity, ChatGPT, and other models are in fact extensive projections of a human mindset. Used for info gathering purposes, NLP-enhanced agents pass the same workflow phases as non-AI projects do.

Data scraping with AI includes 5 stages of work:

Finding Target URLs.
Inspecting the platform for required information.
Sending requests.
Extracting the info.
Storing and cleaning the results. With machine learning aboard, an AI-based info obtaining workflow follows the scheme: Let us explain the diagram, mentioning examples of the best datacenter proxies and other SaaS and standalone solutions: Companies already prefer AI-based tools for enterprise-grade data collection to non-ML ones. By 2034, the former market is estimated at $38.44 billion, which is four times larger than the latter, according to the Market Research Future (MRFR) Report. Additional free proxy tests, trial access to SaaS, integration frameworks, and other components of a ML-oriented workflow let businesses spare even more time, checking setups before scraping sessions.

Scraping with AI: top 8 reasons to use machine learning for data collection

Collecting public data on the internet with AI saves time and eliminates the traditional bottleneck between data discovery and actionable intelligence.

Reasons to use machine learning for scraping are:
1. Adaptability. LLM-powered scripts simplify choosing CSS/XPath selectors through:
a. Processing natural language prompts
b. Auto-updating selectors when target layouts/class/ID changes
c. Choosing attributes unlikely to change (e.g., data-testid) or generating robust selectors via chatbots.
2. Hybrid methodology. AI-directed models choose info extraction scenarios relying on their previous “experience” to:
a. Process static and dynamic content within a single session. The former requires direct HTTP requests, and headless browsers assist in navigating JavaScript-driven platforms.
b. Prioritize API to HTML where applicable to collect web insights faster up to 20–40%. Without need to render JS or search HTML for desired elements, scraping robots address requests directly to servers.
c. Accept manual verification and API calls, which will fill missing or inaccurate page elements.
3. Advanced navigation. Intelligent systems identify data fields — text snippets, HTML tags, UI elements, page structure, and so on — and obtain structured, labeled data. The AI-based technologies are:
a. Computer Vision for layout recognition.
b. NLP for classifying texts and entities as prices, names, locations, dates, and other.
Act ethically on each data collection stage, be it buying residential and mobile proxies from AML/KYC-compliant service or storing datasets.
4. Semantic understanding of unstructured, raw informational assets. LLM deploys:
a. Entity recognition.
b. Context-aware search.
c. Semantic filters for recognizing text elements.

5. Advanced JS rendering. Optimizes requests to JavaScript layouts, applying:
a. Server-Side Rendering (SSR) — fully rendered HTML generated before JavaScript execution.
b. Client-Side Rendering (CSR) with Hydration — instead of HTML rendering, JS builds the full DOM.
c. Selective Script Execution — AI refuses from non-essential scripts (ads, analytics), images and fonts, reducing bandwidth and CPU load.
d. Backend API Querying — cognitive architectures replace obtaining rendered HTML with reverse-engineer API calls. So LLM connects to the web insights’ sources directly.
e. Incremental Static Regeneration (ISR) — static content gets updates only when necessary. Cloud storage like AWS3 or datacenter proxies is the best solution for frameworks like Next.js to cache the info locally.
AI-driven scraping boosts customer analytics and overall performance by 37%, McKinsey claims, and solves other data collection issues.
6. Anomaly detection — AI-fueled constructs identify unusual or unexpected patterns to restore missing data, correct errors, or evade throttling by target sites.
7. Access management — supplement of recurrent “request–answer” TCP connections, relying on:
a. Machine-learned detectors of entry limitations, CAPTCHA solvers, etc. Google reCAPTCHA challenges took more than 800 million hours from users around the globe without proven effectiveness. Instead of spending time on business tasks, personnel have been looking for hydrants and buses (check the “Dazed & Confused: A Large-Scale Real-World User Study of reCAPTCHAv2” for details.
b. Uninterrupted concurrent threading through a KYC-verified geo targeted proxies’ infrastructure — with IP rotation and targeting precision at ISP/carrier-levels.
8. Infrastructure maturity — Modern proxy stacks and AI–driven orchestration make large–scale, compliant data gathering accessible to any team

Old-school methods for gathering public internet insights are forced to navigate JS-heavy platforms and operate encrypted API calls. These technologies face anti-automation web shields (reCAPTCHA, Cloudflare, Akamai, fingerprint checks, etcetera), as cybersecurity Imperva experts believe 37% of global traffic to be the automated requests.

Scraping with AI, being a multi-layered, developed version of traditional info acquaintance, sidesteps its predecessor on corporate-grade levels.

Scraping with AI vs. Traditional web data collection

Data-driven decision-making increases productivity rate to 63%, while LLM technologies free maintenance teams, turning the scripted algorithms into self-healing projects. Pros and cons of non-AI and machine learning scraping approaches are shown in the table below.

Scraping pipeline’s maintenance consumes from 20% to 50% of working hours, while machine learning saves this time. Just as the backend community moved from managing servers to describing desired states in Kubernetes, the internet data obtaining is now transitioning from rule-based extraction to adaptive info acquisition. And the neural networking evolution covers all business spheres, suiting all use cases.

What are AI use cases: examples of applying LLM for scraping

Autonomous, intelligent info collecting streams have broad industry relevance. While the market assessments vary, the development of AI-based scraping solutions’ CAGR is estimated at 19.93% by 2034.

Supplementary techniques are developing at similar rates:

A pipeline needs to spread the load, create consistent digital fingerprints, access geo-targeted content, etc. That is why enterprises buy residential IPs, datacenter and 4G/5G/LTE addresses, developing the proxy market, size of which is estimated at $7.2 billion by 2031.
The Data-as-a-Service (DaaS) industry is also growing, with prospects to multiply seven times in ten years.

The most common use cases for ML-introduced info obtaining include:

*According to the Mordor Intelligence Report.

We mentioned the most trending use cases of LLM-linked data collection, because most businesses leverage scraping at various scales, from chatbots and search engines to brick-and-mortar shops.

Building the future: where to buy proxies for AI-enabled scraping

The path from traditional HTTP-based online info retrieving to AI-enabled scraping means more than a technological upgrade. We are witnessing a fundamental shift, where delegating routine tasks, such as finding and getting required information with further enrichment and patterning is only a step to comprehensive artificial superintelligence (AGI).

Generative artificial intelligence is becoming a full-cycle system, which finds, downloads, and processes the required web insights according to natural language prompts and maintains the pipeline’s elements. Finding ethical and reliable components and performing an initial setup is still a human role though.

https://www.youtube.com/watch?v=IuA38Ag73SI

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