The start of the year is the perfect time for New Year’s resolutions and web scraping industry reports. Thanks to Zyte, we had the opportunity to read in advance their view on the industry, and we’re gonna share with you some key elements from it. If you’d like to read it in full, you can find it here.
The document, titled “The age of fast-forward web data”, identifies six trends reshaping the industry in 2026. We went through the report and pulled out what matters for anyone working in data extraction.
The Market Has Exploded
The report opens with a number: the web scraping market reached $1.03 billion in 2025, with projections pointing to $2 billion by 2030 (some estimates double that figure). The majority of mid-to-large enterprises now use web scraping for competitive intelligence, and most e-commerce companies monitor competitor prices using scraped data.
Web scraping, in other words, is no longer a fringe practice. It has become critical economic infrastructure.
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Trend 1: Full-Stack APIs Replace Separate Components
The first trend concerns the end of standalone proxies. Zyte reports that the market now counts over 250 proxy vendors, with price wars that have eroded margins and turned proxies into commodities. The problem is that websites have evolved their defenses well beyond simple IP blocking: TLS fingerprinting, behavioral analysis, canvas fingerprinting, and JavaScript traps. Some systems claim 99.9% accuracy in distinguishing humans from bots through behavioral biometrics alone.
The market response, according to Zyte, is migration toward APIs that handle the entire stack transparently: proxy rotation, browser automation, unblocking, parsing, and retry logic. The cited figure: request volume through the Zyte API grew 130% year-over-year in 2025.
The trend is real, though it needs context. For those operating at a large scale with specific control requirements, direct component management remains relevant. What we find more interesting is the underlying shift: defense complexity has crossed the threshold of manual manageability for most use cases. Now more than before, it’s a buy vs make choice: you can always try (and probably should, at least to improve your skills in web scraping) to create your in-house solutions but the game is becoming so hard that the market is looking for all-in-one APIs.
The second trend describes AI integration across every link in the chain. The report cites a Technavio projection: the AI-based web scraping market will reach $3.16 billion by 2029, growing at 39.4% annually.
The concrete applications listed in the report cover the entire cycle: auto-classification of content for schema-specific extraction, LLM-powered extraction for unstructured data, automatic identification of selectors and field mappings, change detection, crawler code generation, browser interaction via natural language, data cleaning, anomaly detection, and real-time unblocking strategies.
The key distinction Zyte proposes: LLM extraction for low-volume projects with volatile sites (higher cost per request, but flexibility compensates), code generation for high-volume mission-critical projects (generated code can be tested, versioned, and costs less at scale).
The report also mentions computer-use models for multi-step navigation (forms, filters, gated screens). We think this is a rapidly evolving area worth watching closely, and we have talked about this trend in several other articles on these pages. LLMs are not a silver bullet for HTML parsing, but their use certainly improves the productivity of data acquisition teams, both when they need to write scrapers and when they need to check data quality.
The third trend is the most ambitious: end-to-end automation through agents. Zyte cites a Deloitte study showing 30% of organizations exploring agentic approaches, 38% piloting them, but only 11% with production deployments. The gap, according to Zyte, will narrow in 2026.
The proposed vision: a team specifies an outcome (dataset with schema, coverage targets, freshness, failure tolerance), an agent explores the site, discovers necessary actions, chooses the most efficient method, and when the site changes, the agent diagnoses the breakage, regenerates code, re-validates outputs, and escalates only when confidence drops below a threshold.
In practice, the report describes a multi-agent system: API discovery agents, schema-first extraction agents, self-healing testing agents, vision-based computer-use agents, DOM-native browser agents, and coding agents. Each agent handles one specific job, and an orchestrator supervises.
The vision is compelling on paper, but the report itself admits that production adoption is still limited. Zyte’s practical advice is telling: apply agents selectively. For stable, straightforward sources, a conventional setup remains more cost-effective and we could not agree more.
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Trend 4: The Arms Race Accelerates
The fourth trend is perhaps the most concrete. Anti-bot systems now reconfigure continuously, driven by ML models that adapt in minutes. The report cites Proxyway: “Two days of unblocking efforts used to give two weeks of access... now it’s the other way around.”
Zyte reports observing a major bot management vendor deploy over 25 version changes in 10 months, often releasing updates multiple times per week. Cloudflare, according to the report, has a near-real-time system that adapts its detection strategy every few minutes.
The factors amplifying the mismatch: ML-driven detection with polymorphic JavaScript, WASM obfuscation, RASP, passive fingerprinting at scale; detection mechanisms monitoring timing patterns, network-level anomalies, device fingerprint consistency, pointer curves, scroll variance; growing AI bot traffic volume pushing sites to respond with automatic tuning.
Zyte’s conclusion: manual access strategies are no longer sustainable at scale. Only automated, self-adjusting pipelines survive. We can also say that web scraping is becoming more expensive, and there should be a smarter way to do it. Our idea at Databoutique.com is to share scraping costs across multiple data buyers, and this can be a way to do so.
Trend 5: The Web Fragments Into Access Lanes
The fifth trend describes a trifurcation of the web from a bot access perspective.
The hostile web: sites deploying aggressive honeypot traps, AI-targeted challenge flows, sophisticated fingerprinting. Cloudflare has deployed AI Labyrinth, traps specifically designed for AI crawlers, claiming to have blocked 416 billion AI bot requests in six months.
The negotiated web: publishers adopting licensing, attestation, pay-per-crawl, paywalls. Standards like ai.txt, llms.txt, Really Simple Licensing (RSL) attempt to make permissions machine-readable. Adweek reports that 2026 will see LLM deals shift from one-time payments to usage-based revenue shares.
The invited web: sites exposing machine-first interfaces for approved actors. Shopify, Google, Visa, Stripe, OpenAI either support Model Context Protocol (MCP) or have launched proprietary protocols like Agentic Commerce Protocol (ACP), Universal Commerce Protocol, Trusted Agent Protocols.
The key point: identity becomes a first-class citizen. Initiatives like “Know Your Agent” are gaining traction. Verified or attested bots receive preferential routing, unverifiable bots face increasing friction.
This chapter describes attempts to make the web sustainable for content publishers via protocols that could reward them, but it’s still a long way to go
Trend 6: Regulatory Compliance Arrives
The sixth trend concerns the evolving legal landscape. Two relevant dates: California AB 2013 took effect January 1, 2026; the EU AI Act takes effect August 2, 2026.
California AB 2013 requires developers of publicly available generative AI systems to publish detailed documentation: data sources, dataset size, data types, whether copyrighted material is included, whether datasets were purchased or licensed, whether personal information is included, and data processing methods used.
The EU AI Act imposes transparency and other obligations based on risk to users. General-purpose AI providers must publish training dataset summaries and respect copyright holder opt-outs. Penalties: up to 35 million euros or 7% of global annual turnover.
On the litigation front, the report cites Bartz v. Anthropic (2025): training on legally obtained works is defensible, training on pirated content is not. In Kadrey v. Meta, market harm was a decisive factor.
Compliance, Zyte concludes, becomes an operational requirement. Enterprises will not adopt AI systems without evidence of lawful data sourcing. Provenance tracking becomes necessary for audits, investors, and enterprise customers.
Conclusions
We’re living in an unprecedented era: we have tools in our hands that are improving the efficiency of our work, and we’re still scratching the surface of what they can do. But the LLM training and Agents brought scraping operations to a new level, making anti-bot softwares are more important than ever and raising the bar for the scraping industry itself, which is responding with more advanced tools and APIs. For sure, we’ll have interesting times ahead.
This strongly matches what we are seeing at Kameleo as well. As Proxyway put it, “two days of unblocking efforts used to give two weeks of access... now it’s the other way around.” We aim to release new browser versions within 5 days of the official Chrome release, because even small version gaps are increasingly punished by modern anti-bot systems.
At the beginning of last year, this was relatively easy to achieve. Toward the end of the year, it increasingly required overtime from the team as detection models started adapting faster and with finer granularity.
Chroma 144 was a particularly tough milestone due to the introduction of the new “X-Browser-Validation” headers, which added an extra layer of browser authenticity checks. We wrote more about this here for anyone interested: https://kameleo.io/blog/chrome-144s-secret-handshake-with-google-services
This strongly matches what we are seeing at Kameleo as well. As Proxyway put it, “two days of unblocking efforts used to give two weeks of access... now it’s the other way around.” We aim to release new browser versions within 5 days of the official Chrome release, because even small version gaps are increasingly punished by modern anti-bot systems.
At the beginning of last year, this was relatively easy to achieve. Toward the end of the year, it increasingly required overtime from the team as detection models started adapting faster and with finer granularity.
Chroma 144 was a particularly tough milestone due to the introduction of the new “X-Browser-Validation” headers, which added an extra layer of browser authenticity checks. We wrote more about this here for anyone interested: https://kameleo.io/blog/chrome-144s-secret-handshake-with-google-services