Decodo (Smartproxy) alternative for web scraping: 4 options compared [2026]. By ScrapeWise Team Decodo - the platform formerly known as Smartproxy - is a solid, mid-priced proxy-first option: residential, mobile, and datacenter pools plus a Site Unblocker and web scraping APIs. It's popular with teams that want proxies without Bright Data's enterprise weight or price. But "proxy-first" is also its boundary. You still write and maintain the scraping logic on top of the proxies, and if you need a source outside its API templates you're back to building. Teams look for alternatives when they want either a bigger pool, a different price point, or to stop operating scrapers altogether. Scrapewise compared Decodo against Bright Data, Oxylabs, Zyte, and ScrapeWise on what actually decides the switch. Which Decodo alternative is right for you? | Your situation | Best fit | | Need the largest proxy pool and unblocking scale | Bright Data | | Enterprise proxy + compliance focus | Oxylabs | | Want scraping-as-an-API with auto-extract | Zyte | | Don't want to run scrapers or proxies at all | ScrapeWise | | Cost is the trigger, still want to DIY | Zyte or ScrapeWise | Quick comparison. | Tool | Category | Proxy Pool | You Still Operate | Pricing | | Decodo | Proxy-first platform | Mid-large | Scraper logic | Per-GB / plan | | Bright Data | Proxy + data empire | Very large | Scraper logic | Per-GB / per-record | | Oxylabs | Enterprise proxy + APIs | Very large | Scraper logic | Enterprise | | Zyte | Scraping API | Managed | Some parsing | Per-request | | ScrapeWise | Managed data infra | Managed | Nothing | Pay-per-use | Bright Data vs Decodo. Best for: teams that hit Decodo's pool or unblocking limits at scale. Bright Data's residential and mobile pools are among the largest available, and its unlocker handles the hardest anti-bot targets. If you're outgrowing Decodo purely on scale or success rate, it's the natural step up - at a higher price and more product surface area. See its Bright Data alternative guide. Oxylabs vs Decodo. Best for: enterprises that want proxy scale with a compliance-first posture. Oxylabs is the enterprise heavyweight - large pools, scraper APIs, and a strong compliance story. It's a lateral-to-up move on cost from Decodo, so you'd choose it for scale and enterprise support rather than savings. Its Oxylabs alternative comparison has the detail. Zyte vs Decodo. Best for: teams that want scraping delivered as an API, not raw proxies. Zyte moves up the stack from proxies to a scraping API with automatic extraction, so you write less parsing code. If your friction is building scrapers rather than getting proxies, Zyte reduces more of the work than a proxy swap would. See the Zyte alternative guide. ScrapeWise vs Decodo. Best for: teams that want clean data without operating proxies or scrapers. Decodo gives you the raw access layer; you build everything on top. ScrapeWise is a managed data infrastructure - you name any URL and clean structured records land in your stack, with proxies, fingerprinting, and JavaScript rendering handled automatically. In internal testing (Apr 2026) it returned 97% SKU coverage with real-time (~2-minute) updates and 96% anti-bot success on Amazon EU, on pay-per-use pricing rather than per-GB. The difference from Decodo: no scraper to maintain and no source stuck outside a template. How to choose. * Is your bottleneck proxies or scrapers? Proxies | Bright Data/Oxylabs. Scrapers | Zyte or a managed feed. * Do you need to run infrastructure at all? If not, ScrapeWise removes both layers. * Is cost the trigger? Per-request (Zyte) or pay-per-use (ScrapeWise) can beat per-GB for spiky workloads. The bottom line. Decodo is a good mid-priced proxy platform, but it stops at the access layer. Go to Bright Data or Oxylabs for more pool and scale, Zyte for scraping-as-an-API, or ScrapeWise if you'd rather skip proxies and scrapers entirely and just receive clean data. Paste a URL your current tool cannot reach. See why teams switch to ScrapeWise. 97% accuracy benchmark, no per-SKU pricing. 97% accuracy on Amazon benchmarks · no credit card · book a 15-min call

Oxylabs, a Lithuania-based data infrastructure platform, has secured a $130 million investment from Warburg Pincus, valuing the company at approximately $3.6 billion. This marks Oxylabs' first external funding since its founding in 2015. The investment comes from Warburg Pincus Capital Solutions Founders Fund. Oxylabs plans to use the capital to extend its competitive advantage and accelerate development of next-generation products and capabilities for AI and agentic applications. Oxylabs provides web intelligence infrastructure that serves over 350,000 customers across cybersecurity, e-commerce, and financial industries. The company has evolved from a premium proxy service into a full-stack web-data infrastructure platform. Warburg Pincus Capital Solutions Founders Fund closed in September 2024 with over $4 billion in commitments. Goldman Sachs Bank Europe served as sole placement agent for the transaction.

项目 4β 的影响:factcheck.lt 如何构建人工智能平台以监控东欧的虚假信息. 2026-06-30 Using Oxylabs' Web Scraper API provided through Project 4β, FactCheck built a systematic data-collection pipeline to gather publicly available content from state-owned and independent media websites, video data, search data, and social media. FactCheck.LT, a Vilnius-based research organization focused on media analysis, fact-checking, and countering disinformation, has developed FORESIGHT - an AI-powered multi-agent platform for geopolitical analysis and disinformation detection. As a long-standing partner of Oxylabs' pro bono initiative Project 4β, the organization monitors one of Europe's most heavily controlled information spaces: the Belarusian media ecosystem. Using Oxylabs' Web Scraper API provided through Project 4β, FactCheck.LT built a systematic data-collection pipeline to gather publicly available content from state-owned and independent media websites, video data, search data, and publicly available data on social media. The platform now contains ~ 3 million documents from more than 700 sources, one of the most comprehensive datasets on the Belarusian information space. Monitoring the Belarusian information space is not a simple research task. It requires following a fragmented ecosystem shaped by censorship, exile, platform migration, and state-controlled messaging. After the events of 2020, many independent Belarusian media outlets were forced to relocate outside the country. At the same time, state-owned media retained institutional backing and expanded their digital presence across social media platforms. The result was a scattered yet highly active information environment spanning websites, channels, accounts, and formats. For FactCheck.LT, the challenge was not only to follow individual stories but to understand how narratives are built, repeated, adapted, and amplified across the wider ecosystem. That meant monitoring dozens of outlets, hundreds of video platform channels, and large volumes of short-form and social content over extended periods. Manual collection was insufficient to support that kind of work, and basic scraping tools were unreliable as well. Its biggest technical challenge was reliably collecting publicly available web content at scale. Many of the sources Oxylabs monitored involved dynamic, JavaScript-rendered content or required a more stable collection setup than standard tools could provide. Before Oxylabs, its data collection was inconsistent, slow, and incomplete. Mikhail Doroshevich, Editor-in-Chief and Co-founder of FactCheck.LT The limitations were practical as much as technical. Consistently collecting public web data across different sources was difficult, especially when some pages relied on dynamic content rendering. Public video transcripts, an important source for analyzing broadcast messaging and propaganda narratives, were also not always readily available through standard APIs. For a small organization, building and maintaining custom collection workflows would have taken valuable time away from the research itself. The web intelligence behind the research. Through Project 4β, FactCheck.LT integrated Oxylabs' Web Scraper API into its data collection workflow and built a more stable foundation for long-term research. This support enabled the team to collect and structure publicly available web data from several parts of the information ecosystem, including: * news articles from state and independent media outlets * social media video transcripts from state television channels * public social media profiles and posts from platforms * search engine data used to contextualize shifts in public attention This breadth was essential to the work. Looking at a single article or account in isolation rarely reveals how information campaigns operate. What matters is the ability to compare coverage, detect repetition, identify timing patterns, and place sudden narrative shifts into a broader historical context. With a reliable data collection layer in place, Oxylabs could redirect effort toward what actually matters: analysis, publication, and impact. The difference is between spending days manually collecting data and having a systematic pipeline that automatically feeds its analytical platform. Oxylabs essentially removed the data collection bottleneck. Mikalai Kvantaliani, Co-founder and Project Manager at FactCheck.LT Built on top of that data pipeline, FORESIGHT supports multiple types of analysis simultaneously. The platform tracks how propaganda themes rise and fall over time, compares how different media groups cover the same events, detects signs of synchronized behavior across platforms, and supports forecasting based on historical patterns. What the research made possible. With a more dependable collection pipeline in place, FactCheck was able to move from fragmented monitoring to systematic analysis. The scale alone changed what became possible. The organization built a corpus of ~3 million documents across 700+ sources and expanded video platform monitoring to roughly 1,000 channels. Automated transcript analysis enabled examination of how video messaging was framed, repeated, and adapted over time. That broader visibility helped reveal not only what was being said, but also how messaging changed over time and how narratives were coordinated across formats. One example was the way state media framed relations with the European Union. FactCheck identified a shift between rhetoric favoring European integration and messaging emphasizing an Eastern turn, pointing to a pattern of strategic ambiguity rather than a fixed line. This kind of analysis is difficult to produce without both historical depth and large-scale cross-source comparison. The platform also supported cross-platform coordination analysis. In one social media platform-related study, FactCheck identified highly synchronized, providing strong evidence of coordinated inauthentic activity affecting several countries, including Poland, Moldova, Romania, and Hungary. Key findings. | Metric | Result | | Corpus scale | ~3 million documents across 700+ sources | | Video platform monitoring | Around 1,000 channels with automated transcript analysis | | Coordination detection | Statistical evidence of coordinated behavior at P<10[-80] | | Data collection reliability | Improved from roughly 40% with manual tools to 98%+ in one monitored workflow | Disinformation research often receives attention at the point of publication, when a report is released, or a network is exposed. Less visible is the infrastructure that makes those findings possible in the first place. Investigating disinformation requires more than identifying individual examples. It depends on being able to follow patterns over time, compare sources, and detect coordination across platforms. That kind of work is only possible when researchers have reliable access to public web data. Denas Grybauskas, Chief Governance and Strategy Officer of Oxylabs In environments like Belarus, where state messaging is systematic and cross-border influence activity affects neighboring EU countries, researchers need more than strong analytical frameworks. They need dependable access to public data. Without that, investigations become partial, slow, and difficult to scale. That is why this case matters beyond a single organization. FactCheck.LT's work shows what becomes possible when civil society researchers have access to infrastructure that is usually out of reach for smaller teams. Instead of spending limited time troubleshooting blockers and rebuilding collectors, they can focus on interpretation, publication, and public-interest impact. The results have already extended beyond internal research. FactCheck.LT's findings have informed diplomatic briefings across the Baltic and Eastern European region, supported journalism, contributed to broader discussions around foreign information manipulation and interference, and helped train practitioners working in AI and digital security. For civil society organizations, investigative journalists, and academic researchers working on countering disinformation, reliable data collection is not a luxury. It is a prerequisite. Project 4β makes professional-grade infrastructure accessible to organizations doing critical work on limited budgets. Mikhail Doroshevich, Editor-in-Chief and Co-founder, FactCheck.LT This case highlights the role reliable data collection plays in serious disinformation research. Through Project 4β, Oxylabs supports organizations working to monitor information threats, strengthen independent research, and turn public web data into meaningful analysis. To learn more, contact the team via the form or at [email protected]. Narmin是Oxylabs旗下Project 4β的公关内容经理.她乐于接受挑战,努力唤起人们的关注,而公益工作也让她积累了许多精彩的故事.闲暇时,她尽可能地旅行,或者沉浸在诗歌和朗诵的乐趣中. Oxylabs博客上的所有信息均按"原样"提供,仅供参考.对于您使用Oxylabs博客或任何可能链接的第三方网站上的任何信息,我们不作任何陈述,亦不承担任何责任.在进行任何类型的网络抓取活动之前,您应咨询您的法律顾问,并仔细阅读相关网站的服务条款,或获取网络抓取许可.