Best No-Code Web Scraping Tools in 2026: Browse AI vs Proxy Infrastructure

Most guides to no-code web scraping list ten near-identical point-and-click tools and call it a day. This one takes a different approach, because the real decision in 2026 is not between ten clones; it is between two layers of the scraping stack. Browse AI is the point-and-click layer: you train a robot by clicking the data you want, and it extracts and monitors on a schedule with no code at all. Thordata is the infrastructure layer: a residential proxy network with scraping APIs that solves the problem no point-and-click tool fully escapes, which is getting blocked at scale. Both earn places in our catalog, both come from the catalogue's full listings, and the most interesting question is not which is better but which layer your problem actually lives in, and when you need both.

How we picked

Each tool has its own standalone listing, and we judged them against the job no-code scraping actually serves: getting reliable, structured web data into the hands of people who do not write scrapers for a living. The criteria were time to first useful dataset, resilience when target sites change or push back, output options for getting data into spreadsheets and downstream tools, and honest ceilings; every scraping product has targets it cannot crack and volumes where its model stops making sense, and pretending otherwise produces bad recommendations. We also deliberately picked tools from different layers of the stack rather than three interchangeable robot builders, because that is how real scraping setups are assembled.

1. Browse AI: best for point-and-click extraction and monitoring

Browse AI is built on the observation that most people who need web data are not engineers: e-commerce managers tracking competitor listings, recruiters watching job boards, real estate professionals following listings, analysts assembling datasets. You open a target site inside Browse AI, click the elements you want, and the platform generalizes those clicks into a reusable robot that handles pagination, scrolling, and dynamic content, then runs hourly, daily, or weekly. Two robot types cover the main jobs: extraction robots pull structured rows, while monitoring robots watch pages and alert you on changes; price drops, stock changes, new listings. A catalog of more than 250 prebuilt robots covers common sites such as Amazon, eBay, and LinkedIn, so frequent use cases need no setup at all.

The differentiator is self-healing extraction. When a site changes its layout (the event that silently breaks traditional selector-based scrapers), Browse AI attempts to detect the change and re-locate the data automatically. Combined with native Google Sheets and Airtable connections, webhooks, a REST API, and Zapier or Make integrations reaching thousands of downstream apps, it turns scraping into something a business team can own end to end. The free tier lets you extract from a first site and validate the workflow before paying anything.

The honest limits: credit-based plans cap how much you can extract per month, so very large volumes are better served by infrastructure-level tools. Heavily protected sites with aggressive anti-bot measures can still defeat robots, and complex conditional, multi-step workflows hit a ceiling that code-based scrapers do not have. This is a productized scraper, not a programmable one. Read the full Browse AI review.

2. Thordata: best for proxy infrastructure when scraping at scale

Thordata solves the problem that sits underneath every scraping project that grows: websites block traffic they recognize as automated, and the standard answer is routing requests through residential IPs that look like real users. Thordata's network is advertised at tens of millions of residential IPs across 190+ locations, with targeting down to country, state, city, and ASN level, plus ISP proxies with unlimited-bandwidth options for sustained transfers, datacenter proxies for cost-sensitive volume, and mobile proxies for app-specific data. Above the proxy layer sits a scraping stack that mirrors the market leaders feature for feature: a SERP API returning structured search results, a Web Scraper API with more than 120 prebuilt scrapers, a Web Unlocker that handles anti-bot challenges and CAPTCHAs, and a Scraping Browser for fully rendered, scripted sessions.

Calling Thordata no-code is only half true, and that is worth being honest about. The prebuilt scraper and SERP APIs return structured output without you maintaining parsers, which lowers the bar considerably, but the platform is aimed at developers and data teams, with docs and examples in Python, Node.js, Go, PHP, Java, and cURL. Where it shines is scale economics: teams that find incumbent providers expensive at volume get a feature-complete challenger at value pricing tiers of the market.

The tradeoffs come from its age. It is a younger network with less independent benchmarking than Bright Data or Oxylabs, documentation and tooling are thinner, success rates can vary on heavily protected targets, and there is no permanent free tier on the proxy products, though the SERP, scraper, and unlocker APIs offer free trials. The sensible adoption path is to trial the APIs against your actual targets and scale only if the success rates hold. Read the full Thordata review.

When to combine point-and-click with proxy infrastructure

The two tools are not really rivals; they intersect at the point where a no-code project outgrows itself. A typical arc looks like this: a team starts with Browse AI because the people who need the data are operators, not engineers. Robots run for months, monitoring competitors and feeding spreadsheets. Then the project grows (more sites, more pages, higher frequency) and two pressures appear at once: monthly credit caps start to pinch, and harder targets begin blocking the robots. That is the moment to split the workload rather than abandon the no-code layer. Keep Browse AI for the monitoring and the sites where it works reliably, because nothing beats its setup speed and self-healing for business-owned scrapers. Move the high-volume or heavily protected targets to Thordata's Web Scraper API or Web Unlocker, where residential rotation and anti-bot handling are the whole product, and pipe both outputs into the same spreadsheet or warehouse. Teams with even one person comfortable with Python or Node.js can run this hybrid without an engineering project, since Thordata's prebuilt scrapers return structured data and Browse AI's webhooks and integrations were built for exactly this kind of plumbing.

Which one should you pick?

Start from who will operate the scraper and how hard the targets fight back. If the operators are non-developers and the job is monitoring or moderate-volume extraction from cooperative sites, pick Browse AI: it is the best-executed product in the no-code scraping category, the free tier makes validation risk-free, and the self-healing robots remove the most common failure mode of DIY scraping. If the job is large-scale public data collection (SERP tracking, price monitoring across thousands of pages, training-data gathering) and someone on the team can call an API, pick Thordata, trial its APIs against your real targets first, and treat its shorter track record as a reason to verify rather than a reason to dismiss. And if you are somewhere in between, the hybrid is not a compromise but the architecture most growing data operations end up with anyway: point-and-click where speed of setup wins, proxy infrastructure where scale and blocking decide the outcome.