The leading web data infrastructure company, Decodo, has launched a Web Data API that allows teams to scrape, search, crawl, and map the web with a single API key. Instead of stitching together separate tools, each with its own billing, documentation, and integration work, users can now handle every stage of web data collection in one place.
For AI teams feeding models and agents with fresh, up-to-date information, that consolidation could mean lower costs and a much faster setup.
The company is opening access with a free tier of up to 10K requests a month across all four capabilities. The company says the tier is meant to let a team test a real workload before committing to anything, rather than a trial capped too low to be useful.
What building it in-house really costs
Feeding a system with current web information usually takes several pieces: a scraper someone has to build and maintain, a way to retry blocked requests and fix the parser when a target site changes its layout, and ongoing subscriptions for proxies and search. The costs also land on several teams. Engineering does the maintenance, procurement negotiates and renews the vendor contracts, and the team that commissioned the project absorbs the overruns. Added together, that usually costs more in money and time than a single tool that covers all of it.
In-house scraping projects also tend to run long. An estimate of a few weeks routinely stretches into a quarter once a target site adds new anti-bot measures or a proxy provider raises prices at renewal, and by then the original budget no longer covers the work.
The Web Data API brings four capabilities into that single subscription. It pulls clean content from a single page even when a site actively works to block automated access, returns live search results in place of a static index, maps a domain's URLs in one call, and crawls a site by following its links. Used together, they let a team see a site's structure before deciding which pages to pull. Together they turn a page-by-page tool into something that can describe a whole site before a single page is pulled from it.
A single map call can surface up to 147K URLs, and a crawl job can pull as many as 10K pages, enough to cover most corporate sites and documentation hubs, and many product catalogs, in one pass instead of several smaller jobs pieced together by hand.
Output comes back in HTML, JSON, CSV, or AI-ready Markdown, which sounds like a minor detail until a team has to reformat the same results for three different downstream systems. An analyst loading results into a spreadsheet or a BI tool works directly from CSV, a data engineer writing into a warehouse takes JSON, and a team feeding an AI model works from Markdown, all from the same API by changing one parameter, rather than a separate conversion step tacked onto the end of the pipeline.
The underlying proxy network is not new. Decodo already runs a network of 125M+ residential, ISP, mobile, and datacenter IPs across 195+ locations. That same network now backs all four endpoints, which is what lets them reach sites with advanced anti-bot protection.
"Building and running proxy rotation, CAPTCHA handling, and anti-bot workarounds in-house usually costs two to three times what a team budgets for it up front, once you count the engineering hours spent debugging blocked requests instead of shipping features. A scraper can break overnight because a single target site changed its markup, and fixing it can eat a week of an engineer's time that was never in the original estimate. We built the Web Data API to carry that maintenance work ourselves, so it never lands on a customer's engineering team in the first place," says Vaidotas Juknys, CEO at Decodo.
Connect AI tools, agents, and workflows to live web data
AI teams need more than access to web data. They need an easy way to bring that data into the tools and workflows they already use.
The native MCP Server connects AI tools and agents such as Claude, Cursor, and Windsurf to live web data. An agent can request fresh information when it needs it, rather than relying solely on what it already knows.
For AI coding agents, Agent Skills provide ready-made scraping capabilities they can use to retrieve information from the web. Developers can also use the official TypeScript, Python, and Go SDKs to build scraping and proxy functionality directly into their own applications. A CLI provides another option for teams that prefer working from the terminal.
There are also integrations for popular AI and automation tools. LangChain can bring live web data into AI applications, while n8n lets teams add scraping to automated workflows. OpenClaw gives AI agents access to web search and scraping capabilities.
The result is a range of ways to bring fresh web data into an existing stack. An agent can fetch information on demand, a developer can build it directly into an application, or an automation can collect and pass data between different tools. Teams can choose the approach that fits their workflow without having to build a separate data collection layer for every new AI use case.
Built for teams of two and teams of two hundred
A small team without dedicated data engineers can start its first project on the free starter plan of the new Web Data API. There is no need to spend months building proxy rotation, retry logic, and anti-bot handling before getting reliable data. As the project grows, the same infrastructure can support much larger crawl and map jobs, backed by a proxy network covering 195+ locations.
Most teams fall somewhere in between. That is where having the same infrastructure at every stage becomes especially useful. A ten-person team might start with a handful of crawl jobs each week and eventually run hundreds every day. There is no need to switch vendors, rebuild an integration, or move to a completely different setup. Usage simply grows with the project, while billing adjusts accordingly.
That flexibility also matters when growth happens unexpectedly. A product launch, a new competitor to track, or a sudden research project can take a team from hundreds of requests a day to thousands within a week. Infrastructure built around fixed contracts and manually provisioned resources can make those changes difficult to absorb quickly.
With Decodo, the infrastructure is already there. The same endpoints support customers processing a few thousand pages a month and those processing millions. When usage spikes, teams scale without setting up anything new. The capacity is available through the same API and the same key they were already using.
Vaidotas Juknys added, "We built this for a two-person startup testing an idea over a weekend and for a data team pulling millions of pages a month, on the same infrastructure. The free tier is not a trial version of a smaller product. It is the same API a large customer runs on, just before the billing starts."
Where this fits inside a business
For analytics teams, the value is often in turning public web data into a source their models and dashboards can work with continuously. A retail analytics team, for example, can map a competitor’s catalog to understand its full offering, then re-crawl selected categories regularly to catch changes in prices, products, and availability. Once the fields are extracted, the data can feed into pricing models, dashboards, or internal analysis without someone having to collect and update spreadsheets by hand.
AI teams have a similar need. A company building an AI research tool can use search and scraping to bring fresh information into its pipelines, giving models access to current web data instead of relying entirely on static training data. A market intelligence system could combine search results, news, company pages, and other public sources to keep its dataset up to date as the market changes.
The same infrastructure can support more specialized workflows. An AI agent can search for relevant sources, retrieve the underlying pages, and use that information as part of a larger workflow. A research platform can re-run crawl jobs regularly across thousands of pages and turn them into structured data for analysts or models.
In each case, the data collection layer stays in the background. Analytics and AI teams can focus on what they do with the data rather than maintaining the infrastructure needed to collect it.
Trying it out
Testing the Web Data API does not require a credit card, and the free starter plan covers all four capabilities from the start. An analytics or AI team can begin with a few scrape and search requests, then add mapping and crawling as its data requirements grow. When usage reaches the point where a paid plan makes sense, the same API key and integration can stay in place.
For teams already working with multiple data sources and vendors, this provides a simpler way to expand their data pipelines. New use cases can be added through the same API instead of requiring a separate service, integration, and billing setup each time.
Users can get started with Decodo's Web Data API in a few minutes.