The 2025 Web Scraping Industry Report - For Business Leaders

• For Business Leaders: Buy or Build

• 1. Buying Data is Getting Cheaper and Easier

• 2. What AI and LLMs Unlock for Data Projects

• 3. Hybrid Models: Blending Open Source and Proprietary

• So, Should You Build or Buy?

• A Word on Compliance

• Things to Remember

• Conclusion

Web scraping can feel like navigating a minefield, with websites throwing up roadblocks at every turn—bans, rate limiting, dynamic content that changes elements each time you load the page. It can be extremely frustrating when extracting data. Thankfully, overcoming these challenges is possible with some foreknowledge of bans and the right strategies, which we’ll explore below.

Whether you’re already using web data to drive decisions or just starting to explore its potential, you’ll find yourself asking:

• Should we build our own data collection solution or buy off-the-shelf datasets? 

• Should we go single vendor or multi-vendor? 

• Which use cases first? 

• On whose budget? 

• Is it ok to scrape any and all public data? Is it ok to scrape public personal data? 

• Can I use copyrighted data for my use case? 

And perhaps most recently, what data opportunities and use cases have the AI boom unlocked?

The appetite for data has surged once again, echoing the big data boom of a decade ago. The motivations remain strikingly similar: to gain competitive intelligence, drive growth, reduce uncertainty, and transform raw information into actionable insights.

At Zyte, we see that the buying market has expanded. However, this growth isn’t simply about businesses with in-house data solutions throwing in the towel and saying, "We’re done maintaining our pipelines—take over." Instead, more organisations are entering the data space, testing the waters with low-risk purchases of one-time or off-the-shelf datasets. Once they’ve demonstrated the business value, they scale up with customised schemas and frequency to meet their growing needs.

We see three key factors influencing business leaders in 2025.

1. Buying Data is Getting Cheaper and Easier

In the past, companies may have decided to build their own data infrastructure because:

• Lower unit costs: Once the initial investment was made, scaling internally often appeared cheaper per unit of data collected.

• Control: Building in-house allowed businesses to craft solutions perfectly tailored to their needs. Full ownership over the pipeline gave companies control over quality, compliance, and workflows.

• Limited external options: specific datasets needed by companies were often unavailable off-the-shelf, either due to their niche nature or the lack of established data marketplaces.

But, the economics of buying data is finally catching up with building your own data stack.

Two key factors are driving this shift.

First, the rise of data marketplaces like Databricks marketplace, AWS Data Exchange, Datarade, and Databoutique have lowered the barrier to entry and let data buyers purchase data as needed. Providers simplify data access by handling extraction, cleaning, and delivery, allowing data buyers to bypass infrastructure challenges, reduce upfront data acquisition costs, and increase access to varied datasets.

Second, technological breakthroughs such as AI have also reshaped the playing field. Data vendors that have integrated AI capabilities into their crawling and parsing technologies should now be able to deliver solutions at a fraction of the time and cost. Here’s how:

• AI-driven efficiency: AI dramatically speeds up tasks like crawler development. For example, Zyte can now deliver e-commerce product data 3x faster with the leanest tech. This efficiency translates into lower costs for customers and allows Zyte to eliminate setup costs for data projects.

• Economies of scale: Companies like Zyte handle massive volumes of data across clients, reducing per-unit costs for buyers.

• Focus on core business: Outsourcing allows businesses to focus on their core operations without being bogged down by the complexities of data acquisition.

You might be wondering, “If Zyte is leveraging AI, why can’t we use the same tools to achieve similar results in-house?” Great question, and the short answer is: yes you can. We will delve deeper into this in an article on Zyte’s blog. Subscribe now to be notified.

We now have systems that understand us and can help us understand.

Large language models like Open AI’s GPT are making higher-order insights accessible through natural-language interfaces. Just three years ago, not many media monitoring companies had access to the machine learning engine and infrastructure to conduct high quality machine-driven sentiment analysis, especially for non-English languages. Now, imagine being able to redirect those valuable human brainpower to tasks that truly require creativity and ingenuity, while AI handles the data analysis at scale.

We’re only scratching the surface of what machine learning and generative AI can unlock for extracting beyond structured data.

Customers often turn to Zyte for use cases like parsing messy, unstructured documents—particularly PDFs—or extracting data from large-scale broad crawls. Once requiring significant engineering time and effort, these projects are now achievable in a fraction of the time, provided the data meets compliance and legal standards. This unlocks new efficiencies and capabilities, driving meaningful business value for both parties.

So put on your experiment hat and revisit that wish list of project ideas you once shelved due to budget constraints. Take those ideas to your vendors and explore what’s possible now—you might be surprised by how much has changed. And if you aren’t seeing the reduced cost when you talk to a vendor, double click on how they’re extracting data and challenge their pitch.

The age-old debate of open source versus proprietary solutions is evolving. A hybrid model often provides the best of both worlds—open source tools for flexibility and control, paired with proprietary solutions for scalability and support. This hybrid model offers business leaders agility without sacrificing dependability.

If you are interested in learning more on how to approach mixing open source and proprietary tools for your web data extraction operations, we shared a couple of guidelines in this article.

Before we move onto the list of things worth watching out for, we need to address the big question: how to decide if you should build or buy?

The short answer is: it depends on your stage in the data journey.

The decision to build your own web data extraction infrastructure or buy the data feeds from a data vendor is very contextual. Where you are in your data journey makes all the difference.

The Data Buying Journey

Based on our experience, data buyers and consumers typically move through three stages: Exploring, Discovering, and Defined.

Here’s a guide on how to identify where you are:

So, which approach works best for you?

Buy or Build: A Quick Cheat Sheet

• Exploring: Buy off-the-shelf datasets or find a partner that specializes in fast, low-risk PoCs.

• Discovering: Build for maximal flexibility or find a partner with the expertise that allows for rapid iteration at a reasonable cost.

• Defined: Buy to scale reliably, unless web data expertise is a core asset to your business.

If you are interested in the longer answer to this question, make sure to subscribe to Zyte’s free Extract Data newsletter on Substack where we will unpack this question in an in-depth guide.

What to Watch Out For

• Hidden costs: While the upfront cost of buying data might seem attractive, hidden expenses—such as integration, cleansing, or reformatting—can add up.

• Vendor lock-in: Subscription models might lock buyers into long-term commitments, even if the data no longer meets their needs.

Cost trade-offs: Open-source tools may appear cost-effective initially but often require investment in maintenance and development. Proprietary solutions, while easier to deploy, can carry higher licensing fees. Tip: Consider the total cost of ownership (TCO) for the hybrid model, including maintenance and support costs.

The main ethical pitfalls of buying data without understanding its origin or legality relate to three things:

1. Firstly, you want to ensure you are only collecting public data — unless you have a lawful reason to access non-public data.

2. Second, understanding whether the data you are scraping is copyrighted, and if it is, that your use is not a violation of copyright laws.

Lastly, determine if the data you are collecting includes personal data, which data protection laws apply, and ensure your use is compliant with the applicable laws.

• Dust off and revisit that wish list of data project ideas you once shelved due to budget constraints.

• Factor in total cost of ownership (TCO), including setup, maintenance, and vendor support costs, before committing. Use clear metrics to evaluate whether web scraping is delivering the value that aligns with your business goals. ROI isn't just about financial returns—it’s also about time saved and risks mitigated.

• Partner with vendors who are transparent about their data sourcing methods and adhere to compliance frameworks, avoiding reputational and financial risks.

As we look ahead to 2025, the web scraping ecosystem is evolving in ways that demand sharper strategies and more nuanced decision-making from all players. From data engineers and developers to data buyers and industry players, the challenges and opportunities differ, but the solutions all hinge on keeping your ears to the ground and attaching yourself to what’s constant: change itself.

For data engineers and developers, the tools have never been more accessible, with low-code and AI-powered options lowering the barriers to entry. But, as this report explores, scraping is no longer the primary hurdle—scaling is. The operational realities require more than technical skills; they call for an appetite for experimentation and adaptability.

For business leaders and data buyers, the debate around building versus buying data solutions has evolved. With AI-enhanced efficiencies driving down costs, the focus is shifting from what to outsource to how to validate ROI from purchased data. This report underscores the importance of understanding how your stage in the data journey—exploration, discovery, or definition—guides the best course of action.

For industry players, the stakes are higher than ever. Increasingly sophisticated anti-bot technologies, rising legal scrutiny, and market saturation create a dynamic where success isn’t just about technical innovation—it’s about earning trust. By balancing ethical compliance with thoughtful operational excellence in the age of AI, providers can carve out a sustainable path in an increasingly complex environment.

What unites all these profiles is the growing importance of asking better questions. How do we turn automation into meaningful productivity gains? How do we balance the tension between access and ethics? And how do we ensure that data, no matter how it’s sourced, delivers real, tangible value? The answers won’t come easily, but engaging with these challenges thoughtfully will define who thrives in 2025’s web scraping landscape.

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