Beyond Open Source vs. Proprietary: Crafting the Ideal Web Data Extraction Stack for Control and Scalability
How to Orchestrate Open Source and Proprietary Tools for Your Web Data Extraction Operations
Imagine you’re tasked with building a data extraction system for your company. You face a critical choice: should you go with open-source tools, where you can see and control every part of the code, or invest in proprietary solutions that "just work" out of the box? It’s a familiar dilemma — transparency and control vs. convenience and scalability.
But here’s the thing: when it comes to web data extraction, you don’t have to choose just one path.
Instead, there’s a “third way” — a mixed approach that combines the best of open source and proprietary tools, creating a tech stack that’s reliable, adaptable, and tailored to your unique needs. This approach lets you harness the transparency and flexibility of open source where control matters most, while leveraging the efficiency and managed infrastructure of proprietary tools where reliability and support are critical.
In this article, we’ll explore how to build a balanced web data extraction system using open-source and proprietary options. We’ll break down each part of the tech stack, showing how you can assemble a solution that balances cost, control, and scalability. Along the way, we’ll look at where different players in the industry position themselves on the open-source-to-proprietary spectrum and why this mix of technologies may be the most practical, resilient choice for data-driven organizations today.
Black boxes: we build them and we use them
Large language models (LLMs) show that we can build systems we don’t fully understand.
Like many neural network-based AI systems, LLMs are black boxes: they process vast data sets to generate responses, yet how they arrive at specific answers is not fully traceable—even to their creators. This seems unsettling, but it’s precisely what enables them to make connections that simpler, rule-based systems cannot.
Yet, ChatGPT reached 100 million monthly active users in just two months after launch, making it the fastest-growing consumer app in history. We’re clearly comfortable relying on technology we don’t fully understand.
Yes, the stakes are low when asking language models to help generate memorable wedding vows. But what about business-critical applications? How does this fit with the conventional wisdom in systems design, where eliminating single points of failure (SPoF) is essential?
In reality, we’re willing to trust black boxes when:
The perceived benefits outweigh potential risks.
We trust that designers have good intentions—or at least, no obviously and immediately harmful ones.
Every day, we engage with black boxes: navigation apps, medical diagnoses, unsecured websites, and algorithm-driven social feeds. Whether or not we realize it, we’re constantly weighing these trade-offs.
The truth is we juggle and decide on worthy trade-offs all the time.
Open source: using and maintaining
Open source is cheap to use but costly to maintain and customize.
You pay for the human ingenuity, expertise, and coordination on top in some shape or form. Proprietary solutions, on the other hand, come at a higher upfront cost but typically include maintenance and support, easing the operational burden.
Open source technologies aren’t just about visibility—they’re about empowering users with control. Users can inspect the code line-by-line, modify it to suit specific needs, and share improvements with a broader community.
This way of operating also strengthens security in a unique way. With a broad community constantly reviewing and refining the code, vulnerabilities are often identified and patched faster than in proprietary systems.
This community-driven model can make open source a compelling option if you want to reduce dependency on a single vendor and gain autonomy over your tools.
Depending on where you are in your web data extraction journey, standard schemas and generic crawling logic might not be sufficient. Many people’s data needs are personal, contextual, and fluid. You do need granular control in some areas.
So, how do you balance customizability, reliability, and cost efficiency? What’s the right mix of necessary magic, optimal control of business logic, and technical transparency?
Unbundling the web data extraction tech stack
Let’s begin by breaking down the typical components in a web data extraction tech stack and see what open-source or proprietary solutions typically offer for each.
As a general rule of thumb:
Prioritize open source for well-supported areas where flexibility is key.
Choose proprietary solutions for high-risk areas like unblocking website access, data storage, and monitoring, where compliance, support, and reliability are critical to your business goals.
Make sure you examine the modularity and interoperability of each component allowed by the vendor. Does the solution allow you to assemble and swap different components onto them?
What are my options?
Let’s look at key players in web scraping industry based on their licensing model, target audience, and unique features:
We see two players offering substantial open-source solutions: Zyte and Apify. Zyte displays fifteen years of continuous contributions to multiple data extraction repositories beyond Scrapy, while Apify is actively spreading the word for their homegrown NodeJS-based Crawlee.
The remaining key players such as Bright Data, Oxylabs, DataMiner, ParseHub, and Scrapehero largely emphasize their proprietary technologies, with minimal to no open-source contributions.
It’s precisely this range that enables organizations to find the best fit for their needs—whether they seek the adaptability of open-source or the convenience of proprietary tech—making it crucial to have both models coexisting in the industry.
Elephant in the room
Feels like these days no discussion in tech is complete without talking about AI. Where's the sweet spot for AI-powered tools? What about proprietary AI?
Unlike the fundamentally probabilistic nature of large language models (LLMs), the input and output of most web scraping APIs that are not built solely on top of LLMs, are deterministic.
It means that you get the same output every time given a set of input and, needless to say, the state of the webpage by the time you access it. You get back a body of text or image according to the input parameters you’ve set.
So, any intelligence that a web scraping API employs to unblock, parse, and extract, is less of a black box than any LLM is.
Zooming into Zyte, the composite AI underneath Zyte’s web scraping API, Zyte API, combines:
lean supervised machine learning models trained for structured data extraction for common data types,
small language models to refine and extend schemas (such as structured data points on a page specific to category), and
large language model to support extraction of unstructured data in multiple formats – unlocking valuable use cases unimaginable just 2 years ago.
This hybrid approach is how Zyte balances all three edges of the classic cost-quality-time triangle.
Does it matter that the AI models are proprietary? Not any more than the OS on your phone. Choosing proprietary optimisation tools is a wise choice as long as they yield consistent, replicable output. What matters most is trust in the results.
With Zyte's composite AI, the proprietary nature isn't about withholding control but providing stability and reliability. Zyte aims to maintain that sweet spot between transparency and performance, letting users understand enough to build confidence in the process without needing to manage the model specifics.
By employing the right AI models for different extraction use cases, Zyte ensures that AI enhances rather than obscures the scraping process, allowing businesses to make practical, risk-managed decisions about integrating intelligent automation.
As a concrete example, one core component in Zyte’s solution is a set of AI spiders.
But did you notice that underneath these “AI spiders” are actually a collection of open source spider templates with parameters that support complete customization? You can use the templates to jumpstart your project. These spiders provide a thin layer supporting component #1 and #2 in Table 1.
You don’t even have to use Zyte API to do the automated crawling, parsing, or extraction of data types you specify. You are free to deploy these spiders on any other cloud scraping service providers and stitch other solutions onto it if that makes sense for your business goals.
Build your own trusted system
With all the pieces at hand and a framework to guide your decision-making, you’re in a strong position to design different pieces in a data extraction system that fits your business goals while balancing cost, quality, and time. The key is to construct a system you trust.
Having both open-source and proprietary tools as options is not a bug but rather a feature.
Often, it’s a sense of control we crave—not to understand every detail, but to know that it will respond reliably in a way that we can expect.
And in the land of data, it’s interesting to think that data essentially provides the same psychological benefits. Data helps us control, understand, and predict.
The same way as good systems convert faith into trust, good data convert faith into trust.