In life, unlike in data, choices are rarely binary. Fortunately, the web is breaking out of its false dichotomy.
For years, business buyers have had two main options in the software marketplace:
Software as a service (SaaS): tools enabling customers to do a job for themselves.
Outsourced service: a hands-on, done-for-you solution performed by software experts, removing the burden of in-house labor.
In the web data extraction space, Zyte, too, offers both options:
Products (Zyte API, AI Scraping and Scrapy Cloud) for teams who want to build and run their own data extraction operations.
A managed data extraction service (Zyte Data), through which an expert team of data engineers builds and maintains spiders and data pipelines on customers’ behalf. We call this “Data as a Service” (DaaS).
The software industry has traditionally thought of these two categories - software and service - as discrete, believing that customers will only choose one or the other. But the reality is turning out to be very different.
Dawn of hybrid data ops
As someone who works daily with organizations navigating their data strategies, I have been witnessing a shift in how companies are approaching data acquisition – an evolution from “build or buy?” to “both”.
Zyte customers are increasingly taking a “hybrid” approach to data gathering.
For instance, over the last year, we have seen:
Project partners: An insurer using hands-on data acquisition products to prototype a risk assessment model, then outsourcing ongoing collection and maintenance to Zyte Data.
Sharing the load: A fintech firm leaning on Zyte Data for the build and setup of spiders and pipelines, before running them on its own infrastructure for critical compliance reasons.
Managing complexity: Customers building their own web extraction code for straightforward data sources but calling on Zyte Data to handle more complex targets.
Targeting by type: An apparel company gathering shipment tracking data by itself and grabbing competitor prices using pipelines built by Zyte Data.
Complementary content: A global intelligence provider leveraging Zyte’s tools by itself to auto-extract content like articles, while calling on Zyte Data’s team to gather harder-to-reach data.
Why hybrid models make sense
The emergence of hybrid DaaS/SaaS models is a logical response to the complex realities of modern data needs. Organizations are discovering that the most effective approach is to use the right tool for each specific job, rather than forcing all data requirements through a single channel.
“The main reasons we are seeing this hybrid approach are R&D, speed, edge use cases, and resource constraints,” Tiago Sampaio, enterprise account manager (US), Zyte, told me.
Businesses often use SaaS for internal control and speed, and DaaS when they hit scale, complexity, or compliance bottlenecks. From an operational perspective, hybrid data gathering offers complementarity and flexibility:
Teams can use SaaS tools for rapid prototyping, internal experimentation, and situations where they need complete control over data extraction timing and methodology.
Meanwhile, they can rely on managed data extraction services for complex, ongoing projects that require specialized expertise or sophisticated infrastructure.
A hybrid approach makes sense because, often, data-driven companies need to both run fast and lift heavy. Using both lets you move quickly where you can, and offload where you must.
We're seeing sophisticated organizations that historically would have been purely DaaS customers asking for SaaS capabilities to handle specific use cases. As my colleague Damon McKay, global sales manager, Zyte, told me: “For complex customers with a mixture of data needs, when they go down just a single route, it often isn’t the right approach.”
Implementation strategies
Successfully implementing a hybrid DaaS/SaaS strategy requires thoughtful planning and clear decision-making frameworks.
SaaS tools are optimal for data extraction use cases requiring iteration, control or integration with other systems.
DaaS solutions excel in situations requiring extra skills, ongoing maintenance, or access to data sources that demand significant investment.
The most successful transitions follow a phased approach. This allows teams to build internal expertise while ensuring that critical data flows remain stable and reliable.
The future of data strategy
Like the amalgamation of software and services in the tech landscape at large, the convergence of DaaS and SaaS signals the maturation of the data economy.
As organizations become more sophisticated in their data strategies, they are moving beyond simple either/or decisions toward nuanced approaches that optimize for specific business outcomes.
The companies that will thrive in this new environment are those that embrace flexibility and strategic thinking in their data acquisition strategies. Rather than being constrained by traditional categories, they'll choose the right tool for each job, building blended approaches that maximize both efficiency and effectiveness.
Sometimes, the answer to the question “Build or buy?” is “Both”.
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