Coupler.io Co-Founder Sergiy Korolov on why building everything yourself is a costly illusion
For the past year, a bold idea has been gaining traction across tech circles: artificial intelligence will replace SaaS (News - Alert). The logic seems straightforward. If AI can write code, automate workflows, and generate insights on demand, why pay for dozens of software tools? Why not build your own stack, tailored exactly to your business?
It is a compelling narrative. It is also, according to Coupler.io Co-Founder Sergiy Korolov, fundamentally flawed.
“AI can generate functionality,” Korolov says. “But SaaS was never just about functionality. It’s about everything that happens after the first version.”
That distinction is becoming increasingly important as companies rush to experiment with AI-generated tools, only to discover that building something is very different from operating it at scale. The real challenge is not creating software. It is maintaining it, securing it, integrating it, and ensuring that it performs reliably over time.
At first glance, the case for replacing SaaS with internally built tools seems logical. AI has dramatically lowered the barrier to entry. What once required months of engineering effort can now be done in days. But speed at the beginning does not eliminate complexity down the line. In many cases, it simply delays it.
Korolov has tested this firsthand. Using AI-driven “vibe coding,” he built a functioning product over a few weekends. Fast, impressive, and deceptively simple. But the takeaway was clear. It is much easier to create something new on a blank canvas than to add to an already complex system. Once a product moves beyond the prototype stage, complexity does not disappear. It compounds.
“People are optimizing for how fast they can build something,” Korolov explains. “They’re not thinking about how hard it is to maintain, scale, and secure it over time.”
Modern software is not defined by features alone. It is defined by uptime, infrastructure, compliance, integrations, and continuous iteration. When companies attempt to replace SaaS tools with internal builds, they are not removing those requirements. They are taking ownership of them.
“Who is responsible for keeping that system running 24/7?” Korolov asks. “Who handles outages? Who ensures compliance across regions? These are not one-time efforts. They are ongoing commitments.”
To make the point more tangible, Korolov offers a simple analogy. “Everyone can cook,” he says. “That doesn’t mean restaurants disappear.”
You can prepare a meal at home. You can follow a recipe and get it right most of the time. But restaurants exist because they manage the supply chain, clean the kitchen, and deliver the exact same quality 1,000 times a day without failing. Software operates under the same principle. Companies are not paying for code. They are paying for systems that are maintained, optimized, and continuously improved by dedicated teams.
That system includes everything from infrastructure to user experience to security, all delivered in a way that is predictable and reliable. This is where SaaS continues to hold a significant advantage.
Instead of one company bearing the full cost of development, maintenance, and infrastructure, SaaS providers distribute those costs across thousands of customers. The result is a level of efficiency and reliability that is difficult to replicate internally. Platforms like Figma, Ahrefs, PostHog, and 1Password are not just tools. They are the product of years of focused development, refinement, and specialization.
“These companies have spent years solving very specific problems,” Korolov says. “Replicating that internally is far more expensive than most teams realize.”
More importantly, it pulls attention away from what actually drives growth. Businesses that invest heavily in rebuilding existing tools often find themselves overextended, dedicating resources to infrastructure instead of innovation.
“As a business, your goal is not to rebuild tools that already exist,” Korolov says. “Your goal is to focus on what makes you different.”
He points to a growing wave of YouTube (News - Alert) videos claiming things like, “You can build Calendly in 15 minutes with AI.” Korolov is quick to challenge that narrative. Yes, you can build a clickable prototype in 15 minutes. But a product like Calendly is not defined by its interface. It is the result of years spent solving edge cases, managing infrastructure, and ensuring 24/7 reliability at scale.
That level of execution does not happen over a weekend. And it is not replaced by AI.
So, if AI is not replacing SaaS, where does it fit?
According to Korolov, AI is best understood as a layer that sits on top of existing systems, not a substitute for them. It improves how users interact with software, but it still depends on the underlying infrastructure to function effectively.
“AI makes software more accessible,” he says. “But it still relies on structured, reliable systems underneath.”
This is where many organizations encounter friction. Their data is spread across dozens of platforms, from CRMs and ad networks to analytics tools and financial systems. When AI is applied to that fragmented environment, the outputs it generates are often inconsistent or incomplete.
“You’re asking AI to make sense of disconnected data,” Korolov explains. “That’s why the results don’t match reality.”
This challenge points to a deeper issue. For most companies, the bottleneck is not the tools themselves. It is the data that connects them.
This is where Coupler.io plays a critical role. Rather than replacing SaaS tools, the platform connects them, pulling data from hundreds of sources and structuring it so that it becomes analysis-ready. Before data ever reaches a dashboard or an AI model, it has already been cleaned, organized, and standardized.
“Most companies don’t have a software problem,” Korolov says. “They have a data readiness problem.”
He knows this firsthand. Coupler.io was born out of the team’s own internal data failure, a spreadsheet error that cost $120,000. That experience exposed the risks of disconnected, unstructured data and ultimately led to the creation of a platform built to solve it.
At the center of the platform is Coupler.io’s Analytical Engine, which transforms raw data into structured, reliable datasets that can support accurate reporting and AI-driven insights. This step is critical, particularly as organizations increasingly rely on AI for decision-making.
“AI does not create insights,” Korolov says. “Clean, structured data does.”
Without that foundation, even the most advanced AI tools struggle to deliver reliable outputs. In that sense, AI does not replace SaaS. It amplifies the importance of having the right systems in place.
The idea that AI and SaaS are competing forces is, in Korolov’s view, a misunderstanding of how modern technology stacks are evolving. In reality, the two are deeply interconnected.
“AI increases the value of SaaS,” he says. “Because it increases the need for reliable data and stable systems.”
SaaS provides the infrastructure and execution layer. AI enhances how users interact with that infrastructure. One does not replace the other. They work together.
“AI is the interface,” Korolov says. “SaaS is the engine.”
For business leaders, the takeaway is less about technology trends and more about strategic focus. Building internal tools may feel empowering in the short term, but it often creates long-term drag. Engineering teams become responsible for maintaining systems that do not directly contribute to differentiation.
“The fastest way to slow your growth is to over-engineer your internal stack,” Korolov says.
A more effective approach is to rely on specialized platforms for infrastructure while focusing internal resources on areas that drive competitive advantage.
“Use the tools that already exist,” he says. “And invest your time in what actually moves your business forward.”
If anything, the rise of AI signals not the decline of SaaS, but its evolution. Software is becoming more interconnected, data is becoming more central, and the demand for real-time, reliable insights is increasing. All of these trends reinforce the importance of strong underlying systems.
“SaaS is not going away,” Korolov says. “It’s maturing and becoming more important.”
And in a market filled with noise about disruption and replacement, that perspective offers a more grounded reality.
“You don’t win by building everything yourself,” he says. “You win by building what matters, and relying on proven infrastructure for the rest.”
