Open source software demands rigorous implementation and operational responsibility
Open source software keeps the engine of innovation running. No question about its value. It gives developers the freedom to build, adapt, and move fast. That’s exactly why it powers large portions of global infrastructure, from web frameworks to critical financial systems. But it’s not free in the way many imagine. Getting an open source project into production means serious engineering effort. It’s not grab-and-go. It takes time, testing, and internal expertise. That’s the reality.
Saju Pillai, SVP of Engineering at Kong, said it clearly: “It takes a lot of energy to pull an open-source project off the shelf and run your production workloads on it.” He’s right. The minute you make that project part of your core systems, you own it, from bug fixes to security patches. And if your team misses something, your users pay the price. That’s not just technical debt; that’s reputational and operational risk. Especially when your software touches industries like healthcare, finance, or public infrastructure.
From an executive standpoint, the draw is clear: innovation at reduced licensing cost, rapid adaptability, open development cycles. But the hidden weight is in owning stability, reliability, and long-term sustainability. Open source doesn’t come with a service-level agreement or 24/7 on-call engineers unless you build or buy it. That’s the trade-off.
This doesn’t mean avoid open source. It means respect it. Back it with good engineering. Audit it. Test it. Treat it like any other mission-critical responsibility. Because once open source becomes a core dependency, it becomes your product whether you wrote it or not.
Regulated industries encounter unique challenges in implementing open source solutions
If you’re running a regulated business, banks, insurance, telecom, open source won’t be plug-and-play. You’ve got auditors, compliance teams, legal departments, and cybersecurity standards to answer to. And every new line of code in production has to meet stringent guidelines. So while the flexibility of open source looks great on paper, it comes with real implementation friction in these sectors.
Shubham Agnihotri, Chief Manager for Generative AI at IDFC First Bank, put this into context. He talked about the “compliance and security challenges” that make it hard for regulated industries to deploy open source tools without heavy internal work. His message was simple: open source is powerful, yes, but it’s not ready out of the box for highly regulated environments. Developers in these spaces spend a lot of time customizing and securing before launch.
This is where leadership choices matter. You can opt for the speed and cost-effectiveness of open source, but if you’re in a regulatory environment, get ready for more upfront work and ongoing oversight. The legal exposure and data sensitivity in these sectors raise the bar. And skipping over that preparation phase isn’t an option if you care about long-term product viability and staying compliant.
There’s another angle to consider: time-to-market versus trust. A product that’s built fast but fails a compliance audit loses market trust, something a security-conscious customer won’t forgive. So if your industry depends on regulatory trust, then your open source strategy has to be grounded in risk control, review processes, and secure-by-design principles. That’s non-negotiable.
The difference between community-driven projects and commercially supported products
Understanding open source means knowing what you’re really adopting. You’re not buying a product, you’re integrating a project. It might look polished, might have traction, but unless it’s been commercialized and backed with support, you’re the one accountable for performance, uptime, and security.
Saju Pillai from Kong explained it in straightforward terms: when you’re looking at open source, you’re dealing with a project. Closed-source software, on the other hand, is a product, with warranties, security checks, and long-term support. That’s a key distinction. One model gives you flexibility and freedom; the other gives you predictability and support. You can’t confuse one for the other, especially when making enterprise decisions.
C-level executives need to consider where they want responsibility to sit. If the open source option is strong and your internal team is capable, then building on top of a project is viable. But if your resources are limited, or if you’re operating in a high-stakes environment that demands guaranteed uptime, you want the kind of backing that only comes with a commercial product, whether that’s technically open source or not. A lot of companies are now offering these hybrid models.
The decision isn’t binary, it’s about matching the right model to your risk tolerance and internal capability. A commercially supported open source product may offer the balance you need: innovation without having to shoulder 100% of the operational burden.
Autonomy and control are key driving factors
Control is a major reason companies are adopting open source. When you’re using proprietary software, you’re tied to vendor timelines and bug fix schedules. If something breaks, you wait. And that kind of dependency doesn’t sit well with teams that need to move fast, especially when they’re building and shipping constantly.
Sunny Bains, Chief Architect at PingCap, emphasized that point: companies want the ability to fix issues right away. They don’t want to wait for the next release or put in a ticket that might take weeks to resolve. This mindset is a big part of what inspired the free software movement in the first place, being able to make changes on your own terms.
But the freedom to change the code also means you’re responsible for what happens next. If something goes wrong after you tweak the system, it’s yours to fix. And if your team misses critical updates, especially security patches, you’re the one exposed. So while the autonomy is valuable, it needs to be backed by real ownership and technical strength.
For leadership, this means understanding where the trade-offs are. You avoid vendor lock-in, but you assume greater technical accountability. If you can build internal capability and governance around that, you gain speed and flexibility. If not, those risks compound fast, especially at scale or across geographically distributed teams.
There’s no universal answer. The right call depends on how much risk your team can absorb and how confident you are in their ability to manage complex systems without external support.
Reliability and robust security involves adopting multilayered assurance practices
Running open source software in production isn’t hands-off. If you miss anything, scalability bottlenecks, integration errors, latency risks, you pay for it in downtime or security exposure. And that cost can scale quickly. Reliability doesn’t come from open source by default. You have to engineer for it.
According to Harpreet Singh, CTO at Watermelon Software, organizations can’t skip layers of assurance. Integration testing, load validation, reliability monitoring, these aren’t optional. And for enterprises that rely on distributed systems or real-time access, missing just one layer can create a real vulnerability. Singh put it simply: “If you miss even one game, you’re open to threats and other implications.”
That means security protocols, resilience checks, and performance validation need to be part of the build process from the start. Think stress tests, rollback strategies, and proactive observability. If your teams aren’t building with those measures in place, you don’t have enterprise-grade software, regardless of how good the open source foundation is.
For executives, the key is to enforce operational discipline across engineering teams. Open source can give you a strong baseline, but the responsibility for performance, uptime and safety sits with your organization. Without multilayered assurance, even the best open source frameworks will expose your business to risk, reputational, operational, and financial.
Open source licensing complexities require strategic management and flexibility
Open source licensing is often misunderstood or overlooked, until it becomes a problem. Licenses vary, and they change over time. What’s permissive today could be restrictive tomorrow. If your teams aren’t keeping track, you might end up with code that violates legal or commercial terms. That blocks upgrades and, in some cases, forces product rewrites.
Harpreet Singh, CTO at Watermelon Software, described how a licensing change in a core open source component forced his team to revert to an earlier version of the software. That set back their progress. And it’s not a rare situation. Saju Pillai from Kong shared that when a developer at Kong adds a new library, the associated license goes through legal and compliance review before it’s approved for use. Nothing moves forward until it’s cleared. That’s process-driven discipline, a necessity at scale.
Sunny Bains, Chief Architect at PingCap, fully acknowledged the risk, referring to open source licensing as a “minefield.” To manage that complexity and give customers license certainty, PingCap donated its core scalable storage tech to the Cloud Native Computing Foundation (CNCF). That wasn’t just a goodwill gesture, it was a strategic move to ensure future-proof licensing control for the ecosystem.
For executive teams, this is about visibility and process. Licensing governance isn’t just a developer-side responsibility. You need embedded workflows that track dependencies and vet them through legal teams before production release. It’s also about system architecture. Your core platform must be agile enough to handle licensing transitions without hard downtime or painful rewrites.
Managing licensing risk doesn’t mean avoiding open source. It means treating it with the same long-term oversight you’d apply to any legally binding asset in your business. No shortcuts.
Generative AI introduces new opportunities and challenges
AI is changing how code gets written. Instead of manually drafting every function, teams are starting to use generative tools to handle initial code generation, refactoring, and support documentation. That can accelerate development, make junior teams more productive, and shift senior engineers into more strategic roles, including code review and system design.
But it’s not perfect. AI can generate code fast, but not always accurately. Outputs can be incomplete, misaligned with system context, or simply wrong. There’s a growing need for verification after AI contributes. If no one is reviewing the results with precision, mistakes make it into production, and debugging AI-generated code isn’t always straightforward.
Harpreet Singh, CTO at Watermelon Software, raised the issue of “AI hallucinations”, referring to confident but incorrect code generated by AI. He made the point clear: without additional layers of assurance, AI can produce results that undermine system integrity. Shubham Agnihotri, Chief Manager for Generative AI at IDFC First Bank, offered a real-world example. He used AI to convert monolithic architecture into microservices. The output looked good, but it didn’t work correctly, functionality broke down on execution.
Saju Pillai, SVP of Engineering at Kong, sees where this is going. He predicts developers will spend less time writing core logic and more time reviewing and refining what AI suggests. Sunny Bains from PingCap already uses AI in production support environments, especially to answer common technical questions quickly at what he called “level minus-one support,” freeing up engineers for more complex tasks.
For leadership, the direction is clear. AI can raise team velocity, but it’s not plug-and-play. It needs oversight, structured review steps, and limits on where and how it’s deployed. Treating it as a co-pilot, one that still needs approval before takeoff, keeps it useful and safe. Teams that embrace AI without validation frameworks will face risks they can’t quantify until it’s too late. Teams that build in review protocols will get productivity gains without compromising software quality.
The bottom line
Open source isn’t just a toolset, it’s a responsibility. It gives your teams speed, autonomy, and access to global innovation. But without structure, due diligence, and ownership, it turns into unmanaged risk. Especially at the enterprise level, where a single misstep can ripple across systems, users, and revenue streams.
Business leaders don’t need to avoid open source. They need to ask the right questions, about licensing, security validation, long-term maintenance, and support models. The same applies to AI integration. It’s promising, no doubt. It saves time, scales support, and accelerates iteration. But it still needs a human layer of review to deliver consistent, usable outcomes.
You don’t get stability just by choosing smart tools. You get it by layering process and governance on top of whatever tech you adopt. That’s the real unlock. With the right systems in place, open source becomes an innovation engine. Without them, it’s an operational liability wearing a clever disguise.
