Overcoming Integration Challenges in Super App Development
Super apps are often described as the Swiss Army knives of mobile platforms—combining multiple services like payments, ride-hailing, messaging, food delivery, and e-commerce into a single interface. Their potential is huge, especially in emerging markets. But building one? That’s a different story. In theory, a super app offers convenience, loyalty, and cost-efficiency. In practice, organizations quickly run into a wall: integrating siloed operations and technology stacks. This article explores the main integration challenges enterprises face when building a super app—and how to overcome them with a clear strategy, technical interoperability, and user-first thinking. What is a super app? A super app is a mobile platform that offers multiple services through a single interface, allowing users to handle everything from messaging and payments to ordering food or booking rides. Think WeChat, Grab, or Gojek—apps that dominate daily life in many parts of Asia. The model has been particularly successful in regions where mobile usage is dominant, and app fatigue is high. Western markets have been slower to adopt the model, but interest is growing. In fact, Europe may be lagging, but it’s not far behind. Fragmentation: The roadblock to super app success Large enterprises, especially those with diverse business units, often struggle with a lack of cohesion. Each brand or service runs on its own stack, has its own customer base, and defines success in its own way. This siloed structure prevents companies from delivering a seamless, end-to-end customer journey—one of the core benefits super apps are meant to provide. Without integration: One of the top pain points in super app development is the difficulty of connecting disparate services into a coherent platform. Why integration matters more than ever Super apps aren’t just about bundling services—they’re about building an ecosystem. When done right, this ecosystem offers: More importantly, it fosters habit-driven behavior. As noted in this primer on super apps, users are more likely to return when they can complete multiple tasks in one place. Integration challenges (and how to solve them) Let’s break down the major technical and organizational hurdles that make integration so difficult—and what you can do about them. 4 major integration challenges to address To move toward an integrated future, enterprises must tackle several key pain points. Let’s break them down. 1. Tech stack consolidation One of the first hurdles is the lack of a unified tech foundation. Legacy systems, proprietary stacks, and hardcoded dependencies create major obstacles. Connecting these pieces often means rebuilding architecture from the ground up—or creating middleware layers that can bridge gaps. A modular approach using microservices and APIs is crucial here. This not only enables interoperability but also makes it easier to plug in new services later. A study on platform-based digital strategies confirms that architectural flexibility is key to scaling super apps. The fix: Start by auditing all existing services and identifying integration bottlenecks. Then design an API-first roadmap. 2. Unified user identity Users don’t want to sign up five times to use five services within the same app. But many companies fail to provide a single sign-on (SSO) or unified identity system across their ecosystem. This leads to inconsistent user experiences and makes personalization nearly impossible. Solving this requires a shared authentication layer, tokenized sessions, and centralized identity management that works across brands and platforms. The fix: Consider building a universal “App ID” that spans all services and syncs with user profiles in real-time. 3. Centralized data & Insights Data is the lifeblood of any super app, but it’s often stuck in silos. Without centralized access to behavioral, transactional, and demographic data, your ability to personalize or optimize services is severely limited. Investing in a Customer Data Platform (CDP) that aggregates and normalizes data across the ecosystem allows for: The fix: Prioritize consent and transparency. Users must trust that their data is used responsibly—especially in a super app that touches many parts of their lives. 4. Cultural and operational resistance The final challenge is often internal. Integration isn’t just about systems—it’s about people. Different business units may resist change, especially if they fear loss of autonomy or control. In the super app development journey, successful companies emphasize cross-functional teams, shared KPIs, and leadership alignment from the start. The fix: Create a governance model that promotes collaboration, not competition, between business units. Market expansion: Build, buy, or partner? To scale a super app, you’ll likely want to enter new verticals—groceries, mobility, digital services. But how? Option 1: Build In-House This route gives you full control over UX, data, and infrastructure. But it’s slow and capital-intensive. Best for: companies with deep expertise in the target vertical and long-term strategic commitment. Option 2: Acquire Buying an existing player gives you instant access to customers and operations. But it also means you inherit legacy systems, cultural baggage, and potentially high integration costs. Best for: companies looking for quick market share and willing to manage M&A complexity. Option 3: Partner This is the fastest way to test a new vertical, often through APIs or co-branded experiences. The downside? Less control over UX and long-term positioning. Best for: companies testing new categories or entering competitive markets without heavy upfront investment. Start with what you control If you’re struggling to make everything click, zoom in. Before launching new services or integrating with partners, focus on optimizing what you already own: This user-first approach is similar to the principles outlined in our UX guidelines: simplify, unify, and focus on value delivery. Design for modularity and scale Integration isn’t a one-and-done project—it’s a strategy. As new competitors emerge and user expectations grow, your architecture must support ongoing evolution. The European market has been slow to adopt super apps partly because of fragmented regulations and legacy systems. A modular, privacy-conscious approach is key to overcoming those barriers. Future-proofing your super app Creating a super app isn’t just about design or functionality—it’s about integration. Unifying legacy systems, aligning teams, and centralizing data is hard work. But the payoff is a resilient, scalable platform that meets users where they are—and keeps them coming back. Whether you’re building from scratch or transforming an existing ecosystem, the key is to start small,
Scaling Microservices: Lessons from Netflix, Uber, Amazon, and Spotify
The shift from monoliths to microservices has reshaped how enterprises build software. What began as a way to accelerate delivery soon became the backbone of digital transformation at global scale. Key takeaways Microservices promised agility and scalability. For enterprises under pressure to innovate, the idea of smaller, independent services that could be deployed quickly was irresistible. Yet the real test begins when these systems scale. What looks like freedom in the early days often becomes fragility when service counts rise into the hundreds. Why enterprises adopt microservices The move toward microservices is driven by a need for speed, fault tolerance, and flexibility. Enterprises want teams that can deploy independently, isolate failures, and scale different parts of the system without being held back by a massive monolith. Research shows that 75% of companies are focusing on cloud-native apps. And Gartner estimates that in 2025 more than 95% of new digital workloads will be deployed on cloud-native platforms, up from 30% in 2021. For many enterprises, the shift isn’t just about efficiency—it’s about staying competitive in a digital-first world. But as adoption accelerates, the complexity of operating hundreds of services becomes a challenge in itself. Enterprises quickly realize that microservices don’t eliminate complexity—they redistribute it. The challenges of scaling One of the most visible challenges is service sprawl. Uber, for instance, scaled so quickly that it ended up with over 1,000 services, many with tangled dependencies. The architecture was so difficult to manage that engineers referred to it as the “Death Star.” Observability is another recurring issue. Netflix learned early on that a distributed system without unified logs, metrics, and tracing is nearly impossible to debug. Each service might report as healthy, but the overall system could still fail in unpredictable ways. Ownership and accountability also become critical. Amazon discovered that without clear responsibility, downtime led to endless handoffs and finger-pointing. Their solution was the now-famous “two-pizza team” model, which gave small teams complete end-to-end responsibility for the services they built. Finally, culture cannot be overlooked. Spotify pioneered the squad model to give teams autonomy, but autonomy without guidance soon led to duplication and coordination problems. To solve this, Spotify introduced “golden paths”—recommended tools and best practices that offered autonomy with guardrails. Enterprise case studies Uber: taming sprawl and governing APIs Uber’s rapid expansion led to a sprawling ecosystem of thousands of services, many developed quickly to meet local needs. The result was overlapping APIs, tangled dependencies, and limited visibility into how services interacted. Beyond simple service sprawl, the absence of consistent API governance created fragmentation: different teams exposed similar functionality with inconsistent contracts and standards, making it hard to evolve the platform cohesively. To address this, Uber invested in consolidation and standardization. A service mesh streamlined communication, a unified metrics platform (M3) brought observability under control, and governance practices clarified how APIs should be designed, documented, and consumed. Uber learned that scaling isn’t just about adding services—it’s about enforcing discipline in how they connect and evolve. Amazon: ownership, discipline, and SLAs Amazon faced scaling challenges long before cloud-native architectures were common. Its solution was cultural as much as technical. The famous “two-pizza team” model ensured that no service was owned by a group too large to feed with two pizzas, forcing clear accountability. But ownership was paired with operational discipline: service-level agreements (SLAs) defined expectations for reliability and performance, and the principle of “you build it, you run it” kept teams responsible for their systems in production. This combination of autonomy, accountability, and measurable commitments allowed Amazon to scale not just its architecture but its operating model. Microservices at Amazon worked because responsibility and discipline scaled alongside technology. Spotify: autonomy with Backstage Spotify embraced microservices alongside its squad and tribe organizational model. Small teams had the freedom to build and run their own services, which initially accelerated delivery. But over time, duplication of infrastructure and inconsistency in developer experience created friction. Spotify’s answer was twofold. First, it introduced golden paths—curated sets of tools and practices that teams could adopt without being forced. Second, it developed Backstage, an internal developer portal that centralized service catalogs, documentation, and tooling. Backstage became a key enabler of productivity and consistency, reducing the cost of autonomy. The result was faster delivery with less duplication and a smoother developer experience. Best practices that emerge Looking across these enterprises, a few clear lessons stand out. Platform engineering plays a crucial role by providing shared infrastructure that abstracts away complexity. Observability must be unified so that distributed systems can actually be monitored and debugged. Ownership needs to be clearly defined, with accountability written into team structures. And resilience has to be designed in from the beginning through fault-tolerant patterns and practices like chaos testing. Perhaps the most important lesson, however, is balance. Too much freedom leads to chaos, while too much control stifles innovation. Enterprises like Spotify show that autonomy can coexist with guidance if the right frameworks are in place. Avoiding common pitfalls The temptation when adopting microservices is to equate scaling with adding more. Uber’s early struggles are a reminder that more services often mean more problems. Similarly, enterprises that invest heavily in tools without preparing teams and processes often find those tools underused or misapplied. As Sam Newman, author of Building Microservices, has warned: “Don’t start with microservices. Start with a monolith you understand. Then, only when scaling demands it, split carefully.” Enterprises that ignore this advice often find themselves with an over-engineered system that’s harder to manage than the monolith they left behind. At Netguru, we’ve observed these same patterns in industries ranging from fintech to healthcare. The names and tools differ, but the challenges rhyme. Enterprises often underestimate governance, treating it as a problem for later. They delay investment in observability until the first major outage forces them to act. And they assume that ownership will emerge naturally, rather than defining it explicitly. Our work has shown that the enterprises that succeed with microservices are the ones that treat them as a sociotechnical challenge. Technology is only half the battle. Let’s wrap
ChatGPT Just Became a Shop: The Agentic Commerce Revolution
Commerce inside ChatGPT is live (and evolving fast) OpenAI has just taken a bold step: you can now purchase products directly inside ChatGPT. This isn’t a toy experiment—it’s a fundamental shift in how commerce is delivered. Here’s where things stand today (and where they’re heading): This isn’t just “adding commerce.” It’s collapsing the funnel: discovery, buying, and transaction all exist in one flow now. The new plumbing behind it: Agentic Commerce Protocol (ACP) This shift is powered by a new open standard: the Agentic Commerce Protocol (ACP), co-developed by OpenAI and Stripe. Here’s how it works in brief: This protocol is the plumbing, not the show. But its openness suggests that OpenAI is aiming not only to operate this environment, but to expand a broader agentic commerce ecosystem around ChatGPT and beyond. Why this matters: collapse × scale × control When you combine the commerce shift (discovery → purchase in chat) with ACP underneath, you get a paradigm shift in e-commerce. Some key implications: As Shopify puts it: “Shopify merchants will be able to sell directly through ChatGPT conversations—no links or no redirects, just seamless commerce.” Shopify Vanessa Lee, VP of Product at Shopify, adds: “Shopping is changing fast. People are discovering products in AI conversations, not just through search or ads. This will let our merchants show up naturally in those moments and give shoppers a way to buy without breaking their flow.” Shopify Etsy’s side is equally optimistic. Rafe Colburn, Etsy’s Chief Product & Technology Officer, said: “ChatGPT helps us meet buyers where they are.” TechRadar A mixed signal from markets and reaction The announcement sparked sharp moves in financial markets: Etsy’s stock leapt (though later pulled back) and Shopify shares jumped in response. Reuters But some analysts urged caution. For example: “We are encouraged to see Etsy adapting for agentic (commerce) … but the long-term benefits remain uncertain.” — Nathan Feather, Morgan Stanley Investors That skepticism is fair. Execution is hard. Merchant adoption, buyer trust, policy, fraud, global scaling—all have to line up. Meanwhile, retail metrics already show early signs of ChatGPT’s influence: one study found that 20% of Walmart’s referral traffic now comes from ChatGPT, with similar effects for Etsy and Target. Digiday If you combine referral migration + now built-in purchasing, the shift is accelerating. What you should be doing right now If you’re in e-commerce, this moment demands strategic action. Here’s your playbook: Move Why it’s urgent Integrate with Shopify / enable ACP / Instant Checkout To be discoverable and transactable inside ChatGPT Expose rich structured catalog & metadata Good AI dialogue needs good data: variants, attributes, descriptions, availability Optimize for conversational discovery, not just SEO Think “what would someone ask ChatGPT?” rather than just web keywords Prepare fulfillment, returns & operations A bad chat purchase experience will nullify gains Measure new signals Conversational engagement, conversion inside chat, chat abandonment, attribution Experiment with bundles / multi-brand flows In chat you can pull items from multiple merchants—be ready to compete in that space Prepare for regional rollout & multi-cart support The stack will evolve; early adopters gain experience Risks, caveats & open questions Why this is bigger than “just a new channel” The significance isn’t only in adding in-chat buying. It’s that chat is becoming commerce. Over time: In short: the digital storefront as we’ve known it is being subsumed by conversation itself. Final thoughts ChatGPT is no longer just a search or assistant tool. It’s becoming a shopping destination. With Instant Checkout and the open-sourced Agentic Commerce Protocol, OpenAI & Stripe have laid the foundation. Etsy is the initial launch, Shopify is coming soon, and the roadmap is broad. For brands and retailers, this is the kind of moment where early positioning matters. If you can’t show up inside conversation, you risk being sidelined in the era of intelligent commerce. This is more than a new feature. It’s a redefinition of where and how commerce happens—inside the chat, powered by AI, modular and fluid.