The Scalability Trap: Why Smart Teams Build the Wrong Thing
How confusing two kinds of scalability leads to wasted engineering, lost deals, and rising TCO
The Two Faces of Scalability And Why Confusing Them Is Costing You
“Make sure it scales” is a phrase heard in nearly every product planning meeting, strategy session, and investor call. Everyone agrees, scalability is critical. But too often, no one pauses to ask: scalable in what way?
What often follows is a familiar pattern: developers and architects make critical decisions based on vague or misinterpreted goals, while leadership assumes the future is secured. But these aren't just technical detours, they can lead to strategic blind spots and costly outcomes.
In this article, I’ll break down the two main types of scalability and explain how confusing them can lead to real-world mistakes, while also offering a clearer path forward and try to highlighting the growing importance of Total Cost of Ownership (TCO).
The Two Types of Scalability
Let’s define them clearly:
Usage Scalability: The ability of a system to handle increased load, more users, more computing, more data.
This is typically addressed through performance engineering, elastic infrastructure (like cloud-native services), autoscaling, load balancing, and concurrency optimizations.Delivery Scalability: The ability to replicate, customize, and deploy a service or product across many customer environments.
This includes modular architecture, deployment automation, configuration management, environment isolation, and often, the ability to run in both cloud and on-premise contexts.
They’re both forms of scalability. But optimizing for one doesn’t mean you’re addressing the other.
Why the Confusion Matters
The core problem is that many people, especially in leadership, use the word “scalable” without realizing it can mean very different things. And in many cases, even the engineering teams don't ask which kind of scalability is actually being requested.
The result? Teams build solutions that don’t solve the real business challenge, and worse, are more expensive, harder to maintain, and slower to adopt across customers.
Example 1: The Over-Engineered Prototype
Let’s look at a scenario many product teams will recognize.
A startup develops a web-based analytics tool for industrial clients. It’s customized, performs well in the lab and during the proof of concept phase, and impresses the first customer. Motivated by this success, leadership says, "Let’s make sure it scales."
The engineering team takes this as a cue to “productionize” for high usage. They introduce Kubernetes, autoscaling, multi-region cloud deployment, and CI/CD pipelines for continuous delivery. Six weeks later, the platform can theoretically support 100,000 simultaneous users.
But when it’s time to onboard the second customer, it becomes clear that their needs don’t align with those of the initial proof-of-concept client. This new customer requires a local deployment with no internet access and strict data handling policies. Suddenly:
The cloud-native architecture becomes a liability.
The deployment requires significantly more hardware and administrative overhead.
The cost of infrastructure is higher for both the vendor and the customer.
The system, despite being “scalable,” is inflexible in the context that actually matters.
The engineering effort didn’t just fail to help, it actively made things worse and more expensive.
What should have happened?
The engineering team should have started by clarifying what “scalability” meant in the context of the business strategy. Was the leadership asking for the system to handle more users, or to support more customers in varied environments?
Had they asked the right questions early on
Will this run in the cloud, on-prem, or both?
Will every customer use the same setup, or will we need flexible deployment models?
What operational constraints might the customer environments have?
they would have realized the real challenge wasn’t usage scalability, but customer scalability: the ability to deliver the product easily to different types of clients with different requirements.
That realization would have changed everything.
Rather than reaching for Kubernetes, a powerful but complex solution geared toward horizontal scaling in the cloud, the team could have chosen something simpler and more adaptable:
A Docker Compose or Podman-based setup that is easy to deploy and maintain on customer hardware.
Or, if containerization wasn’t essential, a traditional application install (e.g., using system packages or a lightweight installer).
Or even a modular hybrid architecture, where core services could run locally and less-critical components remained optional or cloud-based.
These approaches would have led to:
Lower TCO for the customer (no need for heavy infrastructure or Kubernetes expertise).
Less overhead for the engineering team (fewer moving parts to support across environments).
A faster onboarding process, making the product more attractive to future customers with similar constraints.
In other words: they would have built the right kind of scalable system, one that scaled to more customers, not just more traffic.
"The right question is usually more important than the right answer."
— Robert Kiyosaki
TCO: The Hidden Scalability Metric
Scalability is not just a technical goal, it directly impacts the economic model of the product or service. That’s why understanding TCO (Total Cost of Ownership) is critical.
Usage scalability often increases operational cost per unit, especially when systems are designed to scale based on demand, but without clear foresight into actual usage patterns. For example, running expensive cloud compute resources 24/7, just in case they are needed, can significantly inflate operational costs. Without proper management, scaling for traffic and usage can lead to inefficiencies, where resources are over-provisioned to handle peak loads that may not be consistent or frequent. This can result in continuous, unnecessary expenses, such as idle computing power, storage, or network capacity, ultimately increasing the Total Cost of Ownership (TCO).
Delivery scalability requires upfront investment in flexible architectures, but it can drastically reduce onboarding costs and support overhead across customers. By incorporating solid configuration management practices, you ensure that each deployment, whether on-prem or in the cloud, is easily repeatable and adaptable to different environments. This approach allows the engineering team to provide customized solutions at scale, avoiding complex, one-size-fits-all architectures that increase costs and maintenance burdens. A focus on configuration management also means fewer manual interventions, streamlined processes, and the ability to quickly adjust to customer-specific requirements without sacrificing consistency.
When you choose the wrong kind of scalability, you're not just building inefficiently, you’re setting yourself up for higher costs, slower growth, and fragile deployments.
Real-World Confusions
Here are two more brief examples of common pitfalls:
Multi-Tenancy Misalignment
A team designs a customer portal assuming one tenant, hardcoding branding, and authentication flows. When a second customer signs on, the team realize the platform isn’t tenant-aware. Retroactive fixes create spaghetti logic and fragile workarounds.
Cloud-Native by Default
An engineering lead builds everything around AWS Lambda, S3, and DynamoDB, assuming infinite elasticity. When a new client then requires a EU-based on-prem deployments for compliance, none of it is reusable.
How to Get It Right
Make the Distinction Explicit: Always clarify: Are we scaling for more usage, or more customers?
Map Tech to Business Goals: Match architectural decisions to real-world constraints, such as network access, security policies, and expected customer diversity.
Model TCO Early: Estimate the lifecycle costs of infrastructure, deployments, updates, and support, for both you and your customers.
Build for Optionality: Wherever possible, design systems that can scale in both directions, but prioritize what’s most aligned with your go-to-market strategy right now.
In Conclusion: Don’t Just Scale—Scale the Right Way
Scalability isn’t a single metric. It’s a direction. It’s a choice. And it can make or break your product's success.
So the next time someone says, “We need to make this scalable,” pause and ask:
“Do we need to scale with more users, or scale across more customers?”
Because without a clear definition, even the most capable teams can invest heavily in solving the wrong problem, delivering performance where flexibility was needed.
“First solve the right problem, then solve the problem right.”