A greener path forward: Overcoming the hidden energy cost of multi-system software architectures

Technology plays a unique and prominent role in an enterprise’s sustainability or green IT efforts. It can be a key part of the solution and a contributor to the problem. As Earth Day approaches, we’re reminded to examine how our choices impact the planet.

Modern applications have brought remarkable convenience and scalability, but beneath these sleek user experiences lies a growing energy appetite. As cloud infrastructure expands and workloads become more compute-intense, the strain on global power grids increases. 

For environmentally conscious developers and tech executives, it’s time to consider energy efficiency as a first-class goal in software architecture.

Modern Application Delivery’s Energy Problem

Today’s cloud applications are more complex than ever. In the quest for scalability, modularity, and performance we’ve embraced microservices, distributed systems, and multi-layered technology stacks. 

A typical web application might involve separate services for the UI, API gateway, business logic, database, cache, and message queue – each potentially running on different servers. While this distributed architecture brings flexibility, it also introduces significant overhead that isn’t immediately obvious. 

Every time one service calls another, data must be packaged (serialized into JSON or another format), sent over a network, and then unpacked on the other side. The service tier, I/O, and serialization can have a negative impact on system performance – and what chips away at performance is also chipping away at efficiency.

Consider a simple user request in a microservices architecture. It might:

• Go through an API gateway to a front-end service.
• Trigger calls to several back-end microservices in sequence.
• Each internal call requires serialization/deserialization of data (converting objects to a transferable format and back again) and a network hop. 
• Data might be fetched from a database and passed through a cache layer, adding more overhead. 
• Multiple copies or transformations of the same data may be created along the way.

These extra steps make the system more CPU-intensive than it needs to be. Recent research found a fine-grained, microservice design consumed 13% more energy and added latency compared to a more consolidated design. 

The latency cost of microservices – an additional 5 milliseconds here or there – is also an energy cost, because CPU cycles spent on overhead still draw power. 

Modern architectural patterns can inadvertently create digital friction, where servers are busy doing work that isn’t core to the application’s purpose. Instead, it’s just moving data around and coordinating between components. This complexity doesn’t only affect a few servers – at scale, it multiplies. 

Large applications might spawn hundreds of services across thousands of machines. Many of those machines run at low utilization, waiting on network calls or handling repetitive data conversion tasks. The result is an industry where compute cycles – and the energy that powers them – are often wasted on overhead.

But there’s another hidden layer to this energy problem. Rather than eliminating the root cause of overhead, teams often throw more hardware at the issue – adding redundancy, spinning up extra instances, or distributing services across geographies. But there’s an asymptotic limit to what hardware can solve. Over time, this strategy becomes a game of diminishing returns.

Geographic proximity is then used as a patch, compensating for performance penalties that were introduced by the system’s own modular design. Instead of continuing to scale infrastructure outward, we should rethink our approach inward. By reducing unnecessary serialization/deserialization and minimizing inter-service chatter, we can target latency where it starts. That’s a far cheaper and more sustainable fix than continuously scaling up compute.

Simpler software architecture can mean less overhead, which ultimately means less energy consumed. When architected well it can even be more performant. 

Compute Cycles = Carbon Footprint

The connection between compute and carbon is direct. Most electricity is still generated from fossil fuels, so the more power servers draw (the more CPUs), the more carbon dioxide is emitted. 

On average, the global power grid emits on the order of 0.5 kg of CO₂ for every kilowatt-hour of electricity produced. That means if a piece of software causes a server to use an extra one kilowatt through inefficient code or unnecessary processing, it’s like putting an additional half-kilogram of CO₂ into the atmosphere. 

Exploding demand for data and compute contributes to the tech industry’s aggregate footprint, with data centers using an estimated 240–340 terawatt-hours of electricity, about 1–1.5% of global demand. While big cloud providers are pledging green energy and improved cooling efficiency, and indeed hyperscale data centers are more efficient, if we continue to layer on complexity without regard to its impact on energy, efficiency gains at the infrastructure level will easily outpace inefficiency at the software level. 

A Fully Fused Stack: Doing More with Less

How can we break the cycle of ever-increasing infrastructure complexity and energy use? 

One promising approach is to simplify the stack itself – to fuse the layers of technology so tightly that much of the overhead disappears. Combining the database, caching, application logic, and even real-time messaging into a single unified process. 

Instead of running a separate Node.js server, Redis cache, Kafka queue, and MongoDB database, and having them chat with each other over networks, you can have one integrated system that provides all those capabilities internally. 

By removing the overhead between systems, the total compute requirements for running an application are significantly reduced. 

Think of what this means in practice. With a fused stack, when your code needs to read data, it calls a function in-memory and gets the data – no serialization to JSON, no TCP/IP round-trip, no context-switch to a separate database server process. The data is fetched and delivered within the same process space. 

Similarly, if you publish a message or query some cached result, it’s handled by the same running engine – not handed off to a separate broker or cache service. 

By deploying data, application, and messaging functions together as a single package, you eliminate the multiple hand-offs that plague a traditional multi-tier architecture, internalizing what would otherwise be network calls or cross-system calls. The result is a dramatic drop in overhead. Less CPU time wasted on packing/unpacking data, fewer context switches, and far fewer network operations per user request.

This approach yields concrete efficiency gains. Fewer independent moving parts mean fewer system layers to manage or secure. There’s less redundant work being done and even less hardware needed overall to support a given workload. 

When you remove needless layers, the system not only runs faster for users, but it also runs leaner on resources.

Innovating for a Sustainable Tech Future

As we celebrate Earth Day, it’s worth reflecting on how software developers and tech leaders can contribute to a more sustainable future through the choices we make in our systems, code and software architecture. Let’s challenge ourselves to build software with the planet in mind.

Modern application delivery doesn’t have to be an energy sink. A fully fused tech stack shows that we can maintain the scalability and functionality we need while stripping out the excess baggage of over-engineered architecture.

Efficient design is sustainable design. Every bit of optimization, every layer fused, or function streamlined, is a step toward a sustainable digital ecosystem. The future of computing can be both high-performance and green – and it’s up to us to make it happen.