Cost efficiency is a key goal for many customers—across both the development and operations phases of software. But in practice, developers often focus more on minimizing build effort, while the long-term cost of operating the system receives far less attention.
This is ironic, because most software systems run far longer than the time it takes to build them. The real cost shows up during runtime—in areas like compute utilization, data storage and access, AI inferencing, maintenance, and support.
Let’s break down some low-hanging opportunities for making systems more cost-efficient during their operational lifetime.
1. Optimize Machine Utilization:
Start with the basics: shut down servers when they’re not in use. Idle machines still consume base-level power and incur maintenance overhead. Strive for a sweet spot in utilization—typically between 70–80%.
If you’re consistently below that threshold, it’s worth consolidating workloads. Grouping multiple low-utilization services onto fewer, higher-utilization nodes can drastically reduce your footprint across hardware, power, and monitoring.
2. Leverage Multithreading and Multi-Tenancy:
Designing systems to support multithreading and multi-tenancy can further improve resource utilization. Shared resource pools tend to be far more efficient than individually reserved resources.
In cloud environments, take advantage of features like burstable instances, spot pricing, and auto-scaling. Managed services from hyperscalers often bring built-in optimizations. Just be cautious of overprovisioning—it might feel safer in the short term, but it’s rarely sustainable from a cost or environmental standpoint.
3. Tame AI Inferencing Costs:
AI workloads—especially inferencing—can quickly become budget killers.
A smart approach is to cache repeat queries. In many systems like customer chatbots, common questions are asked repeatedly. Serving cached responses for these can significantly cut down on round-trips to expensive AI models.
4. Be Thoughtful About Data Storage:
Data is often the silent cost sink in production systems. The solution: store only what’s necessary, and don’t keep it forever.
5. Orchestrate for Efficiency Platforms like Kubernetes offer powerful ways to optimize compute clusters using containerization and workload packing. When configured well, they enable dense utilization of node pools, reducing waste and improving elasticity.
6. Monitor, Measure, and Tune
You can’t improve what you don’t measure. Set up runtime monitoring for key metrics—CPU utilization, memory usage, storage patterns, and server uptime. Use alerts to flag anomalies: underutilized servers, stale datasets, or persistent idle resources.
Over time, these signals can guide proactive cost-cutting moves, fine-tuned to your DevOps workflows.
Each of these techniques deserves a deeper dive, and I’ll explore them in future posts. But even simple shifts in awareness—thinking beyond build time into the life of your system—can yield dramatic cost savings, and make your software more sustainable in the long run.