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I built my home lab with tinkering, experimentation, and learning as the objective. I test containers daily, and have virtual machines for the many experiments I’ve run — some successful, others not so much. Plus, of course, there are backup servers and drives to take care of. But with inflation and the increasing cost of living, I’ve had to take a step back to calculate what my very expensive hobby was costing me. Between the equipment, the air conditioning to keep everything cool in Delhi’s sweltering heat, and ample ambient lighting for the vibes, I was confident my lab was adding a fair amount to my monthly electricity bill. Then I added power monitoring.

That addition to my home lab was all it took for me to take a step back and reconsider how I was running services. I started chasing efficiency instead of uptime. Seeing what my hardware was actually drawing in real-time made me realize that ‘always on’ wasn’t necessarily the best approach for a home lab. Here’s what I changed.

Chasing uptime vs chasing efficiency

Why keeping everything running isn’t always smart

Anyone in the home lab hobby would know that chasing uptime is the prime directive when we start building out our servers and systems. Between power redundancies, UPS systems, and more, we’re basically chasing a system that can stay up through power cuts, reboots, container restarts, crashes, and more. But nowhere in that do we sit back and consider idle power draw.

Now, to be sure, I did look at metrics. But those were CPU load, memory usage, and network throughput. Not the amount of energy I was consuming with each additional service I tacked on, especially things like local LLMs. All that changed when I added smart plugs to track wattage across the board.

The thing is, a single virtual machine might sit idle at a few extra watts. But that number multiplies quickly when you, well, multiply it by the number of active virtual machines, Docker containers, and background services. You might very well be pulling the same amount of power as a gaming PC running full tilt.

Taking a look at the power metering graph gives you further insights. Some things, like NAS drives spinning up at night to run backups, are non-negotiable. But how many power-guzzling containers and VMs do you actually need? That’s the question. Turns out most of my VMs were just not necessary. I left them running because I could, not because I needed to. And it was costing me. That’s the moment I realized that optimizing for uptime isn’t the same as optimizing for efficiency. But I didn’t necessarily need that uptime in a home lab.

Making power consumption a performance metric

Efficiency needs to be part of your deployment strategy

With the power metering data on hand, I used a combination of the app’s built-in graphs and Grafana next to CPU and memory metrics to understand which services were consuming more power than I anticipated. The learning here was that you need to use power usage as another performance metric, not as something you can ignore. This lets you design better systems.

I started off by grouping containers based on when they were needed. For example, a container running a local LLM was not needed when I was asleep. In fact, it’s something that should only be spun up when I’m actively using it. I got a lot more selective about the VMs I left on all the time. Similarly, NAS drives were scheduled to switch on and off on a schedule, with their onboard backup containers planned out to run on a schedule.

Similarly, I moved a variety of containers to lighter hardware. A Home Assistant install doesn’t need a full-blown server. That now runs off a NAS with significantly lower power requirements. This allows me to keep my smart home running while my server can be switched off. That particular NAS is also configured with flash storage instead of hard drives to ensure that power consumption stays low. Over time, I’ve consolidated my setup and split it into two, with all the essential services running on a single low-power NAS, and everything else on my server that is spun up only when needed.

Additionally, this power consumption-driven approach has also reshaped how I deploy new containers and services. Anything going on the essentials system needs to justify its use case and power consumption. If it’s just sitting there for a one-off use, it gets removed or moved to the server for occasional use.

Small changes that can have a lasting impact

Adding power monitoring and planning your container strategy based on power consumption can seem like a nuisance. And in some ways it is. But it’s a one-time nuisance. Once you’ve configured your containers based on need and power consumption, it’s effectively locked in, and you only need to follow the approach anytime you set up a new container. Once you can see your energy footprint, you start managing it like any other resource. Knowing the impact each container or VM has on my power bill has had a profound impact on how I decide what goes in my home lab.