What's your uptime SLA over the last month, six months, year? Do you know off the top of your head? Is your kneejerk response, "as close to 100% as possible"? Consider this: by not knowing your true current SLA, you not only turn a blind eye to a critical success metrics for your systems, you also remove the ability to budget within the margins of that metric.
There are about 8765 hours in a year. How many of those hours do you believe your code absolutely needs to be up to keep your business successful? 99% of the time buys you almost 88 hours of downtime over the course of a year. 99.9% of the time still buys you almost 9 hours. Even 99.99% gives you about 52 minutes a year that your systems can be down. Think of what you can do with these minutes. Note that the much-vaunted "5 9s" reliability (99.999%) breaks down to 8 minutes over the course of a year, which is great if you're Google or the phone company, but probably not a smart goal for your average startup.
Let's say you know that your deployment process is rock-solid without outage and you will never need planned hardware downtime due to the way you've architected your systems. But you also know that you have some risky features that you want to push now, before you announce a critical partnership that should result in a big membership bump. If you're sure that the bump won't cause downtime, you might choose to push the features and risk some downtime in smoothing out rough edges on the code so that you have a really compelling site for those new members.
On the other hand, if you know that you're pretty solid under your current load but a 50% increase in usage has the potential for some degree of system failure, your error budget might not accommodate both the risky new features and the membership growth. And if your business pushes you to do both the risky new features and the growth risk? Make sure they know that your SLA may suffer as a consequence. When you know your goal SLA, and you know something is likely to reduce or violate it, that's a strong signal that you should think carefully about the risks of the project. This can also be a useful negotiation tool when being pushed to implement a feature you don't think is ready for prime time. When they say we need to release this new feature today, which means at least two hours of downtime that pushes you out of SLA, it becomes their job to get authorization from the CTO instead of your job to convince them why it is a bad idea.
I will admit that I do not currently have an uptime SLA for my services. Up until recently, it never occurred to me that there would be any value to trying to pin down a number and measure to it. As a result, while liveness and stability is always a consideration, I haven't taken the time to think through the rest of the year when it comes to hardware upgrades, new features, or deployment risk as measured by likely downtime impact. I'm missing out on a key success metric for my infrastructure.
Once I've managed to nail down a course-grained uptime SLA for my systems, the next phase of this work is to nail down a more fine-grained response time SLA. Of 100 requests, what is the 95th percentile response time from my infrastructure services? This is much trickier than a simple uptime SLA due to the interaction of multiple systems each with their own SLAs. For now though, I need to focus on the big picture.