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The Basics
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What's an SLO?
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What's an SLI?
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What's an Error Budget?
A service level objective (SLO) is a performance target. It answers the question, “How good does this service need to be, and how often?”.
That might mean:
- 99.9% of login requests succeed within 300ms over 30 days
- 95% of data refresh jobs complete within 15 minutes
- Less than 0.1% of search queries return an error per week
An SLO is a goal. A line in the sand that helps your team decide what’s acceptable, what needs attention, and what can wait. As both your software and environment shift and change often, so should your SLOs. Good SLOs are meant to be iterated on as circumstances change.
A service level indicator (SLI) is a raw measurement of how a service is performing. It’s a single, observable metric like availability, latency, or error rate, and it’s often pulled from telemetry systems.
Think of it as the evidence.
Examples of SLIs:
- Percentage of successful HTTP responses
- Average response time for a checkout API
- Rate of failed background jobs
An SLO uses one or more SLIs to define a target. If your SLI is "successful checkouts per request”, then your SLO might be: "99.5% of checkouts succeed over 30 days."
An error budget is the margin for failure you’re allowed based on your SLO.
If your SLO is 99.9% success, that means 0.1% of failures are allowed over a specific time window. That’s your error budget. It’s how you manage risk intentionally instead of emotionally, and it’s how you tell when to prioritize stabilizing reliability on critical services.
Error budgets let teams take calculated risks, like shipping faster or holding off on low-priority incidents, because you know how much room you have before reliability, and the customer experience behind each service is at risk.
Knowing what’s happening isn’t the same as knowing what matters.
When they promise you full visibility, they are probably delivering that to you, and more, with the mountains of dashboards, trends, metrics, alerts, and forensic views. They give you so much to sort through that it almost becomes meaningless. It’s a basement or attic full of stuff, but also mysteries unless you’ve seen the infrastructure from the start.
Every tool promises full visibility. But most of the time, visibility doesn’t mean you know what the hell is going on.
You’ve got dashboards full of metrics - MTTR, availability, percentiles, latency. Alerts firing left and right. You’re reacting fast, sure. You can see everything. But with that much information, it all starts to blur.
SLOs bring context
They help you cut through the noise and figure out what actually matters.
What to work on next.
What that feature release will mean for stability.
Where your KPIs connect - or don’t connect - to the customer experience.
And how your team should invest in reliability going forward.
As systems grow more complex, teams need better ways to understand how well their services are performing.
Dashboards full of uptime, latency, and MTTR metrics give you lots of information, but not always insight, especially when it comes to what users actually experience.
Service level objectives, or SLOs, are performance targets. On their own, in a vacuum, they’re exceedingly simple: a goal for how often something should work. However, when used well, they help teams define what reliable means in context. They create clarity across engineering, product, and leadership and help teams align on where to invest, when to respond, and how much risk is acceptable.
SLOs aren’t new, but they’ve become essential as teams grapple with managing reliability in the most efficient way possible. As adoption increases, teams are asking harder questions:
How do we make them work across teams?
How do we connect them to business priorities?
And what happens when the first version doesn’t deliver what we expected?
Let’s break it down…
The SLO Paradox
Anyone who understands modern reliability practices agrees on one thing: SLOs are the right way to manage service health. They're outcome-focused, grounded in user experience, and provide a common language across engineering, operations, and business stakeholders.
But the moment you try to implement SLOs at scale,
the whole thing becomes... a mess.
