-1.png)
What Is SLO Backtesting?
SLO Backtesting is the practice of validating a proposed reliability target against your actual historical performance — before the SLO goes live. Instead of setting a target and hoping it's right, you run your definition against real data and see the outcome immediately.
Every team starting with SLOs faces the same dilemma: set the target too tight and you burn through your error budget on normal noise; set it too loose and the SLO fails to detect real user-impacting problems. The only way to know is to test — but traditionally that means waiting weeks to accumulate enough live data.
Nobl9's SLO Backtesting eliminates the wait. You connect your existing data source, define your SLI query and proposed target, and Nobl9 pulls up to 30 days of historical metric data. Within minutes, you see a full reliability burn-down chart, statistical distribution of your SLI values, and a clear answer: does this target work with how your system actually behaves?
"SLOs shouldn't take forever to set up. With backtesting, you know if your definition is correct before you ship it — not weeks after."
At the core of Nobl9 Backtesting is the SLI Analyzer — a tool that runs two operations: data import and analysis. Import pulls your raw metric time-series and computes statistical benchmarks (Min, Mean, Max, StdDev, percentiles). Analysis simulates the error budget burn-down against your proposed target, letting you experiment with different values until you find the right fit.
1. Connect your data source
Select from 40+ monitoring tools — Datadog, Prometheus, New Relic, CloudWatch, Splunk and more.
2. Define query & time window
Enter your SLI metric query and choose how much historical data to pull (up to 30 days).
3. Import & analyze statistics
Get Mean, Max, StdDev, p95, p99 — plus a histogram of value distribution across the window.
4. Simulate reliability targets
Try different targets and budgeting methods. See the burn-down chart update instantly.
5. Create SLO from analysis
When the target looks right, create your SLO directly from the analysis — no re-entry needed.
Works with GitOps workflows
Once you've validated your target, you can create the SLO directly from the SLI Analyzer — with all your query settings, budgeting method, and target pre-populated. No duplicate data entry.
Two Steps to a Validated SLO
SLI Analyzer runs two operations — import and analysis — giving you a complete statistical picture of your service's reliability before you commit to any target.
Step 1: Data Import
When you run an import, Nobl9 retrieves historical time-series data from your monitoring tool and computes a full statistical profile of your SLI. This gives you an objective picture of how your service actually behaves — not how you think it behaves.
For threshold metrics (like latency), Nobl9 displays percentile values that let you pick a realistic target. For ratio metrics (like availability), you see the actual percentage of good events over your chosen window.
Statistical benchmarks include:
In this example, the p99 value of 0.58s becomes the natural starting point for a latency SLO target — most users experience well under this threshold, but it accounts for normal variance without being so tight that routine spikes burn the budget.
Step 2: Analysis
Once data is imported, you enter a target value and run analysis. Nobl9 simulates the full error budget burn-down over your historical window — showing exactly how much budget would have remained (or been exhausted) if this SLO had been active during that period.
You can freely experiment: change the target, switch between Occurrences and Timeslices budgeting methods, or narrow the time window to exclude a known incident. Each change produces an updated burn-down chart instantly.
Occurrences vs. Timeslices
Nobl9 supports both SLO budgeting methods — choose the one that fits your SLI type:
Create SLO directly from analysis
Once you've validated your target, Nobl9 lets you create the SLO directly from the SLI Analyzer — with all your query settings, budgeting method, and target pre-populated. No duplicate data entry.
How to Run Your First SLO Backtest
The entire process — from connecting a data source to creating a validated SLO — can be completed in a single focused session, without waiting for live data to accumulate.
Open SLI Analyzer in Nobl9
Navigate to the SLI Analyzer section in the Nobl9 web application. Click "New analysis" to start a fresh backtest session.
Select your data source and configure the query
Choose from any data source connected to your Nobl9 account. Enter the metric query you want to analyze — the same query you'd use in your monitoring tool's dashboard. Select your SLI type (threshold or ratio) and the relevant metric.
Set the graph time window and import data
Choose how much historical data to import — up to 30 days. Click "Import" and Nobl9 fetches your data in the background — you can navigate away while it runs.
Inspect statistical data and SLI distribution
Review the histogram showing value distribution across your window. Check the percentile table — for latency SLOs, the p99 value is typically a strong starting point for your threshold.
Set a target and run analysis
Enter your proposed target value and select a budgeting method (Occurrences or Timeslices). Click "Analyze" to generate the reliability burn-down chart. Adjust the target and re-run until you find a value that keeps the error budget healthy.
Create SLO from analysis
When satisfied with the target, click "Create SLO" directly from the analysis view. All your settings — data source, query, budgeting method, target, and time window — are pre-filled in the SLO creation form.
