Tools for Prometheus monitoring: SLO-based PromQL generator, error budget calculator, and scaling to avoid OOMs.
Cardinality Cloud, LLC
Articles and tutorials about Prometheus monitoring, SLOs, and observability
Why does a 5% error rate trigger an alert at 2 AM? Is it catastrophic during peak traffic or meaningless during low usage? Traditional static thresholds can’t tell you. SLO-based alerting asks a better question: “Are we consuming our error budget faster than planned?” This approach ties alerts directly to user-impacting reliability issues, eliminating arbitrary thresholds and reducing alert fatigue while catching real problems early.
You’ve deployed the generated SLO rules to Prometheus - now what? The recording rules are pre-computing your SLO metrics every minute, but how do you actually check if you’re meeting your targets, monitor error budget consumption, or build dashboards? This guide shows you the essential PromQL queries to unlock the full power of your SLO monitoring, from checking current status to visualizing long-term trends.
Your monitoring just went down because Prometheus got OOM-killed again. Or maybe you’re paying for 32GB of RAM when 8GB would suffice. Sizing Prometheus shouldn’t be guesswork - it’s actually predictable math based on three inputs: active time series, scrape interval, and retention period. Our Resource Calculator does the math for you, showing memory, CPU, and disk requirements with visual guidance on safe ranges and real-world scaling examples.
Engineering wants to slow down and fix stability issues. Product wants to ship faster and hit deadlines. Who’s right? Both - and neither. The real question isn’t “should we prioritize reliability or velocity?” but “how much unreliability can we tolerate while still meeting our promises?” That’s your error budget: the quantitative answer that turns endless debates into data-driven decisions. With a 99.9% SLO, you get 43.2 minutes of downtime per month to spend on innovation, experiments, or controlled risks.
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