Blog
From the blog
Practical Kubernetes guides, operations at scale, and notes from building KubeBolt.
Three LLMs Are Better Than One: Cost Optimization in AI Ops
Using one big model for every incident burns money. Here's how KubeBolt routes across a deterministic layer, Haiku, Sonnet, and Opus to resolve incidents end-to-end for under $1 each.
The Hidden Cost of Per-User Pricing in Observability
Most observability tools bill per active user and per host. The result: your monitoring bill scales with headcount, not with the system you actually run. Here's the math — and why per-resource pricing changes it.
Building an Autonomous Operations Engine: the 6-Layer Architecture
Most AI Ops tools are black boxes that return magic answers. Here's how we built the opposite: a six-layer engine where a deterministic Layer 1 resolves the majority of incidents with no LLM call, and the LLM never touches your cluster directly.
Why We Built Another Kubernetes Tool in 2026
The observability market looks saturated — Datadog, Grafana, New Relic. So why build KubeBolt? Because there's a gap none of the incumbents can fill without rewriting their product.
Common Kubernetes Errors and How to Fix Them
A practical guide to the most common Kubernetes errors — CrashLoopBackOff, ImagePullBackOff, OOMKilled, Pending pods and probe failures — with the kubectl commands to diagnose and fix each.
We Built KubeBolt Open Source First
Open source isn't just a go-to-market for infrastructure tools — it's a covenant with your users. Why we shipped KubeBolt as Apache 2.0 and what we promise stays that way.
How to Automate Kubernetes Incident Resolution
Alerts tell you something broke — they don't fix it. A practical look at the levels of Kubernetes incident automation, from runbooks to autonomous remediation, and how to do it safely.
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