Insights on AI monitoring, uptime engineering, and building reliable systems at scale.
AI teams spend thousands on LLM APIs with zero cost visibility. Traditional uptime tools cannot help. Here is why we built a monitoring platform that understands both.
We built Traxo because the monitoring landscape has not kept up with how modern teams ship software. Here is why we think AI-native observability is the next frontier.
AI API costs can spiral out of control overnight. Learn how to track token usage, set budget alerts, and avoid the most common cost pitfalls when running LLMs in production.
From context stuffing to model overspend, production LLM systems develop bad habits fast. Here is how Traxo catches them before they become expensive problems.
A server responding in Virginia does not mean it is reachable from Tokyo. Learn why multi-region monitoring eliminates false positives and gives you a true picture of global availability.
WebSockets are not always the answer. We chose SSE for our real-time monitoring dashboard and the results exceeded our expectations. Here is the full technical breakdown.