The SRE AI Brain That Never Sleeps

Centralized alert management with automatic root cause analysis across your entire monitoring stack. Reduce MTTR by 90% with AI-powered incident response.

How Uptimes.ai Works

Uptimes.ai deploys an autonomous AI agent in your Kubernetes cluster that monitors alerts from Datadog, Prometheus, PagerDuty, and other tools. When an incident occurs, the agent automatically investigates using eBPF network data, application logs, recent code changes, and infrastructure state — delivering a root cause analysis report in minutes, not hours.

Key Features

Who Uses Uptimes.ai

Site Reliability Engineers, DevOps teams, and platform engineers at B2B SaaS companies and e-commerce platforms use Uptimes.ai to protect SLAs, reduce alert fatigue, and resolve incidents faster.

Integrations

Free DevOps Utilities

Frequently Asked Questions

What is Uptimes.ai?

Uptimes.ai is an AI-powered incident response platform that reduces Mean Time to Resolution (MTTR) by 90%. It uses autonomous AI agents running in your Kubernetes cluster to automatically perform root cause analysis when incidents occur.

How does the AI root cause analysis work?

When an alert fires, the AI agent uses Claude with MCP (Model Context Protocol) tool-calling to investigate. It checks pod logs, Kubernetes state, eBPF network data, Datadog metrics, Prometheus alerts, and recent GitLab code changes to build a complete evidence chain and identify the root cause.

What monitoring tools does Uptimes.ai integrate with?

Uptimes.ai integrates with Datadog, Prometheus, PagerDuty, Kubernetes, GitLab, Slack, Elasticsearch, and Backstage. The AI agent uses these as MCP tools during investigation.

Is Uptimes.ai free to use?

Uptimes.ai offers a starter plan to get started. We also provide free DevOps utilities including an SLA calculator, incident cost calculator, cron expression generator, and MTTR calculator.

Does Uptimes.ai require changes to my application code?

No. Uptimes.ai uses eBPF for network observability at the kernel level, which requires zero application instrumentation. The AI agent runs as a deployment in your Kubernetes cluster and connects to your existing monitoring tools.