Metoro provides AI Site Reliability Engineering (SRE) for Kubernetes environments, offering autonomous issue detection and root cause analysis. The platform automatically correlates telemetry data like traces, metrics, and logs to generate suggested fixes via pull requests. This solution delivers zero-instrumentation, end-to-end observability and automated remediation for containerized applications.
Funding
$1.2M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
SPXVFounders
Product
Problem
Traditional application performance monitoring (APM) tools require manual code instrumentation, which is time-consuming, error-prone, and often necessitates application restarts. This complexity hinders rapid deployment and makes it difficult to gain immediate insights into microservices performance within Kubernetes environments.
Solution
Metoro provides a Kubernetes-native observability platform that automatically collects logs, metrics, traces, and profiling data using eBPF technology. By operating at the kernel level, Metoro eliminates the need for code changes or manual instrumentation, enabling instant and unified visibility into microservices performance. The platform facilitates automated performance regression detection and AI-driven root cause analysis, helping teams quickly identify and resolve performance bottlenecks. Metoro offers a single pane of glass for monitoring, unrestricted API access to data, and simple, predictable pricing.
Target Audience
Metoro targets developers and operations teams managing microservices in Kubernetes environments who need rapid, code-free observability solutions.
Features
- Automatic APM: Generates traces and derives APM metrics from services automatically through eBPF.
- One-click install: Deploys the collector with a single command without requiring container restarts.
- Automated performance regression monitoring: Periodically profiles services, compares profiles, and alerts on performance regressions.
- AI-driven root cause analysis: Proactively monitors for changes in applications and investigates root causes.
- Full APM data: Provides instant visibility into RED metrics and performance bottlenecks.
- Unrestricted API access: Allows access to all collected data.
- Supports multiple cluster types: Runs on Amazon EKS, Google Cloud GKE, Azure AKS, and bare metal (K3s, etc.).