Metoro is a Kubernetes observability platform with an autonomous AI SRE agent for microservices. It covers infrastructure, network, and application performance monitoring in one product.
Metoro uses eBPF to collect runtime telemetry automatically. Teams install it with a Helm chart, and the vendor states setup takes under five minutes with no code changes.
Telemetry streams into a unified store, and the collectors map cluster topology and dependencies. When an alert fires, sandboxed AI agents investigate signals in parallel, correlate related events, and check recent deployments.
Deployment options include managed cloud, Bring Your Own Cloud (BYOC), and air-gapped on-premise installs. This makes Metoro relevant to platform teams and regulated organizations that restrict telemetry egress.
Pricing
The Hobby plan is free forever, needs no credit card, and covers 1 cluster, 1 user, 2 nodes, and 200GB of ingestion per month. The Scale plan costs $20 per node per month with unlimited clusters, users, and nodes. It includes 100GB of ingestion per node, and excess data costs $0.20 per GB. Enterprise pricing is custom, and paid plans offer a free trial of 14 days to one month.
* Disclaimer: Please note that pricing information may not be up to date. For the most accurate and current pricing details, refer to the official website.
Key Features
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eBPF collection of infrastructure, network, and APM telemetry
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Autonomous AI SRE agent for alert investigation
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Deployment verification against past baselines and code diffs
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Log analysis, distributed tracing, and continuous profiling
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SAML 2.0, OIDC, LDAP, SCIM, and role-based access control
Use Cases
Incident Root Cause Analysis
On-call engineers often query metrics and logs by hand during a critical alert. Metoro’s agent investigates across all signals and points to a likely cause within minutes.
Release Regression Checks
Frequent releases can add latency or error spikes. Metoro compares each deployment with past baselines using logs, traces, metrics, and code diffs.
Monitoring Without Instrumentation
Teams skip SDK rollouts and sidecars by installing one Helm chart. eBPF then captures APM and network metrics across the cluster.
Regulated Data Environments
Banks, healthcare providers, and agencies may not send telemetry to outside SaaS vendors. BYOC and air-gapped installs keep logs and metrics inside private networks.
Alert Noise Reduction
Duplicate warnings can hide real failures. Metoro correlates related signals and escalates only enriched incidents to the team.
Strengths & Weaknesses
Strengths
eBPF instrumentation gives infrastructure, network, and APM visibility in under five minutes.
The AI agent triages alerts and verifies deployments without manual queries.
BYOC and air-gapped options keep telemetry in customer-owned accounts or private networks.
Teams can route AI queries through Anthropic, AWS Bedrock, Azure OpenAI, or self-hosted models.
Metrics, logs, traces, and profiling live in a single platform.
Weaknesses
It targets Kubernetes, so standalone virtual machines and legacy monoliths get little native support.
BYOC and on-premise installs require customer-side management of cloud components, KMS keys, and Helm settings.
Code diff analysis and code-fix generation require enabling an external integration such as GitHub.
SAML, SCIM, and custom role setup adds administrative overhead.
Who Is This For?
DevOps and platform engineering teams: They run large Kubernetes estates and want observability that deploys through Helm.
SREs and on-call responders: The agent handles alert correlation and first-pass investigation to shorten time to resolution.
Software development teams: They get regression and performance checks on every release.
Security and compliance officers: Customer-managed encryption keys and zero-egress deployments support data sovereignty requirements.
Frequently Asked Questions
What problem does Metoro solve?
It replaces fragmented logs, metrics, and traces tools with one Kubernetes platform. The AI agent then investigates production issues and identifies root causes.
Does Metoro require application code changes?
No. eBPF collects infrastructure, network, and APM data without modifying application source code.
How do I install Metoro in a cluster?
Deploy the collector with a standard Helm install command. GitHub and AI notification rules are optional extras you configure afterward.
Which Kubernetes platforms does Metoro support?
It supports EKS, GKE, AKS, OpenShift, K3s, and MicroK8s clusters.
What deployment models are available?
Options are managed cloud, BYOC in your AWS, GCP, or Azure account, and air-gapped on-premise installs.
How is data protected when AI features run?
AI vendors are contractually barred from training on customer data, and transfers use TLS. BYOC deployments can use customer-managed encryption keys.
Can Metoro monitor virtual machines or legacy monoliths?
Only to a limited degree. The product is designed for Kubernetes workloads, so non-containerized systems get little native support.
Does Metoro work with existing log and OpenTelemetry pipelines?
Yes. It supports Vector for log collection and OpenTelemetry for ingesting traces, metrics, and logs.
What does the AI agent need to analyze code?
Code diff analysis and fix generation require the optional GitHub integration. Without it, investigations rely on runtime telemetry.
Metoro works with Kubernetes platforms (EKS, GKE, AKS, OpenShift, K3s, MicroK8s), Vector, OpenTelemetry, and GitHub for code-level context. It supports AWS Bedrock, Anthropic, Azure OpenAI, and self-hosted models for AI features. Identity providers include Okta, Google, Microsoft Entra, SAML 2.0, OIDC, and LDAP.