Sold by: Metoro
Metoro is an all-in-one Kubernetes observability platform. APM, network monitoring and infra monitoring in under 5 minutes with zero code changes. Powered by eBPF.
Overview
Metoro is an all-in-one Kubernetes observability platform. APM, network monitoring and infra monitoring in under 5 minutes with zero code changes. Metoro lets developers, dev-ops teams and SREs monitor all of their Kubernetes clusters in a single pane of glass.
Metoro automatically generates traces across services by instrumenting at the kernel level with eBPF, inspecting network requests as they're made, generating L7 traces for protocols like http/dns/postgres/mysql and more.
Metoro also gathers all logs from the cluster with native support for structured logging without any limits on the number of searchable tags.
Metoro gathers and provides over 100 out-of-the-box metrics with support for custom metrics. Custom metrics can either be pushed to a local cluster collector in the Otel format or Prometheus remote-write or Metoro can scrape any Prometheus metrics itself.
Highlights
- Zero code changes required. Metoro uses eBPF to instrument kubernetes-based applications at the kernel level. No code changes are required from developers. A full installation and set up takes < 5 minutes.
- A single centralised platform for all your Kubernetes clusters, compare and diagnose issues across clusters.
- Get deep insights from network instrumentation. See every request made to services in the cluster and to external services outside the cluster.
Details
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AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
Pricing
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/unit |
|---|---|---|
Metoro charges per node minute used rounded down to the nearest hour | Metoro charges per node minute used rounded down to the nearest hour | $0.027 |
Custom Metrics (Per million samples) | Metoro charges per million samples for custom metrics | $0.10 |
Excess Logs (Per GB uncompressed) | Excess logs are logs which exceed the bundled amount included with each node | $0.30 |
Excess Traces (Per million traces) | Excess traces are traces which exceed the bundled amount included with each node | $0.20 |
Excess Metrics (Per million samples) | Excess metrics are metrics which exceed the bundled amount included with each node | $0.10 |
Metoro Credits | Usage accumulated through using the Metoro platform | $1.00 |
Dimensions summary
Metoro bills on usage. The core charge is per node minute, rounded down to the nearest hour, so your cost tracks the Kubernetes nodes you run. Each node includes bundled amounts of logs, traces, and metrics. When you exceed those bundles, you pay separately for Excess Logs per uncompressed GB, Excess Traces per million traces, and Excess Metrics per million samples. Custom Metrics are charged per million samples on top of standard usage. Metoro Credits reflect usage accumulated across the platform. These dimensions work together: node time forms the base, while the excess and custom metric charges scale with data volume.
Top-of-mind questions for buyers
A node minute measures one Kubernetes node running for one minute. Metoro tracks the nodes in your clusters and totals their running time. Metoro rounds that time down to the nearest hour when billing. So your base cost tracks the number of nodes you run and how long each stays active.
Each node includes a bundled amount of logs, traces, and metrics. You pay Excess charges only for volume beyond those bundles. Excess Logs bill per uncompressed GB, Excess Traces per million traces, and Excess Metrics per million samples. If your data stays within the bundle, no excess charges apply.
The per-node-minute charge forms the base and scales with how many nodes you run. Excess Logs, Traces, and Metrics only add cost when data exceeds the bundled amounts per node. Custom Metrics add charges per million samples on top. All charges bill independently and appear together. Node count typically drives the base cost.
metoro.io
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Vendor refund policy
If you're unhappy with the product after 30 days of billed usage, we will entirely refund all accrued usage costs.
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Delivery details
Software as a Service (SaaS)
SaaS delivers cloud-based software applications directly to customers over the internet. You can access these applications through a subscription model. You will pay recurring monthly usage fees through your AWS bill, while AWS handles deployment and infrastructure management, ensuring scalability, reliability, and seamless integration with other AWS services.
Resources
Support
Vendor support
Customers will have a dedicated Slack Connect / shared Microsoft Teams room with Metoro including engineers, the CTO and the CEO of Metoro.
Additionally, email-based support and urgent direct paging services are available.
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
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Kernel-Level Instrumentation
Automatic trace generation across services using eBPF technology at the kernel level without requiring code modifications
Protocol-Specific Tracing
Layer 7 trace generation for multiple protocols including HTTP, DNS, PostgreSQL, and MySQL
Unified Observability Platform
Integrated monitoring combining application performance monitoring, network monitoring, and infrastructure monitoring across multiple Kubernetes clusters
Comprehensive Metrics Collection
Over 100 out-of-the-box metrics with support for custom metrics via OpenTelemetry format, Prometheus remote-write, or direct Prometheus scraping
Structured Log Aggregation
Native collection and indexing of cluster logs with structured logging support and unlimited searchable tags
Real-Time Anomaly Detection
Unsupervised machine learning models train on every metric at the edge, scoring anomalies in real time with no configuration required.
Network Topology and Traffic Analysis
Live topology maps built from LLDP, CDP, BGP, and OSPF protocols; NetFlow v5/v7/v9, IPFIX, and sFlow v5 analysis with top talkers and Sankey diagrams; SNMP device auto-discovery across 200+ vendor profiles with trap receiver decoding 150,000+ trap definitions from 800+ vendors.
Per-Second Metrics Collection
Collects and processes per-second granularity metrics across 850+ auto-discovered integrations covering operating systems, Kubernetes, databases, web servers, message brokers, and AWS services.
AI-Powered Root Cause Analysis
One-click AI investigation on every alert that returns root-cause hypothesis with supporting evidence; generates alert configurations from plain English descriptions and back-tests against historical data.
Edge-Based Data Processing
Metrics are stored and processed on infrastructure with only views streaming to cloud; supports eBPF and OpenTelemetry ingestion with approximately 5% CPU core and 150 MiB RAM resource utilization on typical production systems.
Data Ingestion and Query Performance
Ingests petabytes of telemetry per day with capability to process hundreds of terabytes and execute tens of millions of queries daily without performance degradation
Knowledge Graph Architecture
Utilizes O11y Knowledge Graph to structure and correlate data across logs, metrics, and traces for fast search and correlation capabilities
Natural Language Processing for Incident Analysis
Enables troubleshooting of complex incidents using natural language queries through O11y AI for accelerated root cause analysis
Open Data Lake Foundation
Built on Snowflake data lake architecture providing open data storage without vendor lock-in
Multi-Signal Correlation
Correlates and correlates telemetry signals across logs, metrics, and traces with context-aware analysis for incident resolution
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