Directory Observability & MonitoringLog Management & AnalyticsBeats

Beats

About Beats

Beats is an open source platform for single-purpose data shippers. Beats send data from hundreds or thousands of machines and systems to Logstash or Elasticsearch. Beats sit on servers, with containers, or deploy as functions, and centralize data in Elasticsearch. Beats ship data that conforms with Elastic Common Schema (ECS). Beats can forward data to Logstash for transformation and parsing. Beats are free and open and deploy as a binary without external dependencies.

Products

Filebeat: Lightweight shipper for logs and other data.
Metricbeat: Lightweight shipper for metric data.
Packetbeat: Lightweight shipper for network data.
Winlogbeat: Lightweight shipper for Windows event logs.
Auditbeat: Lightweight shipper for audit data.
Heartbeat: Lightweight shipper for uptime monitoring.

Used for

Tracking infrastructure logs and metrics in the Elastic Stack.
Collecting, parsing, and visualizing information from cloud platforms, containers and systems, and network technologies.
Gathering logs and metrics from environments and documenting them with metadata from hosts, container platforms, and cloud providers.
Monitoring containers.
Shipping data from serverless architectures.

From Beats's own website, read 2026-10-23. How we build listings.

How to evaluate Log Management & Analytics

This is how the CIOPages Research Team evaluates this category. It is not an assessment of Beats. It comes from our Log Management & Analysis buyer guide.

25%

Cost & Tiering Model at Volume

Unit of pricing (per-GB ingest, compute/SVC, credits, host, consumption) and how the bill behaves as volume grows; hot/warm/cold/frozen or archive tiers, index-free or low-cost retention tiers, rehydration cost and latency, and ingest controls or sampling to cap spend

20%

Search, Query & Analytics Power

Full-text vs. label-only vs. schema-on-read; query language depth (SPL, ES|QL/KQL, LogQL, DQL, SQL) and learning curve; performance on cold and high-cardinality data; aggregations, pattern detection, and live-tail for real-time troubleshooting

20%

Pipeline, Ingestion & OpenTelemetry

Agent footprint and collector strategy; native OpenTelemetry/OTLP ingest; parsing, enrichment, masking, routing, and reduction before storage; ability to fan data out to multiple destinations and to swap backends without re-instrumenting sources

15%

Security, Compliance & Retention

RBAC/SSO, field-level masking of sensitive data, immutable/WORM audit retention, and tamper-evident logging; SOC 2 / ISO 27001 / FedRAMP coverage and data residency; SIEM or threat-detection content if logs feed security

10%

Observability Correlation & Architecture

Whether logs sit standalone, inside an observability suite (correlated with metrics and traces), or inside the SIEM; trace-to-log pivots, unified context, and how cleanly it slots beside tooling you already run

10%

Operations, Scale & AI Assist

Self-managed vs. SaaS operational burden, multi-tenant and multi-cluster scale, ingest spikes and back-pressure handling; API/IaC coverage; and AI-assisted features such as anomaly detection, log clustering/patterns, and natural-language query

Other Log Management & Analytics Vendors

View all

Alibaba Cloud Log Service

Axiom

Baselime (Cloudflare)

Coralogix

DataSet

Elastic (ELK Stack)

CIOPages put this listing together from public sources. It’s information, not an endorsement. How we build listings. Work here? Claim this listing or .