Comparing ELK, EFK, and PLG Stacks: Which Logging Solution Fits Your Kubernetes Environment?

Elastic Stack (ELK)

The Elastic Stack consists of Elasticsearch, Logstash, Kibana, and Beats. Beats collect logs, Logstash aggregates and processes them, Elasticsearch stores and indexes logs for fast search, and Kibana provides a web UI for visualization.

EFK Stack for Kubernetes

In containerized environments, especially Kubernetes, the EFK variant replaces Beats with Fluent Bit . Fluent Bit is a lightweight, multi‑platform log processor that can:

Collect logs from many sources (files, Docker, systemd, etc.)

Forward logs to multiple destinations (Elasticsearch, Loki, etc.)

Run natively in Docker and Kubernetes pods

PLG Stack (Promtail + Loki + Grafana)

Components

Promtail : log‑collecting agent that discovers Kubernetes pods, reads log files and systemd journals (AMD64 only), and attaches user‑defined labels before shipping logs to Loki.

Loki : log storage system that indexes only label pairs, not full log messages. This reduces indexing cost and storage overhead. Logs can be stored on local disks or object stores such as Amazon S3. Queries are performed with LogQL, a Prometheus‑style query language.

Grafana : visualization platform that can query Loki and Prometheus together, allowing unified dashboards for logs and metrics.

Design Philosophy

Loki is inspired by Prometheus: it is horizontally scalable, highly available, and supports multi‑tenant isolation. The architecture separates log ingestion (Promtail), indexing (label‑only), and storage, enabling cost‑effective retention of large log volumes.

Comparison: PLG vs. ELK/EFK

Indexing and Storage

Elasticsearch stores logs as unstructured JSON documents and builds a full inverted index for each field, enabling powerful full‑text search but incurring higher storage and indexing costs.

Loki stores raw log lines unchanged and indexes only the label set. This lowers cost but requires LogQL for content‑based searches.

Log Collectors

Fluent Bit / Fluentd are general‑purpose log shippers that can forward logs to many back‑ends.

Promtail is purpose‑built for Loki; it integrates with Kubernetes service discovery and applies the same label model used by Prometheus, simplifying correlation between logs and metrics.

Visualization

Kibana offers rich visualizations, machine‑learning anomaly detection, and advanced UI features for Elasticsearch data.

Grafana focuses on time‑series data and can display logs from Loki alongside metrics from Prometheus on the same dashboard.

Choosing a Stack

Select ELK/EFK when full‑text search, complex query DSL, and mature ecosystem features are required. Choose PLG when you need tight integration with Prometheus metrics, lower indexing costs, and a storage‑agnostic log backend suitable for large‑scale Kubernetes deployments.