Observability in Kubernetes – A Comprehensive Comparison

Kubernetes has become the standard for running containerized applications in recent years. As its adoption grows, so does the need to monitor clusters and applications transparently, traceably, and efficiently. Observability – the ability to reconstruct the state of a system from external signals such as logs, metrics, and traces – is a central concept for this purpose.

This article provides a well-founded overview of open-source solutions in the field of metrics and log monitoring, comparing their strengths and weaknesses in terms of scalability, performance, and maintainability, and exploring various data ingestion methods. The goal is to provide clear guidance for architects and operations teams looking to build a future-proof observability strategy in Kubernetes.

Observability: Core Components

Observability encompasses three dimensions:

1. Metrics: Quantitative measurements, usually time series (CPU, RAM, request latencies, etc.)
2. Logs: Textual event logs
3. Traces: Distributed tracing across system boundaries

This article focuses on metrics and logs, as these are usually the first to be set up in Kubernetes environments.

Metrics: Prometheus, Grafana Mimir, and VictoriaMetrics

Prometheus

Prometheus is the de facto standard for metrics in Kubernetes. It was specifically developed for cloud-native architectures and integrates seamlessly into the Kubernetes ecosystem.

Advantages

• Establishment: Standard in Kubernetes, large community.
• Easy Integration: Native service discovery in Kubernetes.
• Large Ecosystem: Exporters for almost all services.

Disadvantages

• Scalability: Single Prometheus instances hit limits in large clusters.
• Long-term Storage: Limited without external systems (e.g., Thanos, Cortex).

Example configuration for Prometheus scraping:

scrape_configs:
  - job_name: 'kubernetes-nodes'
    kubernetes_sd_configs:
      - role: node

Grafana Mimir

Mimir is a horizontally scalable metrics backend, derived from Cortex. It offers Prometheus-compatible APIs but focuses on high availability and scalability.

Advantages

• Horizontal Scaling: Suitable for very large environments.
• Prometheus-compatible: Drop-in replacement.
• Integration: Closely coupled with Grafana.

Disadvantages

• Complexity: Cluster operation requires more components.
• Resource Demand: Higher than a standalone Prometheus instance.

VictoriaMetrics

VictoriaMetrics is a high-performance time-series database project focused on efficiency and ease of use.

Advantages

• Performance: Very fast ingestion and queries.
• Resource Efficient: Low memory consumption.
• Simple Architecture: Fewer moving parts than Mimir.

Disadvantages

• Smaller Community than Prometheus/Grafana.
• Ecosystem: Fewer additional tools.

Logs: Elasticsearch, Grafana Loki, and VictoriaLogs

Elasticsearch

Elasticsearch has been the standard in log storage for years.

Advantages

• Powerful Query Language (Lucene)
• Broad Integration (ELK/Elastic Stack)
• Scalability: Proven in large environments

Disadvantages

• Resource Intensive: High memory and CPU demand.
• Maintenance Intensive: Complex cluster maintenance.

Grafana Loki

Loki takes a different approach: logs are indexed with labels like Prometheus, not the entire text.

Advantages

• Resource Efficient: Lower index storage requirements.
• Prometheus-like: Labels, easy integration.
• Grafana Integration: Seamless in dashboards.

Disadvantages

• Limited Full-text Search: Focus on label-based queries.
• Complexity: Additional components needed in large clusters.

VictoriaLogs

VictoriaLogs is the log-specific extension of VictoriaMetrics.

Advantages

• Efficiency: Low resource usage.
• Simplicity: Fewer components.
• Integration: Can be combined with existing VM stacks.

Disadvantages

• Younger Project: Smaller community.
• Feature Set: Fewer features than Elasticsearch.

Data Ingestion Methods

Metrics

Prometheus Scrape

• Standard mechanism: Prometheus polls endpoints.
• Advantage: Simple, established.
• Disadvantage: Pull-based, high overhead with many targets.

vmagent

• Agent from VictoriaMetrics.
• Advantage: Resource optimized.
• Disadvantage: Smaller community.

Grafana Alloy

• Grafana Alloy is a new agent for metrics and logs.
• Advantage: Unified data pipeline.
• Disadvantage: Relatively new.

OpenTelemetry Collector

• Universal standard for telemetry data: OpenTelemetry.
• Advantage: Metrics, logs, and traces in one.
• Disadvantage: Complex operation.

Example: Prometheus scraping an app

annotations:
  prometheus.io/scrape: "true"
  prometheus.io/port: "8080"

Logs

Promtail (for Loki)

• Promtail tails local logs and sends them to Loki.
• Advantage: Easy integration.
• Disadvantage: Tightly coupled to Loki.

Vector

• Vector is a high-performance log agent.
• Advantage: Very flexible, various sinks.
• Disadvantage: Higher configuration effort.

Grafana Alloy

• Grafana Alloy can process logs like metrics.
• Advantage: Unified tooling.
• Disadvantage: Not yet widespread.

OpenTelemetry Collector

• Logs, metrics, and traces via OpenTelemetry.
• Advantage: Standardized, many sinks.
• Disadvantage: Complexity in setup.

Example: Promtail Config

scrape_configs:
  - job_name: system
    static_configs:
      - targets:
          - localhost
        labels:
          job: varlogs
          __path__: /var/log/*log

Metrics Backends Comparison

Log Backends Comparison

Conclusion

Observability in Kubernetes is not a luxury but a necessary prerequisite for stable, secure, and scalable operations. The choice of the right stack depends heavily on your own requirements:

• Prometheus + Loki: The pragmatic standard for medium-sized environments.
• Mimir + Loki: For highly scaled enterprise setups with a Grafana focus.
• VictoriaMetrics + VictoriaLogs: When performance and resource efficiency are paramount.
• Elasticsearch: Useful where powerful full-text search is indispensable.

On the agent side:

• Prometheus Scrape/Promtail for simplicity.
• Vector/OpenTelemetry for more complex pipelines.
• Grafana Alloy as a modern unified solution.

In the long run, there is hardly any way around OpenTelemetry if logs, metrics, and traces are to be combined. Until then, the mix of Prometheus, Loki, and complementary tools remains the most stable choice.