Elasticsearch vs Logstash: Key Differences & When to Use Each

Comprehensive side-by-side comparison of features, pricing, and metrics

Key Differences

Compare Elasticsearch and Logstash across features, pricing, integrations, and community metrics. Elasticsearch / Logstash.

Feature

Elasticsearch

Database

Logstash

Logging

Side-by-side comparison of developer tools
Distributed RESTful search and analytics engine
Server-side data processing pipeline
GitHub Stars
⭐ 76,630
⭐ 14,845
Contributors
👥 2,471
👥 613
Pricing
✓ Free
Enterprise: Contact sales
✓ Free
Enterprise: Contact sales
Languages
Java
Java
Features
  • • Elasticsearch
  • • Java
  • • Search Engine
  • • Etl Framework
  • • Java
  • • Jruby
  • • Logging
  • • Real Time Processing
Integrations
  • • elasticsearch
No integrations listed
Momentum Score
66/100 (stable)
47/100 (stable)
Community Health
95/100 (excellent)
34/100 (needs-attention)
Maturity Index
93/100 (mature)
50/100 (emerging)
Innovation Score
65/100 (progressive)
52/100 (evolving)
Risk Score (higher is safer)
87/100 (minimal)
53/100 (low)
Developer Experience
54/100 (needs-improvement)
54/100 (needs-improvement)
Links
🌐 Visit Website View on GitHub
🌐 Visit Website View on GitHub

Elasticsearch Strengths

  • ✓ More popular (76,630 stars)
  • ✓ Larger community (2,471 contributors)

Logstash Strengths

  • ✓ More features (5 listed)

When to Use Elasticsearch vs Logstash

Use Elasticsearch when its strengths align better with your stack and team needs, and choose Logstash when its ecosystem, integrations, or cost profile is a better fit.

Related comparisons

Elasticsearch vs Redis → Logstash vs Vector → Elasticsearch vs ClickHouse → Logstash vs Fluentd → Elasticsearch vs TiDB → Logstash vs Fluent Bit → Elasticsearch vs CockroachDB → Logstash vs Redis → Elasticsearch vs InfluxDB → Logstash vs Grafana → Elasticsearch vs MongoDB → Logstash vs Prometheus →

More Comparisons

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Data source: GitHub API

Last updated: 5/4/2026