Logstash review 2026: features, pricing, and pros and cons of Elastic's open-source log pipeline tool, plus top ELK stack alternatives.
Category
DevOps
Pricing
Open Source / Freemium, from Free
Verified
Not yet
Last updated
July 18, 2026
Founded
2009
Headquarters
Amsterdam, Netherlands and Mountain View, California, US (Elastic NV)
APIOpen SourceSelf-Hosted
Overview
Logstash was created in 2009 by Jordan Sissel while working as a systems administrator at DreamHost, built to centralize and parse logs scattered across many servers, and was acquired by Elasticsearch BV (now Elastic NV) in 2013.
It forms the L in the ELK Stack alongside Elasticsearch and Kibana, and remains a free, open-source component of the broader Elastic Stack, which also now includes the lightweight Beats data shippers.
Key Features
Logstash pipelines are built from three stages, inputs, filters, and outputs, letting engineers pull data from sources like files, Beats, syslog, and Kafka, transform it with filters like Grok pattern matching, and route it to one or more destinations.
While closely associated with feeding Elasticsearch, Logstash can output to many systems beyond it, including Kafka, MongoDB, InfluxDB, Datadog, and BigQuery, and supports running multiple named pipelines in parallel.
Pricing
Logstash itself is free and open source, distributed as part of the Elastic Stack with no licensing cost to download, configure, and run pipelines.
Elastic's revenue comes from paid subscription tiers, typically through Elastic Cloud or self-managed subscriptions for Elasticsearch and Kibana, which add advanced security, ML, and enterprise support; Logstash usage does not require a paid subscription.
Key Features
Input plugins — Ingest data from files, the Beats protocol, syslog, HTTP, TCP/UDP, Kafka, RabbitMQ, and databases.
Grok filtering — Parses unstructured log lines into structured, named fields using pattern matching, the most widely used Logstash filter.
Field enrichment filters — Additional filters handle date parsing, geo-IP lookups, JSON parsing, and field mutation or renaming.
Flexible output plugins — Ships processed events to Elasticsearch, Kafka, MongoDB, InfluxDB, Datadog, BigQuery, flat files, and many other destinations.
Multiple pipelines — Runs several independently configured pipelines in a single Logstash instance for isolated, scalable data flows.
Declarative configuration — Pipelines are defined in Logstash's own configuration-file syntax covering the input, filter, and output stages.
Beats integration — Receives events forwarded by lightweight Elastic Beats shippers like Filebeat and Metricbeat for low-overhead collection.
Open-source and extensible — A large ecosystem of community and Elastic-maintained plugins extends inputs, filters, and outputs.
Pros & Cons
Pros
Free and open source with no licensing cost for the core pipeline engine
Mature, extensive plugin ecosystem covering dozens of inputs, filters, and outputs
Flexible enough to route data to destinations well beyond Elasticsearch
Backed by Elastic, a publicly traded company with long-term investment in the product
Well-documented and widely adopted, with a large community knowledge base
Cons
Can be resource-intensive (JVM-based) compared to lighter shippers like Filebeat or Fluent Bit
Complex Grok patterns can be difficult to write and debug
Configuration syntax has a learning curve for teams new to the Elastic Stack
Advanced Elastic Stack features (security, ML, alerting) require a paid subscription
Increasingly positioned behind Beats for simple collection tasks, adding architectural decisions about when to use which tool
Yes, Logstash is free and open source. Paid Elastic subscriptions apply to advanced Elasticsearch and Kibana features, not to running Logstash pipelines themselves.
Who created Logstash
Logstash was created in 2009 by Jordan Sissel while he was a systems administrator at DreamHost, and it was acquired by Elasticsearch BV (now Elastic NV) in 2013.
What does ELK stand for
ELK stands for Elasticsearch, Logstash, and Kibana, the three original open-source components that together form a widely used log analytics and search stack, now expanded with Beats into the broader Elastic Stack.
What is the difference between Logstash and Beats
Beats are lightweight, single-purpose data shippers like Filebeat for simple collection tasks, while Logstash is a heavier pipeline engine used for more complex parsing, enrichment, and multi-destination routing.
Can Logstash send data anywhere besides Elasticsearch
Yes, Logstash output plugins support many destinations beyond Elasticsearch, including Kafka, MongoDB, InfluxDB, Datadog, Amazon SQS, and Google BigQuery.
What is Grok in Logstash
Grok is Logstash's most widely used filter plugin, which parses unstructured log lines into structured, named fields using pattern matching.