Tenzir vs. Logstash
Where Logstash Taps Out, Tenzir Scales Up
Logstash laid the groundwork for structured log routing. Tenzir builds on that legacy with composable, typed pipelines made for today’s security data landscape.
Introduction
Logstash helped define what a data pipeline could be—powerful, plugin-based, and deeply tied to Elasticsearch. But for modern security teams, log collection on stilts isn’t the end of the road. Today’s pipelines demand context, composability, and clarity—from the edge to your SIEM or data lake, in real time and at scale. That’s where Tenzir comes in: a new foundation for pipeline logic, built from the ground up for security data operations.
TL;DR: Logstash is a log shipper with a plugin heart. Tenzir is a composable pipeline engine with a security-first brain. If you're wrangling YAML and regex to keep up with evolving use cases, it might be time to try something declarative, typed, and built for context-rich pipelines.
Quick Comparison
Logstash
Primary Focus
Observability ETL for the Elastic Stack
Security Data Operations & Data Engineering
Use Cases
Log Aggregation, Transformation, Routing into Elasticsearch
Cost Reduction, Threat Detection, Enrichment, Security Data Lakes
Pipeline Language
YAML-based configuration with plugin stages
TQL: declarative, unified for stream & batch processing
Architecture
Rigid input → filter → output pipeline with stages
Decentralized nodes with central platform for control
Deployment
Self-hosted or Elastic Cloud (with limited automation)
Self-managed, cloud-native, air-gapped capable
Extensibility
Ruby-based plugin ecosystem
User-defined operators, open source content (package library), C++ plugin SDK
Pricing Model
Open source (Apache 2 & Elastic), included in Elastic Cloud tiers
Open Source (BSD 3-Clause), Community, Professional, and Enterprise editions
Core Differences
Product Philosophy
Logstash
Logstash was designed as a log pipeline component within the Elastic Stack, focusing on ingesting and transforming observability data for indexing into Elasticsearch.
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Tenzir rethinks data pipelines from the ground up for SecOps workflows. It supports enrichment, detection, and routing natively—giving you declarative, inspectable pipelines that fit into modern infrastructure-as-code practices.
Architecture & Deployment
Logstash
Logstash is a monolithic process, typically deployed as a standalone instance that requires a JVM. It scales by duplication and relies on external orchestration for resilience.
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Tenzir has a node-based architecture, where each node runs local pipelines and connects (optionally) to a central platform. You can deploy nodes individually, at the edge, or fully air-gapped in data centers.
Pipelines
Logstash
Logstash pipelines follow a static flow of execution, split into three stages: input, filter, and output. Each stage offers numerous plugins.
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Tenzir pipelines are a sequence of operators and can include nested pipelines to form a DAG-style execution model. The pipelines process both unstructured data (bytes) and structured data (events).
Data Model
Logstash
Logstash's data model is JSON. Every event is a JSON object and can have a dynamic structure.
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Tenzir's data model is similar to JSON when it comes to nesting of records and lists. The type system is richer and includes first-class types for time, duration, IP addresses, subnets, etc., making it more natural to work with security data.
Language
Logstash
Logstash comes with custom domain-specific language to compose input, filters, and output stages. Each stage offers conditional logic using if/else, but lacks composability.
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The Tenzir Query Language (TQL) is a powerful data pipeline language that comes with a streaming execution engine powered by Apache Arrow. Native OCSF support, a stateful runtime for enrichment, and dedicated detection operators make it a perfect fit for SecOps use cases.
Logstash got you here. Tenzir gets you further.
Start building pipelines made for scale, security, and clarity.
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