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.

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.