# Fluentd vs Logstash: How to Choose in 2026

Logging plays a crucial role in gaining valuable insights into application
behavior and performance. It is essential for identifying and resolving issues,
understanding application usage patterns, and meeting compliance requirements.
However, log collection and aggregation cam often pose significant challenges
due to the sheer volume and rapid generation of log data, diverse data sources
and formats, and the need for efficient processing and storage.

To address these challenges, log collectors come into play. They enable you to
gather and transform data from multiple sources, and securely aggregate them in one
place to ensure effective management of the data. In this article, we will
explore log collectors in detail, focusing on a comparison between two popular
options: [Fluentd](https://www.fluentd.org/architecture) and
[Logstash](https://www.elastic.co/guide/en/logstash/current/introduction.html).
We will analyze the strengths and weaknesses of each tool to determine which one
is the superior choice for you.

Let's get started!

## What are log collectors?

Log collectors are software tools specifically designed to collect log data from
multiple sources and transmit it to a central location for storage and analysis.
They help streamline the process of aggregating log data from diverse sources,
and they often contain a range of functionalities to manipulate the data,
including filtering, formatting, and enriching log entries to enhance their
usefulness and significance.

Although there are numerous log collectors in use, we will only be concentrating
on comparing Fluentd and Logstash in this article, as previously stated.

## What is Fluentd?

[Fluentd](https://betterstack.com/community/guides/logging/fluentd-explained/), a powerful data-collecting tool developed by Treasure Data, is widely
used gather, analyze, and transmit log data from various sources to a
centralized data store. One of its key strengths lies in its pluggable
architecture, which enables seamless integration with a wide range of data
sources and outputs through an extensive library of over 500
community-contributed plugins.

Harnessing the
[streaming paradigm](https://en.wikipedia.org/wiki/Stream_processing), Fluentd
excels in real-time data processing, enabling it to handle and analyze
substantial data volumes with remarkable efficiency and precision. Whether
capturing logs from numerous servers or managing data streams from Internet of
Things (IoT) devices, Fluentd's prowess in real-time processing makes it an
ideal choice for demanding high-throughput scenarios.

![OIP_(1).jpeg](https://imagedelivery.net/xZXo0QFi-1_4Zimer-T0XQ/227c5acb-1f0d-4fd9-6ee4-93861b87ed00/md2x =474x297)

## What is Logstash?

Logstash, an essential component of the
[Elastic Stack](https://www.elastic.co/what-is/elk-stack) (ELK Stack), is a
robust and open-source tool designed for data collection and processing. It is
often used alongside [Beats](https://www.elastic.co/beats) to aggregate and
centralize log data from diverse sources such as servers, applications, network
devices, and cloud platforms.

With its extensive collection of over 50 plugins, a wide range of input options
for gathering data is supported. This flexibility allows organizations to gather
log data from virtually any system or application within their infrastructure.
Through the use of plugins like grok, dissect, and mutate, Logstash can parse,
dissect, and enrich the log data so that relevant fields are extracted and
transformed into the desired format before the data is centralized into
Elasticsearch or other log management systems.

![7a01ea40-7ddc-44a8-a1e9-b8214703872c.webp](https://imagedelivery.net/xZXo0QFi-1_4Zimer-T0XQ/0431c8d3-2154-4c60-e9ad-9ceee3c7e600/lg2x =1072x325)

---

Now that you have an idea of what problems Fluentd and Logstash solve, let us
now delve into a comparative analysis of these two tools. We will evaluate them
based on the following criteria:

1. Platform overview
2. Memory usage/performance
3. Ecosystem and plugins
4. Log parsing
5. Event routing
6. Transport
7. UI & UX design
8. Pricing

| Feature                  | Fluentd | Logstash |
| ------------------------ | ------- | -------- |
| Platform overview        | ✔✔      | ✔✔       |
| Memory usage/performance | ✔✔      | ✔✔       |
| Ecosystem and plugins    | ✔✔      | ✔        |
| Log parsing              | ✔✔      | ✔        |
| Event routing            | ✔✔      | ✔        |
| Transport                | ✔✔      | ✔        |
| UI & UX design           | ✔✔      | ✔        |
| Pricing                  | ✔✔      | ✔✔       |

✔ - partial or limited feature

✔✔ - complete feature

## 1. Platform Overview: Tie

Fluentd is written using a combination of C and Ruby. Its core functionality and
most plugins are written in Ruby, but certain performance-sensitive components,
such as event buffering and low-level I/O operations, are implemented in C for
improved efficiency and reduced overhead. These C extensions enhance Fluentd's
performance and allow it to handle high-volume data streams more effectively.

Fluentd is a cross-platform tool that is compatible with Linux, macOS, and
Windows. It offers flexible deployment options, allowing it to function as
either a standalone service or integrated into existing infrastructure such as
virtual machines or containers.

Logstash, on the other hand, is developed using a combination of
[JRuby](https://www.elastic.co/blog/logstash-lines-support-for-jruby-9-2) and
Java. While the core of Logstash is primarily written in Java, it leverages
JRuby for specific aspects of its implementation, such as optimizing the points
of contact between Ruby and Java. JRuby enables Logstash to use Java libraries,
expanding its range of features and functionalities. This integration with Java
brings performance benefits and facilitates the use of multithreading, allowing
Logstash to efficiently process data.

Logstash is also compatible with Linux, macOS, and Windows-based servers. In
addition to traditional installations, Logstash also supports containerized
deployment through Docker, enabling easy management and portability across
different environments.

In summary, since both tools are open-source and cross-platform, this round can
be considered a tie.

## 2. Memory Usage/Performance: Tie

Fluentd and Logstash are both designed to be lightweight and efficient with
memory usage, however, they have different memory usage patterns which are
dependent on the use case. Fluentd is generally considered to be more
lightweight and resource-efficient than Logstash so it is more suitable for
memory constrained environments.

In terms of performance, they are both capable of handling high data volumes and
scaling appropriately. If you need lighter alternatives to both tools, you
should check out [Fluent Bit](https://github.com/fluent/fluent-bit) and
[Elastic Beats](https://www.elastic.co/beats/) respectively.

Fluent Bit is a lightweight and flexible data collection and processing tool
that is designed to be smaller and faster than Fluentd and is recommended when
using small or embedded applications. It is written in C.

Elastic Beats is a collection of data shippers that sends data to Elasticsearch
or Logstash. It is not a lightweight version of Logstash, but it is efficient
for collecting and processing data from specific sources. It is useful for cases
where only a specific type of data is needed and not the full range of
Logstash's features.

This round ends in a tie for both tools, as they are capable of scaling,
processing vast amounts of data, while also having lightweight alternatives.

## 3. Ecosystem and Plugins: Tie

Both Fluentd and Logstash boast rich ecosystems of plugins to extend their
functionality. These plugins offer a diverse array of input and output options,
enabling data collection from various sources and sending data to different
destinations including servers, applications, devices, files, databases, cloud
services, and many more.

The main difference between the Fluentd and Logstash plugin ecosystems is the
specific plugins that are available for each tool, as well as the difference in
how the plugins are managed.

Fluentd has a decentralized approach to plugins, with a community of
contributors developing and maintaining them individually rather than hosting
them in a single repository. This allows for a wide range of plugins to be
available and makes Fluentd highly flexible and extensible for use in a variety
of scenarios. You can look at the official
[GitHub repository](https://www.fluentd.org/plugins) for available plugins but
there are many other repositories and resources available for finding and
installing additional plugins.

![pluggin_fluentd_1.png](https://imagedelivery.net/xZXo0QFi-1_4Zimer-T0XQ/e7ff8074-5e3b-4cb4-84fc-0fdaee1c4800/lg2x =686x411)

Logstash, on the other hand, has a
[centralized repository](https://github.com/logstash-plugins) where all of the
plugins are managed by the Elastic team, and it currently contains over 250
plugins at the time of writing.

![lOGSTASH_pLUGINS_1.png](https://imagedelivery.net/xZXo0QFi-1_4Zimer-T0XQ/2884c82d-805e-4d06-1bd5-e6f89edfed00/lg2x =1214x643)

This round ends in a tie since both tools provide an extensive selection of
plugins to extend their functionality.

## 4. Log Parsing: Fluentd wins

Fluentd is a powerful log collection tool that excels in parsing both structured
a and unstructured logs. It provides a range of built-in parsers, including
JSON, regex, and CSV, which are useful for parsing log data from a variety of
sources.

In contrast, Logstash relies on plugins for log parsing. It offers a variety of
input, output, and filtering plugins that allow you to extract specific fields
or metadata from log messages using regular expressions or other techniques.

Both Fluentd and Logstash support custom parsing patterns and can automatically
parse common log formats. However, Fluentd gains an advantage with its built-in
parsers, which eliminate the need for additional plugins in most cases. This
convenience makes Fluentd a favorable choice for log parsing.

![logstash-instance-input-filter-output-plugins.png](https://imagedelivery.net/xZXo0QFi-1_4Zimer-T0XQ/8b18cd64-83a2-4052-89a7-66203794d000/public =1583x612)

## 5. Event Routing: Fluentd wins

Event routing involves directing events or data to some destination based on
specific criteria. Both Fluentd and Logstash provide event routing capabilities,
allowing for the collection, processing, and routing of data from various
sources to different destinations based on specific criteria.

Fluentd uses tags to apply routing rules to events. This means events are routed
to specific destinations based on the tags assigned to them. On the other hand,
Logstash uses conditional statements, such as if-then-else statements, to
control data flow through its pipelines. While providing greater flexibility, it
may require more configuration and can be more intricate to set up and maintain
compared to Fluentd's simpler tag-based method.

Therefore, Fluentd wins this round due to its straightforward tagging approach,
making it easier to set up and manage event routing.

## 6. Transporting data: Fluentd wins

When it comes to transporting data, Fluentd offers a variety of output plugins
that allow you to move collected data to various destinations, including local
files, remote servers, cloud storage services, message queues, and more. It also
features a flexible and configurable buffer system that can store events in
memory, on disk, or in the cloud. This helps prevent data loss during restarts.

Logstash also uses output plugins for transporting data to different
destinations, similar to Fluentd. However, it relies on an in-memory queue with
a default capacity of 20 events to buffer data during collection and processing.
This queue helps handle spikes in event volume and reduces the load on output
destinations.

Notably, the in-memory queue used by Logstash is not persistent, meaning that
queued events are lost during restarts. To ensure persistence, Logstash relies
on external queues like Redis or Kafka.

Fluentd wins this round since it doesn't require additional tools to achieve
persistence.

## 7. User interface: Fluentd wins

Both Fluentd and Logstash focus on functionality and efficiency in their user
interfaces rather than aesthetics. Fluentd has a simpler and more streamlined
UI, with a web-based dashboard for viewing logs and metrics and a simple
configuration file syntax for setting up data collection.

![fluentdui_(1)_1.png](https://imagedelivery.net/xZXo0QFi-1_4Zimer-T0XQ/59abf3fb-fbb4-4c36-b035-8e8798221800/md2x =1049x769)

Logstash itself does not have a built-in graphical user interface (GUI) or
web-based interface for configuring and monitoring pipelines. Instead, Logstash
is typically configured using a text-based configuration file that defines the
inputs, filters, and outputs for the pipeline.

However, Logstash can be integrated with other tools that provide a GUI for
pipeline management and monitoring. For example, the Elastic Stack (which
includes Logstash, Elasticsearch, and Kibana) provides a centralized platform
for data ingestion, storage, and analysis, with Kibana serving as the primary
GUI for managing and monitoring Logstash pipelines.

![orig_(1)_1.png](https://imagedelivery.net/xZXo0QFi-1_4Zimer-T0XQ/1f2794a6-4d09-4399-c313-b9861d3c8000/public =1090x545)

In terms of user experience (UX), Fluentd is a good choice for those new to data
collection and processing tools or for organizations that need to quickly set up
and start collecting data, while Logstash is better suited for more advanced
data processing tasks.

Fluentd wins this round.

## 8. Pricing: Tie

The developers of Fluentd provide a free and open-source version under the
Apache License 2.0. This means that there are no subscription fees or costs
associated with using the tool. However, if you intend to incorporate Fluentd
into a
[comprehensive logging and data analysis solution](https://betterstack.com/logs),
additional software or services may need to be purchased. For example, if you
wish to store and analyze data collected by Fluentd using
[Better Stack](https://betterstack.com/logs), a subscription may be required.

Logstash also offers a free and open-source version, along with paid versions
that provide additional features and support options. Elastic (the company
behind Logstash) provides different pricing tiers for their commercial
offerings, allowing users to choose the level of support and features that best
suit their requirements.

![Screenshot from 2023-06-13 14-04-33.png](https://imagedelivery.net/xZXo0QFi-1_4Zimer-T0XQ/a7aa6eac-bfdf-4df7-84e7-879afb038200/md1x =2422x1171)

This round is considered a tie since both tools offer open-source versions at no
cost while providing paid options for additional features and support.

## Aggregating your logs with Better Stack

Fluentd and Logstash are both effective at collecting and forwarding logs, but
managing and processing logs becomes increasingly challenging as the log volume
grows. To address this challenge, [integrating a reliable log management
tool](https://betterstack.com/community/guides/logging/log-aggregation/) is essential.

[Better Stack](https://betterstack.com/logs) provides a powerful cloud-based log
management tool that excels at aggregating logs from
[Fluentd](https://betterstack.com/docs/logs/fluentd/) and
[Logstash](https://betterstack.com/docs/logs/logstash/). It offers advanced
features such as log parsing, filtering, and correlation, making it a suitable
choice for efficiently managing large volumes of logs.

![Screenshot 2023-06-13 at 13-54-57 Live tail Better Stack.png](https://imagedelivery.net/xZXo0QFi-1_4Zimer-T0XQ/902c0d15-6fbc-4686-d23d-ef3f55070200/md1x =2840x1696)

Better Stack offers effortless integration with various popular tools, including
[Slack](https://betterstack.com/slack-incident-management), Amazon S3, and others. It also enables email notifications and webhooks,
allowing users to receive log notifications and alerts directly in their
preferred communication channels.

These seamless integrations help to simplify the log management process and
enable quick issue resolution. With Better Stack's versatile notification
options, users can easily stay on top of any critical events related to their
system logs, no matter where they are or what communication channel they prefer.

![Screenshot 2023-06-13 at 13-57-43 Integrations Better Stack.png](https://imagedelivery.net/xZXo0QFi-1_4Zimer-T0XQ/0613901c-27e9-4cc7-c7c9-3bc49cf6fc00/lg2x =2836x1703)

One of the key advantages of Better Stack is its ability to notify users of any
irregular volumes of logs generated by their services or any other specified
conditions, which can be immensely helpful in detecting problems and fixing them
quickly.

Another advantage of Better Stack is its affordability. It is more
cost-effective when compared to other log management solutions such as the
Elastic Stack, which utilizes Logstash as its log collector. With Better Stack,
you can enjoy a reliable and robust log management solution without breaking the
bank.

## Final thoughts

Fluentd and Logstash are two of the most popular open-source log processing and
analysis tools available. The former is known for its low memory footprint and
high performance, making it a popular choice for log collection and analysis in
real-time environments. Logstash, on the other hand, offers a wider range of log
processing capabilities and first-class integration with the rest of the Elastic
Stack for log management and analysis.

The correct choice between the two would depend on your specific requirements,
such as the available resources, desired performance levels, and the complexity
of data processing tasks. We hope that is comparison has given you an idea of
what tool is better for your needs. However, we recommend that you carefully
evaluate and benchmark both tools before making a final decision.

Thanks for reading, and happy logging!