Graylog vs Elastic/ELK Stack: The Key Differences to Know

Stanley Ulili
Updated on February 1, 2024

As businesses grow and integrate distributed systems to cope with higher demands, they frequently encounter challenges monitoring system operations and resolving problems. A common strategy to address this involves generating logs, metrics, and traces. Yet, effectively managing these data necessitates an additional tool.

Graylog and Elastic Stack have become prominent options in log management and observability platforms. Both platforms boast advanced search functionalities, data visualization tools, and capabilities for setting alerts to detect anomalies. While they share many features, Graylog and Elastic Stack have distinct differences.

Graylog excels at managing and analyzing log data. Conversely, Elastic Stack is noted for its adaptability in managing various data types, such as metrics, audit records, and traces, in addition to its robust log management features.

This brief comparison only begins to uncover the differences between Graylog and Elastic Stack. This article thoroughly compares their respective features, advantages, and limitations, thereby aiding in well-informed decision-making.

What is the Elastic Stack?

The Elastic Stack, previously known as ELK Stack, is a powerful open-source tool suite for effective log management and observability. Developed by Elastic, it offers a comprehensive solution for gathering, centralizing, and analyzing large volumes of data from diverse sources. Here's an overview of its key components:

  • Elasticsearch: a distributed, RESTful search engine designed to store log data in a manner that optimizes search speed.

  • Logstash: a log collector that retrieves logs from multiple sources, processes them, and sends them to Elasticsearch for storage.

  • Kibana: a web interface used for searching, visualizing, and analyzing logs.

  • Beats: lightweight data shippers capable of collecting and transmitting various data to Logstash or Elasticsearch.

Elasticsearch user interface

The following sections will examine the workings of each component in the Elastic Stack, shedding light on how they effectively work together.

1. Gathering data using log collectors

The first step in the Elastic Stack's data processing pipeline is data ingestion, primarily handled by Logstash. This robust log collector can collect logs from various sources, such as application logs, database logs, and operating systems. Logstash's role includes processing, parsing, enriching, and transforming data before routing it to targets like Elasticsearch.

Alternatively, Beats offers specialized tools for data collection, including logs, metrics, and network packet data, for a more streamlined approach within the Elastic Stack. This data can be either processed further by Logstash or sent straight to Elasticsearch. Some examples of Beats are:

  • Filebeat: Responsible for log collection and shipping.
  • Metricbeat: Dedicated to metric shipping.
  • Packetbeat: Specializes in network packet data transmission.

2. Data indexing in Elasticsearch

Once Elastic or Beats collects data, it is transferred to Elasticsearch for indexing. This step is essential for transforming raw data into a structured format, enabling near-real-time search capabilities.

Elasticsearch excels at indexing various data types, including structured and unstructured text, geospatial, and numerical data. The data is organized into sophisticated data structures and serialized as JSON documents. Elasticsearch uses a data structure called an inverted index, which catalogs each unique word found in any document and also identifies every record in the database where each word appears.

For data retrieval, Elasticsearch employs a JSON-style query language (Query DSL). Here's an example of such a query:

  "query": {
    "bool": {
      "must": [
        { "match": { "title":   "Search" }},
        { "match": { "content": "Elasticsearch" }}
      "filter": [
        { "term":  { "status": "published" }},
        { "range": { "publish_date": { "gte": "2015-01-01" }}}

Furthermore, Elasticsearch offers the flexibility to formulate SQL-like queries for searching and aggregating data within its search environment.

Example of SQL-style search in Elasticsearch

3. Data visualization in Kibana

After Elasticsearch indexes the data, it is presented in Kibana, an interface tailored explicitly for visualizing logs and various data types. Kibana provides visualization tools such as pie charts, line graphs, histograms, or heat maps.

Example of a Kibana dashboard visualization

Users interact with Kibana to analyze logs indexed in Elasticsearch. They can perform queries and apply filters to pinpoint specific data for visualization. Using an intuitive editor, Kibana enables the selection and customization of various visualization formats, like line graphs or pie charts. There's also the convenience of saving these visualizations, incorporating them into dashboards, exporting them as images or PDFs, or sharing them via links.

The advanced visualization capabilities of Kibana significantly enhance data comprehension, spotlighting trends and patterns in an accessible and user-friendly manner, thereby facilitating insightful data analysis and exploration.


  • All the tools in the Elastic Stack are available for free, making it easier to start using them.
  • Configurable for high availability
  • Has managed solutions that include customer service support.
  • Offers monitoring features for Elasticsearch, Logstash, and Kibana to track their health.
  • Supported by an active and extensive community.


  • The learning curve can be steep for new users.
  • Upgrades can be challenging due to the requirement for version uniformity across all components. This is particularly problematic when older versions are required for specific reasons.
  • Logstash demands substantial memory resources, with a minimum requirement of 2GB.

What Is Graylog?

Graylog is a free and open-source centralized log management system. It was designed for efficient aggregation, parsing, and handling of large volumes of log data from various sources like operating systems, applications, and databases. Key components of Graylog's architecture include:

  • OpenSearch/Elasticsearch: storing and indexing logs, enabling fast search operations.
  • Graylog Server: acts as a processing layer, handling log parsing, enrichment, and management.
  • MongoDB: stores operational data and metadata, not the log data.
  • Web Interface: provides a user-friendly interface for searching, analyzing, and visualizing log data.

Graylog simplifies log management, offering powerful search and analysis capabilities through a straightforward interface.

Screenshot of the Graylog architecture

Now, let's explore how Graylog works.

1. Data ingestion through inputs

Graylog is good at handling various data types, including structured, semi-structured, or unstructured logs. It supports multiple formats such as JSON, RAW/Plaintext, Common Event Format (CEF), and RFC 5424 (Syslog).

Graylog uses 'inputs' to receive messages. These inputs are divided into two categories: listener inputs and pull inputs. Listener inputs are set up to wait for applications to send data over TCP or UDP. Common examples of listener inputs are Syslog, Beats, and GELF inputs. Conversely, pull inputs actively fetch data from a specific endpoint before forwarding it to Graylog. Examples of pull inputs include GELF Kafka Input, Syslog AMQP Input, and AWS CloudTrail Input.

2. Data indexing using Elasticsearch/OpenSearch

After receiving data, Graylog moves on to the indexing phase. For this purpose, it uses Elasticsearch or OpenSearch (a fork of Elasticsearch with comparable capabilities). Indexing effectively organizes the data into efficient data structures, significantly improving retrieval speeds.

For searching data, users can access the Graylog web interface, which features a search field with autocomplete functionality:

Guide on searching log data in Graylog

The search syntax used in Graylog closely resembles the Lucene syntax. Here is an example of a more complex search query:

"ssh login" AND
("ssh login" AND ( OR OR _exists_:always_find_me

Once the indexing is complete, the next step in the Graylog process is to visualize the data for extracting valuable insights.

3. Visualizing data with Graylog

Graylog offers a customizable interface for visualizing aggregated data. The Graylog dashboard comprises widgets capable of displaying data through various visualization methods such as bar charts, pie charts, area charts, line charts, data tables, or scatterplots, among others:

Screenshot of Graylog dashboard

Graylog Pros:

  • Graylog is free, making it an accessible option for users.
  • It offers a more straightforward learning curve, with a single interface for data collection, searching, and visualization, unlike solutions like ELK, where each function requires a different tool.
  • Includes a built-in parser for various log types, and users can easily create and immediately test their own parsing rules within the web interface against data in the database.
  • A managed version is available with customer service.

Graylog Cons:

  • Primarily focused on log data, which may be limiting if you need to handle other kinds of data.
  • The dashboard user interface is less refined than Kibana's.
  • A smaller community compared to the Elastic Stack, which may result in fewer resources, less frequent updates, and limited support.

Similarities between Graylog and Elastic Stack

Graylog and Elastic Stack display several similar characteristics:

  • Both are accessible for free and provide managed versions. These managed versions are available through paid monthly plans and come with customer support.
  • Both use Elasticsearch for indexing data. However, it's worth noting that Graylog is confined to using Elasticsearch version 7.x and now focuses solely on integrating with OpenSearch.
  • Both platforms can index log data, perform searches, and offer visualization capabilities essential for thorough data analysis and gaining insights.

Key things to note when choosing between Graylog and Elastic Stack

Lets now look at some of the differences between the tools:

When choosing between Graylog and Elastic Stack, it's essential to consider their differences:

  • The Elastic Stack is designed as a comprehensive big data solution capable of handling a wide range of data, including logs, metrics, and traces. Graylog, on the other hand, is primarily focused on log management, with its features tailored to streamline log analysis.
  • Graylog offers a unified user interface for handling tasks such as data input, parsing, sorting, and visualization. In contrast, the Elastic Stack employs separate tools for each function.

  • In Graylog, index sets can be created in the web UI without direct interaction with Elasticsearch. However, you need to engage with Elasticsearch directly for index management in the Elastic Stack.

  • Graylog supports Elasticsearch up to version 7.10 but is restricted from using newer versions, such as Elasticsearch 8 or higher, due to licensing limitations. The Elastic Stack, however, is compatible with multiple versions of Elasticsearch, including the latest ones.

Choosing between Graylog and Elastic/ELK stack

Choosing between Graylog and the Elastic (ELK) Stack largely depends on your organization's specific needs and resources. Graylog stands out as a robust, user-friendly option for log management, particularly suited if your focus is primarily on log management. On the other hand, if your needs encompass more than log management, such as handling monitoring data, including metrics, the Elastic Stack offers a broader range of functionalities.

Setting up either Graylog or the Elastic Stack can be complex, and if you're looking for a more straightforward solution, consider Better Stack. While Better Stack might offer a partial range of functionalities of Graylog or the Elastic Stack, it is a compelling alternative, especially for companies with limited resources or those who want to avoid the complexities of setting up and managing Elastic Stack or Graylog.

Better Stack provides features like real-time log monitoring, allowing you to watch activities on your platform closely. This real-time display of incoming logs can be crucial for timely responses to issues or tracking system performance:

Screenshot of Live Tail in Better Stack

When you want to gain more insights, Better Stack enables you to create customized dashboards:

Moreover, Better Stack offers the capability to set up custom alerts. This feature ensures that you stay informed about critical events or anomalies in your system. You can receive alerts through various channels like email, phone calls, or webhooks, allowing immediate action in response to any detected issues.

Screenshot of Better Stack configured with the necessary options

Final thoughts

In conclusion, the choice between Graylog and the Elastic Stack depends on your requirements. Graylog is ideal for those who need a user-friendly, efficient log management system with an intuitive interface. It's perfect for straightforward log management tasks.

Conversely, the Elastic Stack is better suited for broader data management needs, including log management, data metrics, etc. It offers scalability and a wide range of analytical tools with its Elasticsearch, Logstash, and Kibana suite. If simplicity in log management is your priority, go for Graylog. If you require a more versatile system with extensive capabilities, the Elastic Stack is the more suitable choice.

Author's avatar
Article by
Stanley Ulili
Stanley is a freelance web developer and researcher from Malawi. He loves learning new things and writing about them to understand and solidify concepts. He hopes that by sharing his experience, others can learn something from them too!
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