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Graylog vs Loggly: What are the differences?

Introduction

Graylog and Loggly are both popular log management solutions used for aggregating, analyzing, and storing log data. While they serve similar purposes, there are key differences between the two platforms.

  1. Integration Flexibility: Graylog offers more flexibility in terms of integrating with various sources. It supports a wide range of log collecters such as Elasticsearch, Beats, NXLog, and more. On the other hand, Loggly has limited integrations with a smaller set of log collectors.

  2. Deployment Options: Graylog provides both cloud-based and on-premises deployment options. This allows users to choose the most suitable option based on their preferences and requirements. Loggly, on the other hand, is primarily a cloud-based log management solution, which may not be suitable for organizations with strict compliance requirements or those preferring on-premises deployments.

  3. Query Language: Graylog utilizes a powerful query language called Graylog Query Language (GQL), which allows users to perform complex searches, create custom dashboards, and build visualizations. Loggly, on the other hand, uses a simplified query syntax based on Apache Lucene, which may not offer the same level of query flexibility and customization.

  4. Data Retention: Graylog provides more flexible data retention options, allowing users to define their own retention policies based on their specific needs. Loggly, on the other hand, has predefined retention periods based on the plan chosen, which may not align with the varying retention requirements of different organizations.

  5. Alerting Capabilities: Graylog offers advanced alerting features, allowing users to configure custom alert conditions and notifications based on log data. Loggly also supports alerting, but it has more limited options and may not provide the same level of granularity as Graylog.

  6. User Interface: Graylog provides a comprehensive and customizable user interface, giving users more control over how they interact with the log data. Loggly, on the other hand, has a simpler interface with less customization options, which may be more suitable for users looking for a streamlined logging experience.

In summary, Graylog offers more integration flexibility, deployment options, query capabilities, data retention options, advanced alerting features, and a comprehensive user interface compared to Loggly. However, Loggly may be a suitable choice for users looking for a simpler, cloud-based log management solution.

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Pros of Graylog
Pros of Loggly
  • 19
    Open source
  • 13
    Powerfull
  • 8
    Well documented
  • 6
    Alerts
  • 5
    User authentification
  • 5
    Flexibel query and parsing language
  • 3
    User management
  • 3
    Easy query language and english parsing
  • 3
    Alerts and dashboards
  • 2
    Easy to install
  • 1
    A large community
  • 1
    Manage users and permissions
  • 1
    Free Version
  • 37
    Centralized log management
  • 25
    Easy to setup
  • 21
    Great filtering
  • 16
    Live logging
  • 15
    Json log support
  • 10
    Log Management
  • 10
    Alerting
  • 7
    Great Dashboards
  • 7
    Love the product
  • 4
    Heroku Add-on
  • 2
    Easy to setup and use
  • 2
    Easy setup
  • 2
    No alerts in free plan
  • 2
    Great UI
  • 2
    Good parsing
  • 2
    Powerful
  • 2
    Fast search
  • 2
    Backup to S3

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Cons of Graylog
Cons of Loggly
  • 1
    Does not handle frozen indices at all
  • 3
    Pricey after free plan

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What is Graylog?

Centralize and aggregate all your log files for 100% visibility. Use our powerful query language to search through terabytes of log data to discover and analyze important information.

What is Loggly?

It is a SaaS solution to manage your log data. There is nothing to install and updates are automatically applied to your Loggly subdomain.

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What companies use Graylog?
What companies use Loggly?
See which teams inside your own company are using Graylog or Loggly.
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Splunk
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Logstash
Logstash is a tool for managing events and logs. You can use it to collect logs, parse them, and store them for later use (like, for searching). If you store them in Elasticsearch, you can view and analyze them with Kibana.
Kibana
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Elasticsearch
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Nagios
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