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  1. Stackups
  2. DevOps
  3. Log Management
  4. Log Management
  5. Graylog vs Loggly vs Logstash

Graylog vs Loggly vs Logstash

OverviewComparisonAlternatives

Overview

Loggly
Loggly
Stacks269
Followers304
Votes168
Logstash
Logstash
Stacks12.3K
Followers8.8K
Votes103
GitHub Stars14.7K
Forks3.5K
Graylog
Graylog
Stacks595
Followers711
Votes70
GitHub Stars7.9K
Forks1.1K

Graylog vs Loggly vs Logstash: What are the differences?

### Introduction
When it comes to log management solutions, Graylog, Loggly, and Logstash are popular choices. Each of these tools offers unique features and capabilities for processing and analyzing log data.

1. **Data Collection Methods**: Graylog and Loggly are both cloud-based log management solutions, while Logstash is an open-source data collection tool. Graylog and Loggly allow users to send log data directly to their platforms, whereas Logstash requires users to set up data collection pipelines.

2. **Storage Options**: Loggly offers limited storage for log data, especially in lower-tier subscription plans, while Graylog allows users to store log data on their own infrastructure or in the cloud. Logstash, being self-hosted, gives users complete control over where and how log data is stored.

3. **Search and Query Capabilities**: Graylog provides a user-friendly search interface with powerful query abilities, making it easy to analyze log data quickly. Loggly also offers advanced search features, but with a more streamlined interface. Logstash, on the other hand, requires users to write custom queries in its configuration files.

4. **Alerting and Notification**: Graylog comes with built-in alerting and notification features, allowing users to set up alerts based on certain log event criteria. Loggly also offers alerting capabilities with customizable thresholds. Logstash, being a data processing tool, lacks built-in alerting functionality and requires integration with other tools for alerting.

5. **Scalability and Performance**: Graylog and Loggly are designed to be scalable cloud-based solutions, offering high performance for log data processing. Logstash's performance and scalability depend on the infrastructure it is deployed on, making it suitable for self-hosted environments with appropriate hardware resources.

6. **Community and Support**: Graylog and Logstash have strong community support due to their open-source nature, with active development and frequent updates. Loggly, being a commercial product, offers dedicated support but may lack the same level of community-driven resources.

In Summary, Graylog, Loggly, and Logstash offer different approaches to log management, with variations in data collection methods, storage options, search capabilities, alerting features, scalability, and community support.

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Detailed Comparison

Loggly
Loggly
Logstash
Logstash
Graylog
Graylog

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

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.

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.

See what your application is doing during development;Catch exceptions and track execution flow;Graph and report on the number of errors generated;Search across multiple deployments;Narrow down on specific issues;Investigate root cause analysis;Monitor for specific events and errors;Trigger alerts based on occurrences and investigate for resolutions;Track site traffic and capacity;Measure application performance;A rich set of RESTful APIs which make data from applications easy to query;Supports oAuth authentication for third-party applications development (View our Chrome Extension with NewRelic);Developer ecosystem provides libraries for Ruby, JavaScript, Python, PHP, .NET and more
Centralize data processing of all types;Normalize varying schema and formats;Quickly extend to custom log formats;Easily add plugins for custom data source
-
Statistics
GitHub Stars
-
GitHub Stars
14.7K
GitHub Stars
7.9K
GitHub Forks
-
GitHub Forks
3.5K
GitHub Forks
1.1K
Stacks
269
Stacks
12.3K
Stacks
595
Followers
304
Followers
8.8K
Followers
711
Votes
168
Votes
103
Votes
70
Pros & Cons
Pros
  • 37
    Centralized log management
  • 25
    Easy to setup
  • 21
    Great filtering
  • 16
    Live logging
  • 15
    Json log support
Cons
  • 3
    Pricey after free plan
Pros
  • 69
    Free
  • 18
    Easy but powerful filtering
  • 12
    Scalable
  • 2
    Kibana provides machine learning based analytics to log
  • 1
    Well Documented
Cons
  • 4
    Memory-intensive
  • 1
    Documentation difficult to use
Pros
  • 19
    Open source
  • 13
    Powerfull
  • 8
    Well documented
  • 6
    Alerts
  • 5
    Flexibel query and parsing language
Cons
  • 1
    Does not handle frozen indices at all
Integrations
Heroku
Heroku
Amazon S3
Amazon S3
New Relic
New Relic
AWS CloudTrail
AWS CloudTrail
Engine Yard Cloud
Engine Yard Cloud
Cloudability
Cloudability
Kibana
Kibana
Elasticsearch
Elasticsearch
Beats
Beats
GitHub
GitHub

What are some alternatives to Loggly, Logstash, Graylog?

Papertrail

Papertrail

Papertrail helps detect, resolve, and avoid infrastructure problems using log messages. Papertrail's practicality comes from our own experience as sysadmins, developers, and entrepreneurs.

Logmatic

Logmatic

Get a clear overview of what is happening across your distributed environments, and spot the needle in the haystack in no time. Build dynamic analyses and identify improvements for your software, your user experience and your business.

Logentries

Logentries

Logentries makes machine-generated log data easily accessible to IT operations, development, and business analysis teams of all sizes. With the broadest platform support and an open API, Logentries brings the value of log-level data to any system, to any team member, and to a community of more than 25,000 worldwide users.

Sematext

Sematext

Sematext pulls together performance monitoring, logs, user experience and synthetic monitoring that tools organizations need to troubleshoot performance issues faster.

Fluentd

Fluentd

Fluentd collects events from various data sources and writes them to files, RDBMS, NoSQL, IaaS, SaaS, Hadoop and so on. Fluentd helps you unify your logging infrastructure.

ELK

ELK

It is the acronym for three open source projects: Elasticsearch, Logstash, and Kibana. Elasticsearch is a search and analytics engine. Logstash is a server‑side data processing pipeline that ingests data from multiple sources simultaneously, transforms it, and then sends it to a "stash" like Elasticsearch. Kibana lets users visualize data with charts and graphs in Elasticsearch.

Sumo Logic

Sumo Logic

Cloud-based machine data analytics platform that enables companies to proactively identify availability and performance issues in their infrastructure, improve their security posture and enhance application rollouts. Companies using Sumo Logic reduce their mean-time-to-resolution by 50% and can save hundreds of thousands of dollars, annually. Customers include Netflix, Medallia, Orange, and GoGo Inflight.

Splunk

Splunk

It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.

LogDNA

LogDNA

The easiest log management system you will ever use! LogDNA is a cloud-based log management system that allows engineering and devops to aggregate all system and application logs into one efficient platform. Save, store, tail and search app

AWS CloudTrail

AWS CloudTrail

With CloudTrail, you can get a history of AWS API calls for your account, including API calls made via the AWS Management Console, AWS SDKs, command line tools, and higher-level AWS services (such as AWS CloudFormation). The AWS API call history produced by CloudTrail enables security analysis, resource change tracking, and compliance auditing. The recorded information includes the identity of the API caller, the time of the API call, the source IP address of the API caller, the request parameters, and the response elements returned by the AWS service.

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