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

Loggly vs Logstash vs Papertrail

OverviewComparisonAlternatives

Overview

Papertrail
Papertrail
Stacks605
Followers378
Votes273
Loggly
Loggly
Stacks269
Followers304
Votes168
Logstash
Logstash
Stacks12.3K
Followers8.8K
Votes103
GitHub Stars14.7K
Forks3.5K

Loggly vs Logstash vs Papertrail: What are the differences?

## Introduction

## Key Differences Between Loggly, Logstash, and Papertrail

1. **Data Sources**: Loggly primarily focuses on cloud-based log management, Logstash is an open-source data processing pipeline, while Papertrail specializes in real-time log management solution.
   
2. **Architecture**: Loggly offers a cloud-based log management architecture, Logstash is a server-side data processing pipeline, and Papertrail provides real-time log management through a cloud-hosted service.

3. **Integration**: Loggly integrates well with various platforms like AWS, Heroku, and Docker. Logstash integrates seamlessly with Elasticsearch for data indexing and searching, while Papertrail offers straightforward integrations with popular services like AWS, Heroku, and Firebase.

4. **Features**: Loggly offers advanced log analysis features like dynamic field explorer and event graphs, Logstash provides robust data processing capabilities through filters and plugins, while Papertrail offers advanced searching and live tail features for real-time log monitoring.

5. **Scalability**: Loggly provides scalable log management solutions for large enterprise applications, Logstash offers scalability through distributed data processing capabilities, while Papertrail provides real-time log management for both small and large applications.

6. **Ease of Use**: Loggly provides a user-friendly dashboard for log management, Logstash requires more technical expertise for configuration and maintenance, and Papertrail offers a simple and intuitive interface for log analysis and monitoring.

In Summary, the key differences between Loggly, Logstash, and Papertrail lie in their focus on data sources, architecture, integration, features, scalability, and ease of use.

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Detailed Comparison

Papertrail
Papertrail
Loggly
Loggly
Logstash
Logstash

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

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.

Intuitive Web-based log viewer;Powerful command-line tools;Long-term archive (S3);REST API;Team-wide groups and searches;Automated export for tables and charts;Search alerts;Easy SQL analytics (Hadoop);Unlimited systems and users;Encrypted logging
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
-
GitHub Stars
14.7K
GitHub Forks
-
GitHub Forks
-
GitHub Forks
3.5K
Stacks
605
Stacks
269
Stacks
12.3K
Followers
378
Followers
304
Followers
8.8K
Votes
273
Votes
168
Votes
103
Pros & Cons
Pros
  • 85
    Log search
  • 43
    Integrates with Heroku
  • 43
    Easy log aggregation across multiple machines
  • 37
    Simple interface
  • 26
    Backup to S3
Cons
  • 2
    Expensive
  • 1
    External Network Goes Down You Wont Be Logging
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
    Great to meet GDPR goals
Cons
  • 4
    Memory-intensive
  • 1
    Documentation difficult to use
Integrations
Slack
Slack
Heroku
Heroku
PagerDuty
PagerDuty
Amazon S3
Amazon S3
AWS Elastic Beanstalk
AWS Elastic Beanstalk
Amazon RDS
Amazon RDS
OpsGenie
OpsGenie
New Relic
New Relic
Librato
Librato
HipChat
HipChat
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

What are some alternatives to Papertrail, Loggly, Logstash?

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.

Graylog

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.

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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