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

Loggly vs Serilog

OverviewComparisonAlternatives

Overview

Loggly
Loggly
Stacks269
Followers304
Votes168
Serilog
Serilog
Stacks2.1K
Followers107
Votes1
GitHub Stars7.8K
Forks840

Loggly vs Serilog: What are the differences?

Introduction

In this article, we will compare Loggly and Serilog and outline the key differences between these two logging frameworks. Both Loggly and Serilog are widely used for application logging, but they have distinct features that set them apart. Let's dive into the differences!

  1. Integration with Logging Platforms: Loggly is a cloud-based log management service that allows you to centralize and analyze logs in real time. It provides out-of-the-box integration with popular logging platforms like Logstash, Fluentd, and AWS CloudWatch. On the other hand, Serilog is a .NET logging library that focuses on structured logging. It can be easily integrated into various platforms such as ASP.NET, .NET Core, and Xamarin.

  2. Logging Approaches: Loggly primarily follows the traditional logging approach, where log statements are written to different log files or streams. It provides powerful search and filtering capabilities to make log exploration easier. Serilog, on the other hand, adopts a structured logging approach. It encourages developers to log structured data using key-value pairs, making it easier to analyze and aggregate logs.

  3. Flexibility and Customization: Loggly offers a wide range of built-in features for log analysis, such as dashboards, real-time alerts, and search query language. It provides a user-friendly interface for configuration and customization. In contrast, Serilog provides a highly flexible and extensible logging framework. It allows developers to build their own sinks, enrichers, and formatters, providing a more tailored logging experience.

  4. Performance and Scalability: Loggly is a cloud-based service that automatically scales resources based on log volume, making it suitable for handling large log volumes and high traffic applications. Serilog, being a library, relies on the hosting environment for scalability. It can be scaled horizontally by leveraging distributed logging frameworks like Seq or by routing logs to a scalable storage backend.

  5. Pricing Model: Loggly has a pricing model based on log volume, where you pay based on the amount of data ingested per day or month. It offers various plans suitable for different log volumes and retention periods. Serilog, being an open-source library, is free to use and does not have any direct costs associated with it. However, additional costs may be incurred if you choose to use third-party services for log storage or analysis.

  6. Language and platform support: Loggly supports log collection from various sources and platforms, including different programming languages like Java, Python, and Ruby. It provides SDKs and libraries for easy integration. Serilog, being a .NET logging library, primarily targets the .NET ecosystem. It offers support for different .NET platforms like ASP.NET, .NET Core, and Xamarin, and provides specific packages for integration with these platforms.

In summary, Loggly is a cloud-based log management service with extensive integration options, while Serilog is a flexible .NET logging library that promotes structured logging. Loggly offers a user-friendly interface and powerful log analysis features, while Serilog provides a high level of customization and extensibility. Loggly's pricing model is based on log volume, whereas Serilog is free to use. Finally, Loggly supports log collection from multiple platforms and languages, while Serilog is primarily focused on the .NET ecosystem.

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Detailed Comparison

Loggly
Loggly
Serilog
Serilog

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

It provides diagnostic logging to files, the console, and elsewhere. It is easy to set up, has a clean API, and is portable between recent .NET platforms.

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
Structured logging; .NET logger
Statistics
GitHub Stars
-
GitHub Stars
7.8K
GitHub Forks
-
GitHub Forks
840
Stacks
269
Stacks
2.1K
Followers
304
Followers
107
Votes
168
Votes
1
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
  • 1
    It's a logging library
Cons
  • 1
    They are two different things
  • 1
    You can't compare this to seq
Integrations
Heroku
Heroku
Amazon S3
Amazon S3
New Relic
New Relic
AWS CloudTrail
AWS CloudTrail
Engine Yard Cloud
Engine Yard Cloud
Cloudability
Cloudability
.NET
.NET
C++
C++
LogRocket
LogRocket
ASP.NET
ASP.NET

What are some alternatives to Loggly, Serilog?

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.

Logstash

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.

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.

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