Datadog vs Loggly

Overview

Loggly

Stacks269

Followers304

Votes168

Datadog

Stacks9.8K

Followers8.2K

Votes861

Datadog vs Loggly: What are the differences?

Introduction:

In this article, we will explore the key differences between Datadog and Loggly, two popular log management and monitoring solutions. Both tools offer valuable features and capabilities to help organizations manage and analyze their logs effectively. However, there are some notable differences between them that can influence the decision-making process for selecting the right tool for specific requirements.

Data Collection and Aggregation: Datadog provides a comprehensive platform for collecting and aggregating logs, metrics, and traces, offering a full-stack observability solution. On the other hand, Loggly primarily focuses on log management and analytics, providing centralized log collection and aggregation capabilities.
Search and Query Capabilities: Datadog offers a powerful search and query language, allowing users to explore and analyze logs in a user-friendly manner. It provides advanced filtering and aggregation options, enabling users to extract valuable insights from logs efficiently. Loggly, on the other hand, offers a simple yet effective search functionality, allowing users to search logs using keywords and basic filters.
Real-time Monitoring and Alerting: Datadog excels in real-time monitoring and alerting capabilities, enabling users to set up alerts based on log data, metrics, and custom events. It provides real-time dashboards and visualizations to track log data and metrics effectively. Loggly also offers alerting features, but it may not be as comprehensive as that of Datadog.
Integration Ecosystem: Datadog has a vast integration ecosystem, allowing seamless integration with various tools and services such as AWS, Azure, Kubernetes, and many others. It offers native integrations and APIs to collect logs from different sources effectively. Loggly also provides integration options, but the range of integrations may be somewhat limited compared to Datadog.
Scalability and Performance: Datadog is known for its scalability and performance, handling large volumes of logs and metrics efficiently. It can handle massive workloads and offers a reliable and robust infrastructure to support high-throughput log data processing. Loggly is also scalable but may not have the same level of performance as Datadog for extremely high volumes of log data.
User Interface and User Experience: Datadog provides a user-friendly and intuitive interface, making it easier for users to navigate and explore log data, metrics, and alerts. It offers interactive dashboards and visualizations, providing a holistic view of log data. Loggly also provides a user-friendly interface but may not have the same level of advanced visualizations and interactive features as Datadog.

In summary, Datadog offers a comprehensive platform for collecting, analyzing, and visualizing logs, metrics, and traces, with strong capabilities in real-time monitoring and alerting. Loggly, on the other hand, primarily focuses on log management and analytics, providing centralized log collection and search capabilities. The choice between these two tools depends on specific requirements and preferences in terms of features, integrations, scalability, and user experience.

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Advice on Loggly, Datadog

Farzeem Diamond

Software Engineer at IVP

Jul 21, 2020

Needs adviceon

Datadog

Dynatrace

AppDynamics

Hey there! We are looking at Datadog, Dynatrace, AppDynamics, and New Relic as options for our web application monitoring.

Current Environment: .NET Core Web app hosted on Microsoft IIS

Future Environment: Web app will be hosted on Microsoft Azure

Tech Stacks: IIS, RabbitMQ, Redis, Microsoft SQL Server

Requirement: Infra Monitoring, APM, Real - User Monitoring (User activity monitoring i.e., time spent on a page, most active page, etc.), Service Tracing, Root Cause Analysis, and Centralized Log Management.

Please advise on the above. Thanks!

1.59M views1.59M

Comments

Medeti

Jun 27, 2020

Needs adviceon

Amazon EKS

Kubernetes

AWS Elastic Load Balancing (ELB)

We are looking for a centralised monitoring solution for our application deployed on Amazon EKS. We would like to monitor using metrics from Kubernetes, AWS services (NeptuneDB, AWS Elastic Load Balancing (ELB), Amazon EBS, Amazon S3, etc) and application microservice's custom metrics.

We are expected to use around 80 microservices (not replicas). I think a total of 200-250 microservices will be there in the system with 10-12 slave nodes.

We tried Prometheus but it looks like maintenance is a big issue. We need to manage scaling, maintaining the storage, and dealing with multiple exporters and Grafana. I felt this itself needs few dedicated resources (at least 2-3 people) to manage. Not sure if I am thinking in the correct direction. Please confirm.

You mentioned Datadog and Sysdig charges per host. Does it charge per slave node?

1.51M views1.51M

Comments

Benoit

Principal Engineer at Sqreen

Sep 17, 2019

Decided

I chose Datadog APM because the much better APM insights it provides (flamegraph, percentiles by default).

The drawbacks of this decision are we had to move our production monitoring to TimescaleDB + Telegraf instead of NR Insight

NewRelic is definitely easier when starting out. Agent is only a lib and doesn't require a daemon

457k views457k

Comments

Detailed Comparison

Loggly	Datadog
It is a SaaS solution to manage your log data. There is nothing to install and updates are automatically applied to your Loggly subdomain.	Datadog is the leading service for cloud-scale monitoring. It is used by IT, operations, and development teams who build and operate applications that run on dynamic or hybrid cloud infrastructure. Start monitoring in minutes with Datadog!
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	14-day Free Trial for an unlimited number of hosts;200+ turn-key integrations for data aggregation;Clean graphs of StatsD and other integrations;Slice and dice graphs and alerts by tags, roles, and more;Easy-to-use search for hosts, metrics, and tags;Alert notifications via e-mail and PagerDuty;Receive alerts on any metric, for a single host or an entire cluster;Full API access in more than 15 languages;Overlay metrics and events across disparate sources;Out-of-the-box and customizable monitoring dashboards;Easy way to compute rates, ratios, averages, or integrals;Sampling intervals of 10 seconds;Mute all alerts with 1 click during upgrades and maintenance;Tools for team collaboration
Statistics
Stacks 269	Stacks 9.8K
Followers 304	Followers 8.2K
Votes 168	Votes 861
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 140 Monitoring for many apps (databases, web servers, etc) 107 Easy setup 87 Powerful ui 84 Powerful integrations 70 Great value Cons 20 Expensive 4 No errors exception tracking 2 External Network Goes Down You Wont Be Logging 1 Complicated
Integrations
Heroku Amazon S3 New Relic AWS CloudTrail Engine Yard Cloud Cloudability	NGINX Google App Engine Apache HTTP Server Java Docker Pingdom MySQL Ruby Python Memcached

What are some alternatives to Loggly, Datadog?

New Relic

The world’s best software and DevOps teams rely on New Relic to move faster, make better decisions and create best-in-class digital experiences. If you run software, you need to run New Relic. More than 50% of the Fortune 100 do too.

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

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.

Raygun

Raygun gives you a window into how users are really experiencing your software applications. Detect, diagnose and resolve issues that are affecting end users with greater speed and accuracy.

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 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.

AppSignal

AppSignal gives you and your team alerts and detailed metrics about your Ruby, Node.js or Elixir application. Sensible pricing, no aggressive sales & support by developers.

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.

AppDynamics

AppDynamics develops application performance management (APM) solutions that deliver problem resolution for highly distributed applications through transaction flow monitoring and deep diagnostics.

Stackify

Stackify offers the only developers-friendly innovative cloud based solution that fully integrates application performance management (APM) with error and log. Allowing them to easily monitor, detect and resolve application issues faster

Related Comparisons

Datadog vs Loggly: What are the differences?

Introduction:

Data Collection and Aggregation: Datadog provides a comprehensive platform for collecting and aggregating logs, metrics, and traces, offering a full-stack observability solution. On the other hand, Loggly primarily focuses on log management and analytics, providing centralized log collection and aggregation capabilities.
Search and Query Capabilities: Datadog offers a powerful search and query language, allowing users to explore and analyze logs in a user-friendly manner. It provides advanced filtering and aggregation options, enabling users to extract valuable insights from logs efficiently. Loggly, on the other hand, offers a simple yet effective search functionality, allowing users to search logs using keywords and basic filters.
Real-time Monitoring and Alerting: Datadog excels in real-time monitoring and alerting capabilities, enabling users to set up alerts based on log data, metrics, and custom events. It provides real-time dashboards and visualizations to track log data and metrics effectively. Loggly also offers alerting features, but it may not be as comprehensive as that of Datadog.
Integration Ecosystem: Datadog has a vast integration ecosystem, allowing seamless integration with various tools and services such as AWS, Azure, Kubernetes, and many others. It offers native integrations and APIs to collect logs from different sources effectively. Loggly also provides integration options, but the range of integrations may be somewhat limited compared to Datadog.
Scalability and Performance: Datadog is known for its scalability and performance, handling large volumes of logs and metrics efficiently. It can handle massive workloads and offers a reliable and robust infrastructure to support high-throughput log data processing. Loggly is also scalable but may not have the same level of performance as Datadog for extremely high volumes of log data.
User Interface and User Experience: Datadog provides a user-friendly and intuitive interface, making it easier for users to navigate and explore log data, metrics, and alerts. It offers interactive dashboards and visualizations, providing a holistic view of log data. Loggly also provides a user-friendly interface but may not have the same level of advanced visualizations and interactive features as Datadog.

Datadog vs Loggly

Overview