Loggly vs. the ELK Stack: A Quick Feature Analysis – ELK Stack Alternative

Logs provide valuable information about an organization’s applications and systems, such as information about performance monitoring and troubleshooting, production monitoring, and security and compliance. To understand logs and mitigate risks, however, it’s crucial to have a reliable and advanced log management solution. Several log aggregation tools offer proactive monitoring, fast data analysis, and optimization features, and teams must consider cloud-based log management tools capable of integrating with existing software (using secure endpoints like HTTP and powerful APIs), providing enhanced security, and offering better performance.

Cloud logging is an effective way to maintain large volumes of log events. The major advantage of using a cloud-based logging solution is it streamlines various processes and helps eliminate the need for multiple tools required to aggregate logs from various sources. Both SolarWinds® Loggly® and the ELK Stack provide cloud-based logging solutions. They cover all the basic logging features along with advanced capabilities, including the ability to archive to Amazon S3, role-based access, peak overage protection, JIRA software integration, custom retention periods, and agentless log collection (schemaless). These features are distributed among their various plans, which are designed with business requirements in mind.


SolarWinds Loggly is a great log management tool for organizations prioritizing speed and efficiency. It also offers intuitive and interactive dashboards divided into distinct sections to give a centralized and unified view of data. These dashboards include key performance indicators, flexible visual representations, shareable reports, and dashboard options. Loggly also offers advanced logging capabilities, including live-tail logging, API access, alerts to multiple endpoints, agentless log collection, automated parsing, color-coded tags, GitHub integration, and more. Additionally, the tool offers free and premium plans to support businesses of every size.

The ELK Stack

The ELK Stack is a log management solution with a combination of three open-source tools:

  • Logstash for log shipping
  • Elasticsearch as a scalable search engine
  • Kibana, a UI for easy log search and visualization

All three open-source tools work together to provide the best logging results. The role of Logstash is to process the server-side data and funnel it to Elasticsearch, which is responsible for searching and analyzing relevant logs. Once the logs are analyzed, Kibana displays them in the form of charts and graphs with its excellent visualization capabilities. The ELK Stack offers fast searches, real-time visualizations, and data parsing. The tool is free and open-source; however, it offers various plans (such as its Gold, Platinum, and Enterprise plans) providing access to its advanced functionalities with endpoint protection.

Loggly vs. the ELK Stack

Organizations in their early development phase have more time to invest than capital. For these organizations, choosing the ELK Stack is a smart decision. The ELK Stack offers flexible and up-to-the-mark open-source projects. Loggly also uses Elasticsearch as its core technology. However, when it comes to running an on-premises stack or a cloud service for log management, the ELK Stack requires dedicated resources or specific expertise. If the organization is beyond the early development phase, Loggly is a perfect solution capable of handling maintenance, outages, performance, and requests for new data. Because Loggly is a commercial solution, it takes full advantage of open-source software components such as Elasticsearch and Apache Kafka, but it doesn’t require any dedicated resources, additional costs, or effort. Outlined below are some additional differences between the two logging solutions.

Data Processing

While using the ELK Stack, it’s crucial to use grok filters before preprocessing the log data. Grok filters are a feature of Logstash designed to help parse unstructured log data into a structured and queryable format using text patterns. These filters need to be maintained as the log data changes over time.

In contrast, Loggly automatically parses different types of data—including Apache, NGINX, and JSON—without the need for grok filters. Users can create their own derived folders and fields with advanced features like filters, faceted search, statistical analysis on value fields, and more.

Huge Log Volumes

When organizations expand their IT environments, they may not be equipped to handle the accompanying complexity. Managing huge log volumes can be daunting, especially with a single log management installation. Logging solutions need to be built with redundancy (failover, use of multiple servers) and a robust queuing mechanism to maintain logs generated in huge volumes. Depending entirely on a single installation leads to failures, and when they grow exponentially, they become severe outages like dashboard or report failures. In such a scenario, using multiple servers helps. Although a hosted ELK Stack service can help, it scales the total cost of ownership (TCO).

Loggly, on the other hand, has optimized its search infrastructure and meets these requirements with its cloud-based log management and analytics service. It focuses on log agility and elasticity and analyzes unpredictable log data in its shareable dashboards in the form of charts. Additionally, Loggly provides Amazon S3 backup to store huge volumes of log data.


Although both tools use advanced technologies and offer robust features, Loggly is a better alternative to the ELK Stack. The ELK Stack needs experts to deploy most of its components. Moreover, users need to have an ELK Stack subscription to use an alert/notification plug-in known as Watcher to identify and track basic log events. The Loggly Enterprise plan offers email alerting and integrates with endpoints such as HipChat, PagerDuty, and Slack. With Loggly, there’s no need to manage heap, indices, shards, or cluster state, and it can handle the inevitable spikes during log data management.


*As of June 2020