StackShareStackShare
Follow on
StackShare

Discover and share technology stacks from companies around the world.

Follow on

© 2025 StackShare. All rights reserved.

Product

  • Stacks
  • Tools
  • Feed

Company

  • About
  • Contact

Legal

  • Privacy Policy
  • Terms of Service
  1. Stackups
  2. DevOps
  3. Log Management
  4. Log Management
  5. Loggly vs Splunk

Loggly vs Splunk

OverviewComparisonAlternatives

Overview

Loggly
Loggly
Stacks269
Followers304
Votes168
Splunk
Splunk
Stacks772
Followers1.0K
Votes20

Loggly vs Splunk: What are the differences?

Key Differences between Loggly and Splunk

Loggly and Splunk are both popular log management and analysis tools used for monitoring and troubleshooting applications and infrastructure. However, there are several key differences between them.

  1. Pricing Model: Loggly offers a simple and straightforward pricing model based on the volume of log data ingested, making it easy to estimate costs. On the other hand, Splunk follows a more complex pricing structure based on the number of users and the amount of data indexed, which can make it difficult to predict expenses accurately.

  2. Ease of Use: Loggly is known for its intuitive user interface and straightforward setup process, making it more accessible for users of all skill levels. Splunk, on the other hand, has a steeper learning curve and requires more technical expertise to fully utilize its capabilities.

  3. Search and Query Capabilities: Splunk offers advanced search and query capabilities, including the ability to perform complex ad-hoc searches and correlation across multiple data sources. Loggly, while still capable of searching and analyzing logs effectively, may not have the same level of flexibility and advanced features as Splunk in this regard.

  4. Integration and Compatibility: Splunk provides extensive out-of-the-box integration options with a wide range of data sources and tools, making it easier to aggregate and analyze logs from various systems. Loggly, while still offering integrations with popular services, may have limitations in terms of compatibility with certain platforms or custom log formats.

  5. Scalability and Performance: Splunk is known for its scalability and ability to handle large volumes of log data efficiently. It can be deployed in distributed environments, enabling high availability and improved performance. Loggly, while scalable to a certain extent, may have limitations in managing larger data volumes and may not provide the same level of performance as Splunk in more demanding scenarios.

  6. Enterprise Features: Splunk offers a wide range of enterprise-level features, including role-based access control, advanced alerting and reporting capabilities, and integration with third-party security tools. Loggly, while suitable for many use cases, may have limitations in terms of advanced enterprise features and security-related functionality.

In summary, Loggly and Splunk differ in their pricing models, ease of use, search capabilities, integration and compatibility options, scalability and performance capabilities, as well as the range of enterprise features offered. Choosing between the two ultimately depends on specific requirements and preferences.

Share your Stack

Help developers discover the tools you use. Get visibility for your team's tech choices and contribute to the community's knowledge.

View Docs
CLI (Node.js)
or
Manual

Detailed Comparison

Loggly
Loggly
Splunk
Splunk

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

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
Predict and prevent problems with one unified monitoring experience; Streamline your entire security stack with Splunk as the nerve center; Detect, investigate and diagnose problems easily with end-to-end observability
Statistics
Stacks
269
Stacks
772
Followers
304
Followers
1.0K
Votes
168
Votes
20
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
  • 3
    API for searching logs, running reports
  • 3
    Alert system based on custom query results
  • 2
    Ability to style search results into reports
  • 2
    Query engine supports joining, aggregation, stats, etc
  • 2
    Dashboarding on any log contents
Cons
  • 1
    Splunk query language rich so lots to learn
Integrations
Heroku
Heroku
Amazon S3
Amazon S3
New Relic
New Relic
AWS CloudTrail
AWS CloudTrail
Engine Yard Cloud
Engine Yard Cloud
Cloudability
Cloudability
No integrations available

What are some alternatives to Loggly, Splunk?

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.

Apache Spark

Apache Spark

Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.

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.

Presto

Presto

Distributed SQL Query Engine for Big Data

Amazon Athena

Amazon Athena

Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.

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.

Related Comparisons

GitHub
Bitbucket

Bitbucket vs GitHub vs GitLab

Bootstrap
Materialize

Bootstrap vs Materialize

Laravel
Django

Django vs Laravel vs Node.js

Bootstrap
Foundation

Bootstrap vs Foundation vs Material UI

Node.js
Spring Boot

Node.js vs Spring-Boot