Best Log Management Tools In 2024

There has been an enormous growth in the volume of log data. Companies that used to log GBs are now logging Terabytes daily.

Yet, most modern log management tools promise this:

• Pay LESS

• Extract MORE,

• And do it in REAL-TIME!

Some even add – don’t leave ANYTHING behind to it.

How are they promising to do it?

Which log management tools are THE BEST in living up to this PROMISE?

Let's find out by going through the features, functionalities, and use case details of the top logging tools list!

1. EdgeDelta

EdgeDelta's log management tool is a comprehensive solution for modern log management. It uses a single agent to collect, preprocess, aggregate, transform, and route log data, making it efficient, scalable, and cost-effective. It sets itself apart from other top log management software by analyzing logs before indexing, being vendor-agnostic, and supporting different streaming destinations.

How does EdgeDelta Manage Logs Effectively?

1. High-Volume Ingestion & Querying: Edge Delta's architecture supports millions of log lines per second and petabyte-scale querying, ensuring full visibility and performance without overspending.

2. Analyzing Data at Source: Edge Delta processes data where it's generated, reducing the need for large data transfers, speeding up analysis, and cutting costs.

Pricing: EdgeDelta, with its flexible data management options, is cost-effective.

Pros and Cons

Pros:

• Efficient handling of large data volumes

• Advanced anomaly detection with real-time alerts

• Significant cost savings through data optimization

• Otel Schema support and extensive integrations

• Strong focus on security and full data visibility

Cons:

• Dependence on the distributed architecture might necessitate adjustments in existing workflows

2. DataDog

The awards and numbers on the funding reports makes it very clear – Datadog is the market leader in the Observability Market.

Ever since its launch Datadog has been helping thousands of companies manage hundreds of terabytes of logs every day. Its architecture is specifically designed to handle large volumes of logs, making it the top choice for log management among growing enterprises.

What Makes DataDog The Best Enterprise Log Management Tool

• Built to Scale: Processes, searches, and analyzes logs on a massive scale

• User-Friendly Interface: Simplifies monitoring of logs, metrics, and traces, offering a seamless experience for DevOps engineers.

• 700+ Integrations enhance log data collection and extend monitoring capabilities to the front end by automatically capturing console logs.

• Unified Data Analysis: Ensures cohesive analysis and correlation of data from various sources.

Datadog Log Management Pricing

How Datadog Helps You Reduce Your Log Management Costs

• Logging without Limits™: It decouples log ingestion from indexing. This makes it affordable to observe all logs in real time. And the flexibility to adjust indexing based on current needs ensures affordability without compromising on data visibility.

• Custom Metrics from Logs: Transforms logs into efficient custom metrics at the point of ingestion to enhance observability.

Pros And Cons

Pros:

• Low Maintenance: Datadog is easy to maintain and implement, reducing operational effort.

• Intuitive Dashboards: Its dashboards are user-friendly

• Wide Integration Range: Offers extensive integrations, covering nearly all monitoring needs.

Cons:

• Costly for Small Entities due to its pricing on data ingestion-based model.

3. SumoLogic

SumoLogic is a cloud-native log management platform designed to ensure maximum visibility of complex IT infrastructures. Here are a few features that enable the SumoLogic Log Monitoring Tool to manage voluminous logs of complex modern cloud-based architectures.

What Makes SumoLogic Log Monitoring Special?

1. One-Click Integrations For Multi-Cloud Architectures: Sumo Logic offers seamless integration capabilities with AWS, Azure, and GCP, facilitating the centralization of log data across different cloud environments. This feature is crucial for organizations leveraging multi-cloud architectures, enabling them to maintain visibility and control over their server log data.

2. DevOps and DataOps teams can analyze large amounts of data quickly and accurately by centralizing logs and using predictive analytics, which helps identify issues and root causes.

3. Unified log management improves availability and performance while significantly reducing the Mean Time to Resolution (MTTR) with machine learning for root-cause analysis.

4. Scalability: Sumo Logic scales up and down dynamically, supporting large-scale workload migrations, expanding deployments, and adapting to seasonal spikes in data generation and usage

Pricing

Pros And Cons

Pros:

• Powerful logging system combined with insightful analytics with a special focus on security and compliance.

• Cloud-based architecture that allows you to scale log management with ease

• Integration rich.

Cons:

• No special features to reduce the log size and cost

• On paper, pricing is expensive.

4. Elastic Stack

Elastic, the company behind Elasticsearch, Kibana, Beats, and Logstash (the Elastic Stack), has established itself as a powerhouse in search-powered solutions. The platform offers secure, real-time-search monitoring, analysis, and visualization from any source and format.

The Elastic Stack's ability to handle high ingestion rates and powerful search capabilities make it an ideal solution for managing the growing volumes of log data.

Key Features Of Elastic Stack

• Data Transformation: It enables the parsing, transforming, and enriching of logs, making unstructured data highly usable and insightful.

• Extensive Data Source Support: Offers integrations for Amazon, Azure, Google Cloud, and cloud-native technologies,

• Optimized Query Performance: Improves query speeds with "schema on write" and offers flexibility with "schema on read" for dynamic data handling at query time. This flexibility aids in optimizing data analysis and storage strategies according to specific needs.

• Live Troubleshooting: Facilitates real-time search and monitoring with live tail features, enhancing immediate response capabilities.

• Machine Learning Insights: Automatically applies machine learning to log messages to rapidly detect patterns, trends, and outliers, speeding up issue resolution.

• Visualization is a key strength of Elastic, with Kibana providing a rich interface for creating and sharing dynamic dashboards that visualize real-time data.

• Elastic Observability Suite offers integrated logs, metrics, and APM traces, making it easier for organizations to monitor and react to their operational data.

Pricing

Pros:

• Strong community and ecosystem, with extensive documentation and integrations for cloud, hybrid, and on-premise log management

• Flexible and scalable architecture suitable for handling large volumes of data.

Cons:

• The complexity of the Elastic Stack can present a steep learning curve for new users.

• Cost can escalate quickly based on usage, especially for organizations with large-scale data needs.

5. Logz.io

Designed for engineers by engineers, Logz.io is an observability and security platform built on the foundation of open-source tools. Started with a log management tool in 2015, they now offer Open 360™, designed for unified full-stack observability and security. They work as a fully managed SaaS model, leveraging the best open-source technologies like OpenSearch, Jaeger, and Prometheus.

Logz.io Log Management Features

• Intuitive and High-Performance Search Filters: Enables quick data exploration to debug problems faster..

• Log Patterns for Accelerated Troubleshooting: Scans through log data in seconds to identify issues.

• Customizable Monitoring Dashboards: Visualize spikes, dips, and trends in logs with prebuilt or customizable dashboards.

• Zero-Maintenance Log Collection and Scaling: Based on OpenSearch and OpenSearch Dashboards, it handles ingestion, storage, dynamic scaling, and data security effortlessly.

• Full Environment Visibility: Unifies logs with metric and trace analytics for a comprehensive insight into the environment.

• Real-Time Alerting: Advanced alerts with multiple queries and conditions to reduce alert fatigue.

What's Different in Loz.io?

• Open-Source Log Management Tool: Utilizes OpenSearch and OpenSearch Dashboards, offering a robust and flexible foundation.

• Human-Coached AI/ML: Loz.io uses advanced machine learning informed by thousands of engineers' experiences for superior anomaly detection.

Pricing - Here is how the Logz.io log management service is priced.

Reducing Costs: Logz.io's approach to logging as a service emphasizes cost efficiency and full visibility by unifying logs, metrics, and trace analytics.

Here are a few ways they try to reduce your bill:

1. Dividing your data across tiers based on your needs

2. Removing data you don’t need with advanced ML-powered filters

3. By converting logs to metrics

4. Summarizing not-so-critical log data with visualizations

5. By cooling off log data in Troubleshooting Tier after 5 days,

6. And by using a good old archive and restore method.

Pros and Cons

Pros:

• Unified observability and security on a fully-managed SaaS platform.

• Cost-effective solutions for handling large volumes of observability data.

Cons:

• The complexity of features and functionalities might require a learning curve for new users.

• Cannot set all filters in GUI

• Search inconsistencies arise when a string search yields excessive data

6. Graylog

Graylog is a comprehensive log management solution with focus on centralized log management, coupled with advanced analysis tools and support for modern infrastructure.

Whether it is troubleshooting, system security analysis, real-time analysis of terabytes of logs, compliance management or operational monitoring, Graylog provides a scalable and flexible platform to meet the needs of diverse IT environments.

What Makes It Special?

• Lightning-fast search capabilities - Search terabytes of data in milliseconds

• Set Triggers, Aggregations, and correlations to get high-fidelity alerts

• Ability to extend features with plugins from Graylog Marketplace

• Customize dashboards with a wide range of data widgets

Like most of the above solutions, Graylog uses the Streamlining logs method to reduce unnecessary data storage and processing costs.

Here are

A Few Graylog’s Logging and Cost Optimization Strategies

• Source Filtering: Implementing filtering at the source, such as network devices, to only send relevant messages.

• Grok Patterns: Using Grok patterns within Graylog to parse and filter logs, deciding which fields are valuable and which can be dropped.

• Pipelines: Utilizing Graylog pipelines for fine-grained control over log processing, including dropping messages or fields that are not necessary.

Combine these features with Graylog Illuminate for log data normalization and easy-to-use in-built and custom log aggregation options. You get one of the best log monitoring tools for those who need customized savings.

Pricing

Pros:

• Open-source and cost-effective log management solution.

• Comprehensive features for log processing, analysis, and security insights.

• Strong support for anomaly detection and real-time alerts.

• Extensive documentation, resourceful marketplace, and community support.

Cons:

• Advanced features and customizations might require in-depth technical knowledge.

• No Alerts on log parameters correlation

• Dashboard flexibility is limited

7. Mezmo

Mezmo, previously known as LogDNA, is a comprehensive log management solution designed to streamline the complexity of log data into actionable insights.

Mezmo tackles the growing concern over logging costs by offering efficient data processing

• Exclusion Rules: Users can exclude less critical machine or software-generated logs from being stored, focusing resources on valuable data.

• Variable Retention: Allows users to save logs in the Mezmo UI only for the time they're relevant, controlling costs without sacrificing data access.

• Cost-Effective Archiving: Allows for cost-effective log archiving to ensure logs are kept only as long as they are relevant.

• Data Restoration: Facilitates easy retrieval of archived logs, ensuring data is accessible when needed for analysis or compliance.

Features, Functionalities, and Use Cases:

• Log Search and Analysis: Mezmo provides an intuitive querying and log aggregation system to simplify the processing of voluminous log data

• Anomaly Detection and Real-time Alerts: Equipped with anomaly detection capabilities, providing real-time alerts to address potential issues preemptively.

• Kubernetes Log Management: enables log collection across Kubernetes clusters in any environment with three simple kubectl commands.

• Visualization and Dashboard: Enables easy visualization of log events and patterns, aiding in root cause analysis and troubleshooting.

Pricing -

On the Professional plan, the cost per 1GB log increases with RETENTION time.

• 7-Day Retention - $1.05/GB

• 14-Day Retention - $1.30/GB

• 30-Day Retention -$1.80/GB

Pros and Cons:

Pros:

• Simplifies log management and analysis

• Efficient anomaly detection with real-time alerts

• Cost-effective log management strategies

• Comprehensive data visualization tools

• Supports a wide range of integrations and OpenTelemetry

Cons:

• Indexing, log searching, and log-reading lag

• No cluster views

• Not many color options for managing logs effectively

Like in any other tools, all the above log management tools have overall positives and negatives. Considering the complexity and variations of log management and monitoring in different scenarios, it is hard to set one simple framework to choose a good log management tool.

However, here is a basic checklist that can help you refine your requirements document and find the right log management tool for your needs.

How To Choose the Best Log Management/Monitoring Tool in 2024

Log Search and Analysis: Determine the ease of querying, log aggregation, and analysis. The tool should have quick and efficient log search capabilities.

Data Handling and Ingestion Rates: Examine how the solution manages large volumes of data and its log ingestion rates, ensuring scalability and performance.

Anomaly Detection and Real-time Alerts: Check for ML/AI-powered anomaly detection features and the ability to configure real-time alerts for proactive issue resolution.

Observability and Visibility: Assess the solution's capability to provide maximum observability and 100% visibility across your systems.

Cost-Saving Mechanisms: Check if the tool is reducing log management costs using observability pipelines, telemetry pipelines, or other innovative approaches.

Schema Compatibility and OpenTelemetry Support: A good log management tool for modern distributed applications should support OpenTelemetry (Otel) standards for seamless integration, interoperability, and compatibility with other popular systems.

Integrations: A good log management software should allow you to get a unified view of logs from third-party solutions.

Visualization and Dashboard Capabilities: Evaluate the tool's visualization features for log events and patterns and how these visualizations aid in root cause analysis and troubleshooting.

Log Archiving and Retrieval: This functionality aids in optimizing costs. So, check how the log management tool archives and retrieves logs.

Storage Control and Variable Retention: Assess the flexibility in controlling log storage volumes and retention periods, allowing for customization based on relevance and compliance requirements.

Infrastructure Security Enhancement: Determine how the solution contributes to the security of your infrastructure, protecting against threats and vulnerabilities.

Operational Insight: Search for information on what operational insights the tool provides for SREs, developers, and IT teams to understand the infrastructure better.

Kubernetes Friendly: Kubernetes adoption is at its peak. A good log management tool should offer ease of monitoring stateless applications.

Cost Structure: Review the pricing model, including the availability of a free plan or trial and how costs are calculated to ensure it aligns with your budget and usage expectations.