Top 6 Filebeat Alternatives 2025

Filebeat, a lightweight log collection tool from Elastic, is known for efficiently gathering and shipping logs. While it has impressive features, other tools offer more advanced features and remain lightweight.

This article explores six alternatives to Filebeat, which will help you uncover options that could better match your needs.

Filebeat key features

Filebeat is built for simplicity and scalability, making it an excellent choice for gathering logs from various sources and shipping them to destinations like Elasticsearch or Logstash.

Its modular design and predefined modules for services such as MySQL, Apache, and Kubernetes let you hit the ground running without complex setups.

Filebeat operates with minimal resource usage and adds valuable context to your logs—like Kubernetes pod details or cloud metadata—giving you deeper insights.

As part of the Elastic Beats ecosystem, it integrates well with the Elastic Stack, ensuring a smooth and efficient logging experience.

Top 6 alternatives to Filebeat for log shipping in 2025

Let’s see how Filebeat stacks up against its alternatives and explore the top contenders for log shipping:

1. OpenTelemetry Collector

The OpenTelemetry Collector is a fantastic, lightweight alternative to Filebeat. It provides powerful capabilities for collecting, processing, and exporting telemetry data, including logs, metrics, and traces.

🌟 Key features

• Vendor-neutral
  
• Flexible configuration
  
• Extensible through modular components
  
• Supports multiple protocols and data formats
  
• Filtering support
  
• Extensible architecture
  

➕ Pros

• Unlike Filebeat, which focuses primarily on logs, OpenTelemetry supports traces, metrics, and logs
• The Collector supports open-source observability formats (e.g., Jaeger, Prometheus, Fluent Bit) and integrates with various backends
• As an open-source CNCF project, OpenTelemetry benefits from rapid improvements and a large, active community
• Extensible through modular components, allowing for custom pipelines without modifying core code
• Provides reasonable default configurations and supports popular protocols
• Built-in mechanisms for filtering and encrypting sensitive data before export enhance security and compliance

➖ Cons

• Automatic instrumentation and advanced features may sometimes introduce higher resource consumption
• OpenTelemetry’s broad feature set and modular architecture can make it challenging to configure

2. Vector

Vector is an observability data pipeline written in Rust that excels at collecting, transforming, and routing logs and metrics. With its high performance and memory-safe design, Vector is built to handle demanding workloads efficiently while maintaining reliability.

🌟 Key features

• Vendor-neutral design
  
• Programmable transforms
  
• Advanced filtering support
  
• Unified observability
  
• Extensible architecture
  
• Single binary deployment
  

➕ Pros

• Unlike Filebeat, which primarily focuses on logs, Vector supports both logs and metrics
• Vector offers highly configurable and programmable transforms (e.g., using Vector Remap Language), allowing advanced data manipulation
• Vector is packaged as a single binary with no runtime dependencies
• Supports multiple configuration formats (YAML, TOML, JSON
• Rust's memory safety and performance characteristics make Vector more memory-efficient
• Supports multiple topologies (distributed, centralized, or stream-based), making it suitable for a variety of environments

➖ Cons

• Less mature ecosystem compared to Filebeat
• Vector’s programmable transforms (e.g., VRL) can be challenging to learn

3. Fluentd

Fluentd is another mature log shipper that offers a unified logging layer for collecting, transforming, and routing logs. With its extensive plugin ecosystem, JSON-based data structuring, and proven reliability, Fluentd provides flexibility and performance for organizations of all sizes.

🌟 Key features

• Pluggable architecture
  
• Unified logging layer
  
• High availability
  
• Supports both memory and file-based buffering
  
• Unified logging with JSON
  
• Failover mechanisms
  

➕ Pros

• Supports a wide range of data sources with over 500 community-contributed plugins
• Features like memory- and file-based buffering, failover, and high availability ensure minimal data loss
• Over 5,000 companies use Fluentd, and it has been a trusted solution for over a decade
• Structures log data in JSON, simplifying downstream processing

➖ Cons

• In environments with extremely high log volumes, Fluentd may introduce slightly higher latency

4. Fluent Bit

Fluent Bit is another log shipper that is lightweight and highly efficient. It was built as a lightweight alternative to Fluentd by the same company, specifically designed for modern environments like containers and edge computing, where minimal resource consumption and high performance are critical.

🌟 Key features

• Asynchronous I/O with built-in TLS/SSL support
  
• Pluggable architecture
  
• Data parsing & transformation
  
• Programmable filters (Lua, SQL-based stream processing)
  
• Exposes internal metrics via HTTP in JSON
  

➕ Pros

• Includes built-in SQL stream processing, enabling advanced data manipulation and analysis directly within the pipeline
• Its asynchronous, event-driven design leverages OS APIs for performance and reliability, ensuring robust data collection and delivery
• Integrates seamlessly with over 80 data sources, filters, and destinations
• Fluent Bit's abstracted I/O layer supports high-scale read/write operations and efficiently handles backpressure to prevent data loss

➖ Cons

• Features like stream processing and SQL queries may have a steeper learning curve
• Fluent Bit supports Elasticsearch but lacks the deep integration that Filebeat offers as part of the Elastic Stack

5. Logstash

Logstash is a feature-rich data processing pipeline tightly integrated with the Elastic Stack, capable of ingesting, transforming, and forwarding logs. It offers advanced features like persistent queues, dynamic data transformation, and secure pipeline management.

🌟 Key features

• Pipeline management UI
  
• API and plugin generator
  
• Persistent queues and dead letter queues
  
• Advanced parsing & transformation
  
• Built-in pipeline viewer and monitoring features
  

➕ Pros

• Excels at transforming data on the fly with features like grok filters for extracting structured data from unstructured logs
• Logstash can ingest data from a wide variety of sources, including logs, metrics, AWS services, and custom applications, simultaneously
• As part of the Elastic Stack, Logstash integrates well with Elasticsearch and Kibana
• Logstash offers over 200 plugins for inputs, filters, and outputs
• Features like the pipeline viewer and built-in monitoring make it easy to observe, debug, and optimize Logstash deployments

➖ Cons

• Logstash requires significant CPU and memory resources
• Relies on the Java Runtime Environment (JRE), which can increase startup times

6. Rsyslog

Rsyslog is a log shipper commonly found by default in many operating systems. It can efficiently process, transform, and forward logs to various destinations and has a modular, high-performance architecture that makes it suitable for handling high-throughput environments.

🌟 Key features

• TLS-protected syslog transmission
• Supports regular expressions, boolean expressions
• High performance
• Dynamic configuration
• Multi-threaded processing

➕ Pros

• Features like on-disk spooling, backup servers, and persistent queues ensure data is not lost even during failures
• Rsyslog supports regular expressions, sequence filtering, and boolean expressions for precise control over log processing
• Allows for sub-configuration files and modular designs for inputs and outputs
• With multi-threaded processing and dynamic work thread pools, Rsyslog handles large-scale logging efficiently on multi-core systems
• Rsyslog can also monitor text files, handle logs from NAT environments, and execute scripts based on received messages

➖ Cons

• Configuring Rsyslog for complex pipelines can be challenging

Centralizing logs with Better Stack

Exploring these six alternatives to Filebeat highlights the importance of efficient log collection. However, centralizing your logs is equally critical for optimizing your observability strategy, and that’s where Better Stack makes a difference.

Better Stack consolidates logs from multiple sources into a unified platform, offering much more than basic storage. It has features like structured log storage, real-time search, and SQL querying, enabling you to analyze and act on your data quickly.

Additionally, its dashboards provide clear visual insights, making monitoring more effective. Beyond that, Better Stack integrates incident management to help ensure rapid resolution of the issue.

Final thoughts

This article has explored six alternatives to Filebeat, each with unique strengths. The OpenTelemetry Collector is great for its vendor-neutrality and community support, while Vector and Fluent Bit deliver lightweight, high-performance solutions.

No matter which tool fits your needs best, centralizing your logs with a platform like Better Stack can amplify your observability strategy, simplify workflows, and keep your systems running smoothly.

Thanks for reading, and happy logging!

Article by

Stanley Ulili

Stanley Ulili is a technical educator at Better Stack based in Malawi. He specializes in backend development and has freelanced for platforms like DigitalOcean, LogRocket, and AppSignal. Stanley is passionate about making complex topics accessible to developers.

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