OpenTelemetry vs Fluent Bit - Key Differences 2025

Modern applications demand strong observability to ensure performance, reliability, and quick troubleshooting. Two powerful open-source tools, OpenTelemetry and Fluent Bit play key roles in this space. While OpenTelemetry offers a full-stack framework for collecting metrics, logs, and traces, Fluent Bit specializes in fast, lightweight log forwarding. In this blog on OpenTelemetry vs Fluent Bit, we will explore how these tools differ, where they excel, and how they can work together to build a robust observability pipeline.

Table of Contents:

1. What is OpenTelemetry?
• Scope: Unified Observability
• Key Components
• Strengths
• Use Cases
• Scalability
• Use OpenTelemetry when
2. What is Fluent Bit?
• Scope: Purpose-Built for Logs
• Functionality
• Strengths
• Use Cases
• Scalability
• Use Fluent Bit when
3. OpenTelemetry vs Fluent Bit: Differences between OpenTelemetry and Fluent Bit
4. Can OpenTelemetry and Fluent Bit be used together?
5. Setting up a Fluent Bit and OpenTelemetry Pipeline
6. Visualizing Telemetry with Atatus

What is OpenTelemetry?

OpenTelemetry (OTel) is an open-source observability framework that provides a standardized way to collect, process, and export telemetry data, metrics, logs, and traces, from cloud-native applications. It’s vendor-neutral and designed to work with any backend, making it a flexible choice for modern observability needs.

Scope: Unified Observability

OpenTelemetry supports metrics, logs, and traces in a single framework. This unified approach helps teams gain a complete view of what is happening inside their systems from overall performance trends to the lifecycle of a specific request.

Key Components

• APIs & SDKs: Available in multiple languages to instrument applications and generate telemetry data.
• Collector: A central service that receives, processes, and exports telemetry data from different sources.
• Exporters: Modules that send processed data to backends like Prometheus, Jaeger, Zipkin, or observability platforms like Grafana, Datadog, Atatus or New Relic.

Strengths

• End-to-end visibility: View how a request flows through various microservices and where latency or errors occur.
• Vendor-neutral: Avoids lock-in by supporting open standards like OTLP (OpenTelemetry Protocol).
• Flexible integrations: Works with popular open-source and commercial tools in the observability ecosystem.

Use Cases

1. Monitoring & Troubleshooting Distributed Systems

• End-to-end tracing: Track requests across services for full visibility.
• Root cause analysis: Quickly identify which service caused an issue.
• Service dependency mapping: Understand how services interact and depend on each other.
• Context propagation: Maintain trace context across service boundaries for accurate troubleshooting.

2. Performance Optimization

• Bottleneck detection: Identify slow components using traces and metrics.
• Resource analysis: Monitor CPU, memory, and network usage to improve efficiency.
• Front-end performance: Catch inefficient JavaScript or slow user interactions.

3. Integration with Existing Tools

• Vendor-neutral data collection: Compatible with any observability backend.
• Toolchain integration: Works seamlessly with Prometheus, Grafana, Jaeger, etc.
• Customizable pipelines: Adapt to specific environments with flexible configuration.

4. Additional Use Cases

• Capacity planning: Use telemetry trends to forecast infrastructure needs.
• Security monitoring: Spot unusual patterns or unauthorized behavior.
• Exception tracking: Capture and analyze application errors in real time.

Scalability

OpenTelemetry is designed to operate at cloud-native scale. In large, microservices-based systems, telemetry data can grow rapidly. OpenTelemetry handles this by:

• Supporting batch processing and sampling to reduce load
• Running the Collector as a sidecar, gateway, or agent depending on architecture
• Being highly configurable, so teams can adjust data flow based on their performance and cost requirements

This makes OpenTelemetry suitable for everything from small apps to large-scale distributed systems with hundreds or thousands of services.

Use OpenTelemetry when:

1. You need full observability: logs, metrics, and traces
   OpenTelemetry offers a unified, vendor-neutral framework for all telemetry signals.
2. You want deep application insights: With SDKs for most programming languages, OpenTelemetry allows detailed instrumentation and context-rich tracing.
3. You are centralizing telemetry pipelines: The OpenTelemetry Collector aggregates data from multiple sources, transforms it, and sends it to your observability tools.
4. You are building a platform-agnostic setup: OpenTelemetry supports many exporters, from Jaeger to Prometheus to Atatus, without vendor lock-in.
5. You plan to correlate logs, metrics, and traces: OpenTelemetry is designed to correlate telemetry signals across services for end-to-end visibility.

What is Fluent Bit?

Fluent Bit is a fast, lightweight, and highly efficient open-source log processor and forwarder. It is designed to collect logs from various sources, process them, and then forward them to a destination like Elasticsearch, OpenSearch, or an observability platform via protocols such as OTLP.

Scope: Purpose-Built for Logs

Unlike full observability frameworks, Fluent Bit is focused specifically on logs. It excels in environments where you need to collect and move logs quickly with minimal resource overhead.

Functionality

• Collection: Pulls logs from files, containers, system logs, etc.
• Filtering: Modify, enrich, or exclude logs using built-in filters.
• Buffering: Handles bursts of logs using memory and file buffers.
• Shipping: Sends logs to various outputs like Elasticsearch, Loki, OpenTelemetry Collector, Kafka, etc.

Strengths

• Lightweight and performant: Written in C, uses very little memory and CPU, ideal for edge environments.
• Pluggable architecture: Offers flexible input, filter, and output plugins to build custom log pipelines.
• Kubernetes native: Designed to run as a DaemonSet, making it easy to collect logs from every node and container in a Kubernetes cluster.

Use Cases

1. Log Aggregation & Forwarding

• Kubernetes: Collects container logs and sends them to Elasticsearch, Kafka, or cloud services.
• Edge Computing: Gathers logs from devices/sensors and forwards them to a central backend.
• Cloud Environments: Collects logs across cloud services for centralized processing.
• Centralized Logging: Aggregates logs from various sources into a unified backend.

2. Data Transformation & Enrichment

• Structured Logging: Converts raw logs into structured formats like JSON.
• Filtering & Routing: Filters logs by content and routes them to different destinations.
• Metadata Enrichment: Adds contextual info (e.g., hostname, timestamp) to logs.

3. Enhanced Observability

• Metrics Collection: Forwards system or application metrics to Prometheus/Grafana.
• Trace Collection: Sends traces to backends like Jaeger or OpenTelemetry Collector.
• Real-time Monitoring: Enables real-time dashboards and alerting systems.

4. Additional Use Cases

• Log Volume Reduction: Filters unnecessary logs at the source to save bandwidth and storage.
• Persistent Storage: Stores logs in services like Amazon EFS to prevent data loss.

Scalability

Fluent Bit is built for high-throughput log processing in modern, dynamic environments. Its scalability comes from:

• Low resource footprint: Consumes just a few MBs of memory and CPU, allowing it to run on edge devices, small VMs, or as a sidecar in Kubernetes.
• Concurrent log pipelines: Can handle logs from multiple sources and send them to different destinations in parallel.
• Efficient buffering: Smart buffering mechanisms prevent data loss during spikes or temporary backend outages.

This makes Fluent Bit ideal for large-scale environments like production Kubernetes clusters, where thousands of logs are generated every second and need to be processed and forwarded efficiently without taxing system resources.

Use Fluent Bit when:

1. Your focus is only logs: Fluent Bit is purpose-built for collecting, processing, and forwarding logs efficiently.
2. You need lightweight performance: Fluent Bit is ideal for resource-constrained environments like edge devices or sidecars in Kubernetes.
3. You want fast log shipping at scale: Fluent Bit is optimized for high-throughput and low-latency log forwarding.
4. You are already using log-centric tools: It integrates seamlessly with Elasticsearch, OpenSearch, Kafka, and other log backends.
5. You need simple filtering or enrichment at the edge: Fluent Bit can parse, tag, filter, and enrich logs before forwarding them.

OpenTelemetry vs Fluent Bit: Differences between OpenTelemetry and Fluent Bit

Can OpenTelemetry and Fluent Bit be used Together?

Yes, OpenTelemetry and Fluent Bit can be integrated to build a flexible, scalable log pipeline. This setup is especially useful when you want to use Fluent Bit’s lightweight log collection capabilities at the edge and OpenTelemetry’s centralized processing and exporting features.

The integration works like this:

• Fluent Bit acts as the log forwarder. It collects log data from containers, files, or nodes and sends it in a structured format.
• OpenTelemetry Collector acts as the receiver and processor. It ingests the logs from Fluent Bit, processes them (batching, filtering, enriching), and exports them to one or more backends.

To make this integration work, Fluent Bit must be configured to output logs using a protocol the OpenTelemetry Collector supports usually OTLP over HTTP or gRPC.

This setup is beneficial when:

• You want to centralize log processing across multiple services or nodes.
• You need to enrich logs before sending them to a backend.
• You are building a vendor-neutral observability pipeline that works with different backends (e.g., Jaeger, Prometheus, Elasticsearch, or Atatus).

Setting up a Fluent Bit and OpenTelemetry Pipeline

This section walks you through how to integrate Fluent Bit with the OpenTelemetry Collector to build a unified log pipeline. Fluent Bit will collect and parse logs, and forward them to the OpenTelemetry Collector, which will handle exporting them to a backend like Jaeger.

Step 1: Install Fluent Bit

Begin by installing Fluent Bit using the official installation script:

curl https://raw.githubusercontent.com/fluent/fluent-bit/master/install.sh | sh
fluent-bit --version

This command installs the latest version of Fluent Bit and verifies the installation.

Step 2: Configure Fluent Bit to Send Logs to OpenTelemetry

Create a Fluent Bit configuration file (e.g., fluent-bit.conf) to read logs, parse them as JSON, and send them to the OpenTelemetry Collector:

[SERVICE] 
    Log_Level info 

[INPUT] 
    Name tail 
    Path /var/log/app/*.log 
    Tag app.logs 

[FILTER] 
    Name parser 
    Match * 
    Parser json 

[OUTPUT] 
    Name opentelemetry 
    Match * 
    Host otel-collector 
    Port 4318 
    TLS On 
    Log_Response_Payload true

This setup reads JSON log files from /var/log/app/, parses them, and sends them via OTLP to the OpenTelemetry Collector.

Note: Make sure the otel-collector hostname and port (4318) are accessible. Also, ensure the opentelemetry output plugin is available in your Fluent Bit version (it is included in recent versions).

Step 3: Configure and Deploy the OpenTelemetry Collector

Create a configuration file for the OpenTelemetry Collector (otel-collector.yaml) to receive data from Fluent Bit and export it to a backend like Jaeger:

receivers: 
  otlp: 
    protocols: 
      grpc: 
      http: 

exporters: 
  jaeger: 
    endpoint: "http://jaeger-collector:14268/api/traces" 

service: 
  pipelines: 
    traces: 
      receivers: [otlp] 
      exporters: [jaeger]

This configuration enables the OTLP receiver (both HTTP and gRPC) and routes the traces to a Jaeger collector.

To deploy the configuration in a Kubernetes environment:

kubectl apply -f otel-collector.yaml

If you are not on Kubernetes, you can run the collector locally using:

./otelcol --config=otel-collector.yaml

Visualizing Telemetry with Atatus

Once your Fluent Bit and OpenTelemetry pipeline is set up, the next step is to choose a backend that can ingest, visualize, and alert on the telemetry data effectively. Atatus supports both Fluent Bit and OpenTelemetry out of the box.

You can forward:

1. Logs collected by Fluent Bit
2. Traces and metrics instrumented through OpenTelemetry

This makes it easier to:

• Search and analyze logs with contextual filters
• View distributed traces with end-to-end latency breakdowns
• Monitor key metrics and define service-level objectives (SLOs)
• Get alerted on performance anomalies or errors

By plugging Fluent Bit and OpenTelemetry into Atatus, you gain a unified observability experience without the need to manage complex backend infrastructure.

You can get started for free in just a few minutes — no credit card required. Just plug in your data and start gaining insights instantly.