Observability Agent Profiling: Fluent Bit vs OpenTelemetry Collector Performance Analysis

Introduction

Parseable is a full stack observability platform. While it integrates with every major observability agent - OTel, FluentBit, Vector, and more, we often get this question: "What is your recommended observability agent?".

To settle this for us and for our users, we wanted to see how do the two most popular agents - OTel Collector and Fluent Bit stack up when with same input data, same server conditions and same target system? We ran both agents side by side against the OTel-Demo microservices and grabbed CPU, memory, I/O and load stats to find out.

Test Setup

We used OTel Collector v0.129.1 (with default batching + memory queues) and Fluent Bit v4.0.5 (with opentelemetry plugin + basic filters) in this experiment.

To ensure both Fluent Bit and OpenTelemetry Collector have the telemetry data source, we used the node-exporter to generate metrics and send it through the agents.

The destination of this telemetry was a Parseable cluster running v2.4.0, again to ensure the exact same situation for both the agents.

Note that this is a controlled environment and does not fully represent production scenarios. For this testing, we focused on a limited set of metrics to ensure consistency all generated from node-exporter running on an EC2 instance. The EC2 instance was a t3.small with 2 vCPUs and 2 GiB of RAM, running Ubuntu 22.04.

Initial OpenTelemetry Demo Experiment

We initially attempted to use the OpenTelemetry Demo application (v2.0.2) as our telemetry source to create a realistic microservices environment. However, this approach proved unsuitable for fair agent comparison. The OTel Demo is inherently instrumented for OpenTelemetry protocols, generating rich telemetry data optimized for the OpenTelemetry Collector's native ingestion capabilities.

When routing this data through Fluent Bit's OpenTelemetry plugin, the volume differential became significant: over a 1-hour window, Fluent Bit processed approximately 80% less telemetry data compared to the OpenTelemetry Collector due to protocol translation overhead and data structure incompatibilities. This created an unfair comparison where the agents weren't processing equivalent workloads, making performance metrics non-comparable.

Running identical workloads for the same time period revealed significant telemetry data processing differences:

Key Findings:

• Metrics processing: Fluent Bit captured 93% fewer metric records
• Trace coverage: 95% reduction in trace data capture
• Log correlation: 82% fewer log records with corresponding trace correlation

Consequently, we pivoted to using node-exporter as a neutral telemetry source, ensuring both agents process identical data volumes and formats for accurate performance profiling.

Test Results

Key Insight: On edge devices, idle overhead is practically identical. OpenTelemetry Collector carries a small constant RAM overhead (~50 MiB). Under load, Fluent Bit maintains flatter CPU/memory usage and avoids long I/O stalls, while Collector scales up resource usage for richer in-pipeline processing.

Detailed Resource Analysis

CPU Utilization (Host Level)

Conclusion: At idle, both agents are essentially free with no meaningful CPU tax.

Memory Breakdown (Host Level)

Conclusion: Collector's resident footprint is smaller at idle, but under load this flips (Collector grows, Fluent Bit stays leaner).

Disk I/O Performance

Conclusion: Both barely touch disk when idle; Collector shows rare but taller spikes.

Pressure Stall Information (PSI)

Conclusion: PSI is minimal across the board, but Collector's background activity produces slightly higher IO stalls.

Network Activity

Conclusion: Collector emits significantly more baseline network traffic (control frames, keep-alives, metrics) even when quiet.

Performance Analysis Summary

What This Means

Idle Cost: Negligible for both agents (~1% CPU, few hundred MiB RAM, near-zero disk)

Resource Patterns:

• Collector's background network & IO stalls are higher
• Impact is invisible on high-performance infrastructure
• Can matter on edge nodes or eBPF-heavy hosts

Under Load Behavior:

• Fluent Bit: Maintains flatter CPU/RAM usage, avoids long IO-full stalls
• OpenTelemetry Collector: Scales up resource usage in exchange for richer processing capabilities

Key takeaway: Fluent Bit’s leaner in-process memory and heavier file-cache usage aligns with its “small-batch, high-frequency” design. The Collector prefers “bigger-batch, in-memory” work, trading higher RAM for reduced syscall overhead.

Deep Dive: Memory Internals Analysis

Test Context

Environment: 1-vCPU/2 GiB nodes running side-by-side comparison
Note: While absolute values are small on this 2 GiB test box, the memory usage patterns reveal important architectural differences.

Virtual Memory Management

Committed Memory

Page Cache Behavior

Dirty/Writeback Pages:

• Both agents show small, periodic dirty spikes (dozens–hundreds of KiB)
• Near-zero "writeback now" activity
• Conclusion: Page cache is functioning optimally; no swap pressure or sustained flush issues

Memory Allocation Patterns

Kernel Slab Usage

File-Backed vs Anonymous Memory

Analysis:

• OpenTelemetry Collector: More memory-mapped code/segments (Go runtime + processors)
• Fluent Bit: C buffers/queues use anonymous memory, less file-backed mappings

LRU (Least Recently Used) Memory Behavior

Active vs Inactive Memory Pages

Key Insights:

• Fluent Bit: Maintains more active cache and larger hot working set
• OpenTelemetry Collector: Smaller active cache, more memory appears as mapped

Performance Implication: Fluent Bit keeps more file cache hot (optimal for frequent small writes/reads), while OTel Collector's memory is more mmapped (better for batching & fewer syscalls).

System Memory Regions

Protected Memory Areas

Architecture Summary

Fluent Bit Memory Model:

• Shape: Hot file cache + lean process
• Strength: Excellent for high-frequency, small-batch operations
• Trade-off: More syscalls, but efficient cache utilization

OpenTelemetry Collector Memory Model:

• Shape: Larger mmaps/queues + cooler cache
• Strength: Superior for complex in-pipeline processing
• Trade-off: Higher memory overhead, but fewer syscalls

Decision Framework: Choose based on your traffic pattern and the amount of "thinking" you need in-pipeline. Fluent Bit excels at efficient data movement, while OTel Collector excels at data transformation and processing.

VMStat Analysis: Page Cache Behavior

Test Environment

Configuration: 1-vCPU / 2 GiB node comparison (Fluent Bit vs OpenTelemetry Collector)

Page Cache Activity

Paging Operations (In/Out)

Analysis: Fluent Bit interacts with the page cache significantly more, showing higher steady page-in/out activity and taller spikes. This aligns with its small-batch, frequent I/O pattern, while the Collector's bigger batches keep paging activity calmer.

Page Fault Analysis

Minor vs Major Page Faults

Key Insights:

• Both agents keep their working sets in RAM (major faults ≈ 0)
• Fluent Bit shows more minor-fault churn, consistent with frequent small buffer operations
• No disk-backed memory pressure on either agent

Memory Pressure Indicators

Swap and OOM Analysis

Health Verdict: No memory pressure detected on either agent. The differences observed are about memory churn patterns, not capacity limitations.

Technical Notes

Monitoring Artifacts: Small negative values in page-in/out metrics are normal artifacts from rate() calculations over counter resets—not indicative of system issues.

Memory Behavior Summary

Fluent Bit Memory Pattern

• Characteristic: Higher page-cache churn with more minor faults/page-ins
• Benefit: Very predictable, low-latency write operations
• Best For: Environments where consistent, small-batch processing is preferred

OpenTelemetry Collector Memory Pattern

• Characteristic: Calmer paging overall with periodic large flushes
• Benefit: More efficient batching with fewer kernel interactions
• Best For: Environments where burst processing and complex transformations are needed

Selection Criteria: Choose the memory behavior pattern that complements your node's overall workload characteristics and resource constraints.

Comprehensive System Analysis: VM, Process & Clock Behavior

Memory & Virtual Memory Activity

Committed Memory Analysis

Status: Both agents operate well within memory commit limits

Memory Mapping Patterns

Key Insight: OpenTelemetry Collector relies more on memory-mapped segments (plugins, batching, encoders), while Fluent Bit uses more traditional heap allocation patterns.

Page Cache & Writeback Behavior

Analysis: Fluent Bit generates more frequent small dirty pages; OTel Collector amortizes writes into fewer, larger bursts.

LRU (Least Recently Used) Balance

Performance Impact: Fluent Bit maintains a larger hot working set; OTel Collector leaves more mapped memory that's easily reclaimable.

System Memory Regions

Paging, Faults & Swap Analysis

Page I/O Operations

Memory Fault Analysis

Conclusion: Fluent Bit touches more new pages (consistent with high-frequency, small-batch pattern), while OTel Collector's larger in-memory queues result in fewer page faults overall.

Swap & OOM Status

System Clock & Time Synchronization

Conclusion: No clock synchronization bias introduced by either agent

Process & CPU Scheduling Behavior

Process Queue Analysis

Process Fork Patterns

Conclusion: OpenTelemetry Collector spawns more short-lived helper processes for exporter retries and pipeline management.

CPU Utilization per Core

Conclusion: OpenTelemetry Collector is the heavier CPU user on identical workloads—still within healthy limits but more resource-intensive.

Performance Characteristics Summary

Fluent Bit Profile

• Pattern: Small batches, more page churn, higher minor faults, frequent writeback
• CPU Usage: Lower sustained CPU consumption
• Memory Behavior: More traditional heap-based allocation
• I/O Pattern: High-frequency, small-batch writes

OpenTelemetry Collector Profile

• Pattern: Larger batches, more memory maps, fewer page faults
• CPU Usage: Higher CPU consumption with occasional fork spikes
• Memory Behavior: More memory-mapped segments and shared memory
• I/O Pattern: Batched, amortized writes

Architecture Trade-off: Both designs are valid but stress different kernel subsystems. Fluent Bit's frequent disk activity is noticeable on slower storage; OpenTelemetry Collector's CPU overhead is felt on resource-constrained systems.

Resource Usage Patterns

Decision Framework

Choose Fluent Bit when:

• CPU efficiency is paramount
• Simple data forwarding is sufficient
• Resource constraints are tight
• Predictable, low-latency processing is needed

Choose OpenTelemetry Collector when:

• Rich in-pipeline transformations are required
• CPU resources are abundant
• Complex routing and processing logic is needed
• Future-proofing and vendor neutrality matter

Conclusion: Fluent Bit remains the lean data shipper, while OpenTelemetry Collector serves as the feature-rich processing pipeline. Choose based on whether you prioritize resource efficiency or processing capabilities.

Storage & Disk Performance Analysis

Resource Utilization Summary

CPU Performance Comparison

Key Finding: OpenTelemetry Collector requires significantly more CPU resources for identical workloads.

Memory Usage Analysis

Paging & Memory Fault Behavior

Disk I/O Characteristics

Write Pattern Analysis

Conclusion: Neither agent approaches disk limits; both maintain healthy I/O patterns with minimal wait times.

System Health Indicators

Performance Pattern Analysis

Fluent Bit Architecture

• Strategy: Leverages page cache and kernel writeback
• I/O Pattern: Smaller, more frequent I/O operations
• Trade-off: More vmstat activity and faults, lower total CPU usage
• Best For: Cost-efficient log/metric shipping with minimal resource overhead

OpenTelemetry Collector Architecture

• Strategy: Maintains larger batches in user-space
• I/O Pattern: Fewer, larger batch operations
• Trade-off: Higher CPU and memory usage, reduced syscall churn, cleaner paging
• Best For: Complex in-pipeline processing, transformations, and multi-protocol support

Performance Trade-off: Choose Fluent Bit for efficiency-focused deployments, or OpenTelemetry Collector when advanced processing capabilities justify the resource premium.

Key Performance Metrics

Quick Reference Numbers

Capacity Planning & Cost Analysis

Infrastructure Sizing (24×7 Operations)

Target: ~50% headroom on production workloads

Cost Savings: Fluent Bit can deliver 25–35% instance-cost savings for edge shipping scenarios.

Performance Verdict

Fluent Bit emerges as the pragmatic performance and cost choice for straightforward telemetry shipping, offering:

• Lower CPU overhead (~50% less than OTel Collector)
• Reduced memory footprint
• Cost-effective scaling (25–35% infrastructure savings)

OpenTelemetry Collector justifies its resource premium when advanced capabilities are required:

• Rich processing pipeline features
• Multi-protocol support
• Complex transformation capabilities

Decision Framework: Choose based on whether you prioritize operational efficiency (Fluent Bit) or processing capabilities (OpenTelemetry Collector). Both integrate excellently with Parseable for comprehensive observability solutions.

Network Traffic Analysis

IP Throughput Comparison

Packet Processing Rates

Note: "mp/s" = milli-packets per second

Socket & Network Stack Usage

Network Health Status

Network Behavior Analysis

OpenTelemetry Collector Network Profile:

• Sends richer control/metrics messages via gRPC/HTTP
• Higher metadata overhead per transaction
• More frequent batch transmissions
• Increased kernel softnet processing load

Fluent Bit Network Profile:

• Lean protocol usage with minimal overhead
• Fewer concurrent connections
• Lower packet rates with efficient batching
• Reduced kernel network processing

Network Performance Verdict: Both agents maintain healthy network operation with zero packet drops or errors. However, OpenTelemetry Collector generates 5-6× more network traffic and requires 5× more kernel network processing due to its richer telemetry pipeline. Choose Fluent Bit for bandwidth-constrained environments.

Conclusion

This comprehensive profiling experiment emerged organically while our team was instrumenting and setting up the OpenTelemetry Demo application for Parseable integration. What started as a routine demo setup evolved into a fascinating deep-dive into observability agent performance characteristics.

The Journey

While working on creating seamless integration guides for both Fluent Bit and OpenTelemetry Collector with the OTel Demo application, we realized we had the perfect opportunity to conduct a rigorous side-by-side performance comparison. The controlled environment, identical workloads, and comprehensive monitoring setup provided an ideal testbed for agent profiling.

Key Findings Summary

Fluent Bit emerges as the efficiency champion:

• 50% lower CPU consumption (~27-30% vs 40-45% per core)
• Smaller memory footprint with intelligent kernel page cache utilization
• Network-friendly with 5-6× less traffic generation
• Cost-effective for straightforward telemetry forwarding scenarios

OpenTelemetry Collector excels in processing capabilities:

• Rich transformation pipeline with extensive processor ecosystem
• Multi-protocol support for complex telemetry routing
• Future-proof architecture with vendor-neutral design
• Enterprise-ready for sophisticated observability requirements

Total Metrics Count Analysis

To validate data processing volumes, we performed a SELECT COUNT(*) query on the telemetry data ingested by each agent over the test period:

Analysis: Both agents processed nearly identical data volumes, with OpenTelemetry Collector handling slightly more records. This minimal difference (0.4%) confirms our test methodology provided a fair comparison baseline, ensuring performance differences reflect agent efficiency rather than workload variations.

Practical Integration Guides

Based on this research, we've created comprehensive setup guides for both agents:

• Fluent Bit with OpenTelemetry Demo Application - Lightweight telemetry collection setup
• OpenTelemetry Collector with OpenTelemetry Demo Application - Full-featured observability pipeline

Both guides provide step-by-step instructions, configuration examples, and integration patterns with Parseable for immediate implementation.

What's Next: Data Format Performance Analysis

This profiling experiment was incredibly fun and revealed another fascinating research avenue. We've also benchmarked how each agent performs with different data serialization formats:

• Protocol Buffers (protobuf) - Binary efficiency vs processing overhead
• JSON - Human-readable vs bandwidth implications
• Compression strategies - gzip, snappy, and their impact on agent performance

The data format choice significantly affects CPU utilization, network bandwidth, and end-to-end latency. We'll be publishing a dedicated blog post on "Telemetry Data Format Performance: Protobuf vs JSON in Observability Pipelines" with detailed benchmarks and optimization recommendations.

Final Thoughts

Choosing between Fluent Bit and OpenTelemetry Collector isn't about finding a "winner" - it's about matching the right tool to your specific requirements. Whether you prioritize operational efficiency or processing capabilities, both agents integrate excellently with Parseable to deliver comprehensive observability solutions.

The real winner? The observability community, which benefits from having multiple high-quality, open-source options that excel in different scenarios.

Want to try these setups yourself? Check out the Github repository for the OpenTelemetry Demo Application.