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F5 BIG-LTM 4000S vs. F5 BIG-LTM 3900-8G-R: A Technical Showdown for High-Performance Load Balancing
Jun 25 , 2025 154

F5 BIG-LTM 4000S vs. F5 BIG-LTM 3900-8G-R: A Technical Showdown for High-Performance Load Balancing

When optimizing application delivery in enterprise environments, F5 Networks’ BIG-LTM series offers scalable solutions tailored to diverse needs. However, selecting between the BIG-LTM 4000S and BIG-LTM 3900-8G-R requires balancing cost, performance, and future scalability. Here’s a detailed analysis to inform your decision.


1. Core Functionalities

Both models deliver robust load balancing and traffic management:

  • SSL/TLS Offloading: Accelerate encrypted traffic with dedicated crypto acceleration.

  • Traffic Steering: Advanced routing, caching, and compression for optimized workflows.

  • Security: Integrated DDoS protection and web application firewall (WAF).

4000S Exclusive Advantages:

  • Predictive Analytics: Machine learning-driven insights for proactive scaling.

  • Edge Computing Integration: Direct connectivity with F5’s Distributed Cloud services.

  • Multi-Tenancy: Isolate workloads with granular partitioning for service providers.

3900-8G-R Focus Areas:

  • Cost-Effective Simplicity: Streamlined deployment for small to mid-sized businesses.

  • Reliability: Proven track record for stable performance in predictable workloads.


2. Performance Benchmarks

SpecificationF5 BIG-LTM 4000SF5 BIG-LTM 3900-8G-R
Max Throughput2.4 Tbps300 Gbps
Concurrent Connections6 Billion200 Million
TLS Transactions/sec1.2 Million80,000
Latency<0.5ms (ultra-low latency)<2ms (optimized for general use)
MACsec SupportYes (encrypted backbone networks)No
F5-BIG-LTM-4000S vs F5-BIG-LTM-3900-8G-R(水印).jpg


3. Design & Aesthetics

  • BIG-LTM 4000S:

    • 2U chassis engineered for high-density deployments.

    • Redundant fans, power supplies, and network interfaces.

    • Modular architecture with up to 8 expansion slots for 100GbE/400GbE cards.

  • BIG-LTM 3900-8G-R:

    • Compact 1U design with fixed configuration.

    • Limited to 4 expansion slots for 10GbE/25GbE ports.

    • Streamlined interface with essential LEDs and USB ports.


4. User Experience

  • Management Interface:

    • Both offer GUI (F5 BIG-IP Configuration Utility) and CLI.

    • 4000S adds iControl REST API and Ansible automation for DevOps.

  • Troubleshooting:

    • 4000S includes LiveDashboard for real-time analytics; 3900 relies on basic logs.


5. Pricing Structure

  • BIG-LTM 4000S:

    • List Price: 50,000–70,000+.

    • ROI: Ideal for enterprises prioritizing scalability and AI-driven optimization.

  • BIG-LTM 3900-8G-R:

    • List Price: 10,000–15,000.

    • Best For: SMBs seeking a reliable, entry-level ADC without advanced features.


6. Battery Consumption

  • BIG-LTM 4000S:

    • Higher power draw (~1,500W) due to redundancy and advanced hardware.

    • ENERGY STAR® certified for efficiency.

  • BIG-LTM 3900-8G-R:

    • Moderate power consumption (~300W), aligning with eco-friendly budgets.


7. Compatibility & Scalability

  • BIG-LTM 4000S:

    • Expandable to 32x 100GbE ports and 16x 400GbE ports.

    • Configurable with up to 64GB RAM and 8TB NVMe storage.

  • BIG-LTM 3900-8G-R:

    • Limited to 4x 25GbE ports and 8GB RAM.

    • Best for static environments with minimal growth expectations.


8. Software Ecosystem

  • BIG-LTM 4000S:

    • Runs TMOS 17.x with AI/ML-driven optimizations (e.g., Traffic Steering Advisor).

    • Priority access to beta features and extended support contracts.

  • BIG-LTM 3900-8G-R:

    • Limited to TMOS 16.x with standard feature sets.

    • Annual updates focused on security patches.


Final Verdict

  • Choose BIG-LTM 4000S If:
    You require extreme scalability, AI-driven insights, and future-proof 400GbE readiness for hybrid cloud architectures.

  • Choose BIG-LTM 3900-8G-R If:
    Your environment demands a cost-effective, reliable ADC for straightforward load balancing without advanced customization.

Pro Tip: Invest in the 4000S if your organization plans to adopt edge computing, AI/ML workloads, or 5G edge deployments within 3–5 years.




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