What is Server Profiling in Unreal Engine?

Server profiling is the process of collecting and analyzing performance data from a dedicated game server in order to understand how it consumes critical resources such as CPU, memory, and network bandwidth.

In Unreal Engine, server profiling provides visibility into how systems like gameplay, simulation timing, and replication behave under real multiplayer load. The goal is to identify performance bottlenecks and limits, and stability risks before they impact players or production infrastructure.

A server profiling process typically helps developers answer questions such as:

• What factors are limiting or over-consuming CPU, and why (for example, player count, tick rate, AI, physics, or replication)?

• Which subsystems dominate per-tick cost, and how changes to tick rate affect gameplay quality, simulation stability, and network usage?

• What causes network jitter, replication spikes, or server-side desynchronization?

• Which allocations drive memory usage, how memory scales over time or per player, and what conditions could lead to out-of-memory (OOM) crashes?

How to Profile Servers in Unreal Engine & Best Server Profiling Tools?

Epic Games provide any Unreal Engine users with profiling tools that can be used with Edgegap:

Unreal Trace Server

Unreal Trace Server is the “collector” of server profiling data.

In details, its a lightweight service that collects, stores, and streams trace data emitted by Unreal Engine applications during runtime.

It enables tools like Unreal Insights to connect to running servers or recorded sessions to analyze the CPU, memory, networking, and timing behavior in real time or offline.

How to use it with Edgegap:

1. Configure tracing in your Unreal Engine server (built-in and custom events).

2. Save trace on the server disk with -tracefile, upload to 3rd party storage and analyze offline,

   • Alternatively, stream trace data with Edgegap’s Deployments for internal port 1981over UDP protocol.

Unreal Insights

Unreal Insights is the “recorder” of server profiling data.

In detail, it is Unreal Engine’s performance analysis and visualization tool for examining trace data captured from running applications and servers.

It allows developers to analyze CPU execution, memory usage, threading, networking, and timing behavior to identify bottlenecks and scalability issues.

How to use it with Edgegap:

1. Analyze Memory Insights and Networking Insights.

2. Leverage these insights to Optimize Server Builds.

Edgegap’s Container Logs

Available within your game server deployment’s detail page, container logs are every action that happens within the deployment.

It helps you inspect your game server’s logs in case of issues, or when debugging:

Warning: Once deployment stops, container logs are deleted. Set up third party S3 log storage to save logs.

Review container metrics (processor, memory, networking) to:

• Identify common connection issues when troubleshooting;

• Detect inefficient implementation patterns causing spikes in resource usage;

• Pinpoint inefficient resource usage in particular scenarios;

• Verify changes in your server’s resource usage during optimization;

• Benchmark your server initialization resource consumption and duration.

• Note: History metrics display value averages with 1 minute time period, available in Free tier.

Additional deployment information can be retrieved in JSON format:

• From inside the deployment (game server), using Deployment Context API

• From outside the deployment (backend / third party), using Deployment Status API

Edgegap’s Container Metrics

Also available within your game server deployment’s detail page, container metrics (as the name implies) provide performance data of your container across processor, memory, networking.

Edgegap’s Dashboard Monitoring

Edgegap’s Dashboard provides you with the ability to monitor your server scalability with in-depth analytics.

Available for users who upgrade to the “pay as you go” (just add a credit card to your free account, no spend required) it provides insights on:

• General Insights: monitor releases with live server count per version + resource usage overview,

• CPU Insights: troubleshoot lagging servers due to processor-heavy operations,

• Memory Insights: mitigate server restarts due to exceeding allocated memory,

• Networking Insights: detect inefficient networking patterns and optimize netcode.

What Resource to Evaluate in Server Profiling and Why

Ultimately, reducing resource usage of your dedicated game servers means 1. Improves your players’ experience and 2. Lower your cloud computes costs.

It is thus key to assess each of the following elements and review, based on your tests and thus unique situation, what you should spend time optimizing.

Note: there’s obviously more, but the following checklist is to help you get started with your server profiling process.

How to A/B Test

1. Compare metrics under different player counts to measure scaling.

2. Iterate on optimization and validate against performance targets.

CPU Insights

Objective: Reduce CPU bottlenecks to allow higher player count or smaller instance sizes.

What to review:

• Identify high-cost subsystems per tick (AI, physics, replication, gameplay logic).

• Adjust servers tick rates to balance simulation fidelity vs. CPU load.

• Check for thread contention or stalled tasks in the Task Graph.

• Optimize heavy loops or per-tick events.

Memory Insights

Objective: Minimize memory footprint and prevent OOM crashes, enabling denser server packing.

What to review:

• Monitor persistent allocations (UObjects, large arrays).

• Track transient allocations and reduce temporary object churn.

• Evaluate garbage collection frequency and duration.

• Ensure memory scales linearly with player count.

Network Usage Insights

Objective: Reduce egress usage and network spikes, improving stability and supporting more concurrent players.

• Profile replication of actors and properties; remove unnecessary updates.

• Reduce packet size and frequency where possible.

• Use property delta replication and compression.

• Monitor bandwidth spikes when players join/perform actions.

Tick Time & Frame Stability

Objective: Maintain stable tick rates to avoid CPU spikes and network desyncs.   

Asset & I/O Usage

Objective: Reduce runtime I/O spikes that indirectly impact CPU and networking performance.

What to review:

• Profile asset streaming and runtime loading.

• Monitor serialization cost for replication and save/load operations.

• Reduce disk I/O spikes that can affect the CPU/network.