Gaming apps monitoring made simple with Logz.io

Game developers are challenged when it comes to gathering deep telemetry data in their games. Milliseconds matter! How do you manage to keep your overhead low while minimizing data gaps to help you understand player’s in-game experience at scale?

Logz.io’s cloud-native observability platform enables gaming industry engineers to use the best open source tools to monitor and troubleshoot their games and supporting infrastructure; allowing teams to understand how many servers, how many players, on which platform are impacted at any given time.

Companies including Unity and Mediatonic use Logz.io to increase productivity, reduce MTTR, and improve player experience.

Overwhelmed with monitoring data? Focus on the data that matters to monitor gaming apps and infrastructure more effectively.

Real-Time Performance Monitoring for Game Launches
Real-Time Performance Monitoring for Game Launches

Our platform covers operations by tracking new player entrances and production issues across gaming experiences. Near real-time, time series data makes its way to Logz.io where teams create customized, detailed visualizations in master dashboards.

Multi-Cloud Application
Multi-Cloud Application

For game developers, Logz.io has the ability to ingest logs from multiple cloud services and gather data from their game-centered services, like:

  • AWS Lumberyard
  • AWS GameLift
  • Azure PlayFab
  • Agones by GCP
  • Open Match by GCP
“Logz.io has really helped us gain insight from our own data. We had already tried an in-house ELK deployment, and the move to Logz.io has proven itself as being extremely valuable to us. Their constant care for the product, and their keen focus on what provides value for their customers, has saved us both time and money we would otherwise have to spend on maintaining our own system.”
Claus Nielsen
DevOps Engineer at Unity
Recognizing Patterns
Recognizing Patterns

Log Patterns by Logz.io makes quick work of separating the wheat from the chaff: it uses advanced clustering techniques to analyze millions of logs.
Built on top of a self-learning engine, Log Patterns refines its existing patterns and adds new ones.

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