Game Optimization Toolkit Online
Based on visualized data display, providing targeted analysis suggestions and performance parameter trends, facilitating efficient quality control execution
GOT Online is a Unity game performance optimization platform for development and testing teams that need deeper root-cause diagnostics, cross-version trend tracking, and a structured optimization workflow.
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Detect overall performance during project runtime, including engine module statistics, CPU function time, GPU time, heap memory trends, hardware information, and FPS frame rate
Detect overall performance during project runtime, including engine module statistics, CPU function time, GPU time, heap memory trends, hardware information, and FPS frame rate
Display the quantity and memory trends of various resource types during project runtime, as well as specific information and lifecycle of any resource
Display heap memory allocation of script functions during project runtime, mainly including specific heap memory allocation and leak analysis
Analyze Lua CPU time and memory leak issues
Display historical data trends, provide professional suggestions and analysis
Focuses on Unity optimization signals across CPU, GPU, memory, resources, and stutter.
Supports report sharing and collaboration workflows for development and QA teams.
Integrates with automated test pipelines to continuously track performance changes.
Works well with Gears: use Gears for quick checks and GOT Online for deeper diagnostics.
GOT Online is a Unity game performance optimization platform from GameOptim for development and testing teams, covering data capture, bottleneck diagnosis, trend analysis, and regression verification. It is especially useful for teams with frequent releases, multi-role collaboration, and long-term performance governance goals.
GOT Online aligns FPS, CPU/GPU, memory, temperature, power, and network curves on the same timeline. Teams can locate the first abnormal signal and trace it down to specific modules and call stacks to distinguish root causes from chain reactions.
By combining device segmentation, scene splitting, and metric comparisons, teams can quickly identify high-risk device groups and gameplay segments. This helps prioritize fixes with the largest impact.
GOT Online provides cross-version trend comparison and module-level change visibility to identify likely regression sources. Whether changes come from logic, assets, or scene content, teams can narrow ownership faster.
Even when average FPS is acceptable, GOT Online can locate transient anomalies through jank-frame analysis. Combined with stack traces and module cost breakdowns, teams can pinpoint loading, animation, physics, GC, and other experience bottlenecks.
Teams can establish a baseline version, define threshold rules for key metrics, and connect them to automated release gates. After each optimization, they run same-scene revalidation to maintain a stable detect-fix-verify loop.
In long-session tests (30-60+ minutes), GOT Online can capture thermal throttling stutter after overheating, memory accumulation, and periodic spikes. This helps detect slow-burn risks before release.
GOT Online provides a linked multi-metric GPU view to isolate the dominant bottleneck type. Teams can more quickly distinguish bandwidth pressure, shader pressure, and sync-wait inefficiency.
GOT Online supports same-scene multi-plan comparison, so teams can see the combined impact of each change on FPS, jank, memory, power, and thermals. Decisions become based on total outcome instead of single metrics.
With custom APIs, teams can attach business-critical events such as combat bursts, scene transitions, and first-load paths to the performance timeline. This restores the real experience context when user feedback and raw metrics do not align.
SDK integration only requires 3 steps:
Local testing; View on GameOptim official website; Instantly view evaluation reports
Invite members to participate in projects; one upload, multiple sharing
Important engine module indicators; detailed analysis of resources and Mono heap memory; Lua performance analysis; comprehensive performance brief analysis
