Why Roblox Games Become Slower as They Grow
What works for 20 players often fails for 100 because of how the work scales, not because the code was bad.
A game runs well in testing and in its first weeks. Then it grows, and servers stutter, memory climbs, and input feels delayed. Nothing was obviously wrong. The failure comes from work that grew faster than the player count. Finding it means knowing the shapes of cost, not generic advice to optimise.
Work that scales with players squared
The classic case is a loop that visits every player, and inside it, every other player. At 20 players that is a few hundred checks. At 100 it is ten thousand, every time it runs. Proximity checks, leaderboards that recompute all rankings, and effects that compare each player against all others are typical sources.
Common failure modes
- Per-player loops inside per-frame loops
- Work that is small per player becomes large when it runs every frame for everyone.
- Broadcasting
- Sending the same data to every client on each change. Cost per message multiplies by the number of clients.
- Unbounded state
- Tables that grow and are never cleaned up, such as records for players who left or events that were never removed. Memory climbs through the session.
- Leaked connections
- Event connections created on join and not disconnected on leave keep objects alive and keep running.
- Wide replication
- Many instances changing properties often, or large numbers of descendants in the workspace, increase what the engine must process and send.
- Repeated searching
- Scanning large collections each time instead of keeping an index.
- Shared bottlenecks
- Everything waits on one service, one lock-like structure, or one persistence request path.
- Accumulated systems
- Each feature added its own loop, listener and effect. No single one is slow, but together they are.
Why testing at 20 misses it
Small counts hide the shape of the cost. A quadratic loop and a linear one look the same at low numbers. Memory leaks need hours. Network costs depend on how many clients are listening at once. The server may also share load with other factors at peak times that your tests do not recreate.
How to find the real problem
- Measure on the server and client separately. Use the profiling and diagnostic tools Roblox provides for script time, memory, network traffic and physics.
- Test with realistic populations. Simulate load with test clients or scripted bots if you can, and run for long enough to see memory trends.
- Plot cost against player count. A curve that bends upward is the clue to quadratic work.
- Look at what grows over time in a long session: tables, instances, connections.
- List all work that runs on a timer or every frame and attribute cost to each.
- Check the size of messages and how often they are sent.
Fixes that match the cause
| Cause | Direction of fix |
|---|---|
| All-pairs comparison | Spatial grouping, update only nearby or changed entries |
| Broadcasting every change | Send changes only to those who need them, batch updates, send less often |
| Growing tables | Clean up on leave, set limits, store compact data |
| Leaked connections | Track and disconnect when the owner is removed |
| Heavy per-frame work | Move to event-driven updates, or spread work across frames |
| Central bottleneck | Reduce requests, cache, queue and batch |
Design for the target size
Decide the maximum players per server early, and test at that number. Add a performance check whenever a new system touches all players. The question to ask of each: how does this cost change when the server is full?
For the risks of changing performance-sensitive code, read Why Optimizing One System Can Break Another.