When I joined the multiplayer game project, the product concept was already strong. DJs could host a jam, play a set of songs, and players would guess the right songs in a pattern, similar to bingo. The product experience worked, but the infrastructure could not keep up. Sessions crashed once 70-80 players joined.
I was assigned as the DevOps engineer to implement autoscaling and make the game reliable for larger audiences. At first glance, it looked like a simple capacity problem: add resources as player count increased. The real issue was more specific. The workload spiked around gameplay events, not in a clean linear pattern.
Understanding the Bottleneck
The frontend was built on Next.js. Observing the crashes, it quickly became clear that the spike happened when players moved from the waiting room into gameplay. Normal Horizontal Pod Autoscaling (HPA) would not work well here because the resource spike was not proportional to CPU or memory. It was tied to a specific gameplay transition. I decided to use KEDA (Kubernetes Event-driven Autoscaling). I implemented a metric API that pings the server every few seconds. When player count in the waiting room hit a threshold, KEDA would scale the Next.js pods automatically.
Deep Monitoring
Normal GKE observability didn’t give enough insight. So I added kube-prometheus-stack and Headlamp to monitor resource consumption in detail. That’s when patterns became clearer. The backend was not as idle as I expected. The multiplayer game’s Hocuspocus service was running in the same pod as the API. When Hocuspocus crashed under load, it brought down the API too. I separated the backend API and Hocuspocus into their own pods with one container per pod. That isolation immediately improved stability.
Non-linear Resource Patterns
Then came the tricky part: resource usage wasn’t linear. During the waiting room stage, the frontend peaked as everyone loaded at once. Backend usage stayed low. Once the game started, frontend stabilized, but Hocuspocus usage rose sharply as players interacted via sockets. As the game progressed, Hocuspocus continued consuming more resources, showing that simple scaling rules wouldn’t cut it. Initially, I thought horizontal scaling Hocuspocus via Redis extension would solve it. But there was a catch: inter-pod communication. Each pod broadcasted updates to all others. With n pods, that’s n*(n-1) interactions—a massive overhead that actually increased resource usage as I scaled.
Finding the Sweet Spot
I ran a series of load tests to determine the optimal configuration. The goal was simple: each pod should support roughly 100–150 players. That meant fewer, bigger pods rather than many small ones, reducing communication overhead while maintaining capacity. After fine-tuning, the multiplayer game could comfortably handle 400+ concurrent players without crashes—a fivefold improvement from the original 70–80 players.
How I Validated the Scaling Claim
I did not treat the 400+ number as a vanity metric. The test focused on the exact flow that was breaking production: players joining the waiting room, moving into gameplay together, and then generating socket-heavy updates through Hocuspocus.
During each run, I watched three signals:
- frontend pod behavior during the waiting-room-to-gameplay transition,
- Hocuspocus CPU and memory growth as socket activity increased,
- and whether API stability degraded when collaboration traffic became noisy.
The important discovery was that adding more small Hocuspocus pods made the system worse because each pod had to broadcast updates to the others. The final setup favored fewer, larger pods, with the frontend scaling through KEDA when the waiting-room player threshold crossed the configured limit.
That is why the final result mattered: it was not just “more pods.” It was a scaling rule tied to the real product event, backed by observability and load-test feedback.
Optimizing CI/CD for Next.js
Scaling the infrastructure was only half the story. The Next.js build pipeline was slow—over 6 minutes per build. For fast iteration, this needed optimization. I implemented several strategies: Turbo prune - only included required dependencies in each build instead of the entire monorepo. Next.js standalone output + multi-stage Docker build - reduced unnecessary layers. GitHub Actions caching - avoided rebuilding unchanged Docker layers. Turborepo remote caching - sped up repeated builds across branches and pipelines. The result? Build time dropped by 66%, from over 6 minutes to around 2 minutes. That meant faster testing, faster deployment, and a smoother development workflow.
CI/CD Evidence
The build improvement came from removing avoidable work from the pipeline:
- Docker builds no longer copied the entire monorepo context into every image.
- The Next.js app used standalone output so runtime images carried only what they needed.
- GitHub Actions caching kept unchanged layers and dependencies from rebuilding on every run.
- Turborepo remote caching reduced repeated work across branches and related pipelines.
The practical impact was a shorter feedback loop for the team. Deployments moved from a slow, wait-heavy process to something engineers could run and review more often without blocking product iteration.
Looking Back
Working on the multiplayer game taught me that scaling isn’t just about adding pods or spinning up machines. You have to understand the real resource patterns—when spikes happen, which services interact heavily, and how data flows between them. Observability, careful pod design, and CI/CD optimization together make the difference between a system that barely survives peak load and one that runs smoothly at 5x capacity. It was tricky, sure, but seeing 400+ players interact in real-time without a crash felt pretty satisfying.
Key Results
- Increased game capacity from 70–80 to 400+ simultaneous players
- Implemented event-driven auto-scaling using KEDA and GKE
- Isolated backend services into separate pods for stability
- Optimized Next.js CI/CD pipeline, reducing build time by 66%
- Observed and tuned non-linear resource patterns for real-time gameplay