3 Common Misconceptions About Scaling Real-Time Infrastructure

As developers build more data-driven, interactive systems from sports betting platforms to financial dashboards and multiplayer apps, real-time infrastructure becomes critical. But too often, scaling that infrastructure reveals ugly truths buried beneath good intentions and half-truths. 

Let’s unpack the 3 biggest lies developers tell themselves when building real-time systems and what to do instead. 

Lie #1: “WebSockets Are All I Need” 

Reality:
WebSockets are a transport, not a solution. Yes, they open a persistent connection. But without: 

• Topic-based routing
• Session filtering
• Message control
• Throttling
• Retry logic
  …WebSockets become firehoses, not frameworks. 

The result?
You end up reinventing the wheel with brittle, hand-rolled solutions, ones that don’t scale past thousands of users. 

What to do instead: Use WebSockets within a purpose-built real-time data layer that handles routing, personalisation, and observability. 

Lie #2: “We’ll Scale It Later” 

Reality:
If your infrastructure can’t scale now, it won’t scale later, at least not without major rewrites. “Later” becomes when: 

• Your user count spikes overnight
• Your egress costs explode
• Your polling overwhelms the backend 

Real-time systems often grow non-linearly. A design that works for 1,000 users may fail miserably at 10,000, especially with full payload duplication, aggressive polling, or no topic abstraction. 

 What to do instead: Design for delta efficiency and broadcast selectivity from the start. Assume success and plan for it. 

Lie #3: “More Infrastructure = More Control” 

Reality:
Over-building often creates fragility, not power. Developers love to tinker. But bolting together Kafka, Redis, custom pub/sub, and multiple sync layers often leads to: 

• Complicated deployment pipelines
• Latency bottlenecks
• Debugging nightmares 

More control is great, until it steals your time and buries your team in complexity. 

What to do instead: Choose a unified real-time platform that’s built for data delivery, not just messaging. Look for features like: 

• Topic trees
• Role-based access
• Push-first architecture
• Observability by session, topic, or region 

 The DiffusionData Takeaway 

DiffusionData was built to solve the exact pain points developers face at scale: 

• Delta streaming (sends only what changed)
• Session-based filtering (stream only relevant data)
• SDKs in multiple languages (integrate fast)
• Throttle + transform in motion (not at rest) 

You don’t have to bolt together WebSockets, Kafka, and half a dozen patches. You can stream intelligently from day one and scale without rewriting. 

Final Thought 

Scaling real-time infrastructure isn’t about adding more tools, it’s about building the right architecture. If you’re still telling yourself: 

“WebSockets are good enough”
“We’ll fix it when we grow”
“I need to control everything manually” 

…then you’re not designing for resilience. You’re designing for rework. Stop scaling problems. Start scaling performance. 

Try Diffusion Cloud for free and discover hands on how Diffusion handles real-time delivery at scale: Sign up for free