My data arrived 1ms late!

John Doe, a platform engineer, was watching his dashboard closely when he noticed a troubling lag. His data wasn’t arriving exactly when it happened. The dashboard wasn’t reflecting the real-time data he needed—critical data that a stock trading analyst relied on to make split-second decisions. He’d poured his time and energy into designing this system, carefully picking the best data streaming technology and the perfect tech stack to make it as real-time as possible. And now, with a 1ms delay, the trading team might miss crucial opportunities. So what should John do?

When we talk about "real-time," most people imagine high-stakes trading floors where milliseconds can mean the difference between profit and loss. And yes, for some data, arriving even 1ms late can indeed be a game-changer. But is every use case worth the intense effort and high cost of achieving near-zero latency? After all, 100ms is still less than a second. The answer depends on the time value of data—how valuable data is when it arrives instantaneously versus a few milliseconds (or more) later.

So how do we decide between ultra-low latency and more relaxed latency? Let’s break down the factors that determine if the chase for low latency is worth it.

Assess the Real-Time Needs of the Use Case

• Immediate Impact on User Experience: For applications where delays are noticeable to users or degrade the experience (e.g., gaming, VR, or real-time communication), low latency (<100ms) is essential.
• Safety and Criticality: Applications related to safety or critical decision-making, like autonomous vehicles or emergency healthcare systems, require low latency to ensure timely actions.
• Tolerance for Delays: If a use case can tolerate slight delays without impacting quality (e.g., streaming analytics dashboards, social media feeds), higher latency is often acceptable.

Determine the Interaction Frequency and Responsiveness Requirements

• High-Frequency Interactions: Workloads that involve continuous, high-frequency interactions, like high-speed trading or multiplayer gaming, benefit from low latency to keep interactions smooth and synchronized.
• Asynchronous or Periodic Updates: If updates can be batched or processed at intervals without affecting performance or insights (e.g., predictive maintenance or IoT sensor aggregation), higher latency is typically acceptable.

Evaluate Technical Requirements and Constraints

• Data Volume and Processing Complexity: Low latency requires fast processing and transmission, which can be costly or complex with large data volumes or intricate computations. If processing time or network transmission could introduce delays, high latency may be preferable.
• Network and Bandwidth Constraints: Low latency often demands stable, high-bandwidth network infrastructure. For workloads running over unreliable or variable networks, higher latency might be more realistic and cost-effective.

Consider Cost Implications

• Resource Cost: Low-latency systems require more computing power, optimized algorithms, and sometimes specialized hardware, which can be expensive. For non-critical or cost-sensitive applications, higher latency may be a viable compromise.
• Scalability Needs: Low-latency infrastructure can become costly at scale (e.g., in distributed systems or global applications). If the application is intended for a large user base or frequent interactions, a balanced latency approach may help reduce costs.

Understand User Expectations and Perceptions

• Perceived Delay Tolerance: For some applications (e.g., retail or content delivery), user perception is more flexible regarding delays, and latencies above 100ms may not be noticeable or frustrating.
• Competitive Benchmarking: Analyze latency expectations within the industry. For instance, finance, gaming, and customer service sectors often have low-latency benchmarks to remain competitive, while others, like analytics, can handle more relaxed latency standards.

Identify Regulatory or Compliance Requirements

• Compliance Requirements: Some industries have strict regulations around response times for data processing (e.g., financial trading), where compliance necessitates low latency.
• Data Privacy and Localization: Regulations that require data to be processed within specific regions may impact latency, as data cannot be geo-replicated globally for speed. If regulations are flexible, higher latency may be acceptable if it lowers complexity.

Weigh the Risk of Latency on Business Outcomes

• Business Impact of Delays: If delays could result in missed opportunities (e.g., in trading) or dissatisfied users (e.g., in customer support), prioritize low latency. However, if slight delays don’t compromise business outcomes, opt for a latency level that balances performance with efficiency.
• Error Tolerance and Retries: High-latency systems often allow more error tolerance, as they can process data in batches or at intervals. If error tolerance is a priority, higher latency may be acceptable, whereas low latency typically requires high accuracy and minimal retries.

Here is the summary guidance for choosing latency optimized vs. latency relaxed / cost optimized:

• Choose Latency Optimized (<100ms)
  • Real-time interaction and immediate responsiveness are essential.
  • User experience will suffer noticeably from delays.
  • Safety or critical business decisions are impacted by response time.
  • The use case demands precise synchronization (e.g., multiplayer gaming, telemedicine).
• Choose Latency Relaxed and Cost Optimized (>100ms)
  • Slight delays are tolerable without impacting functionality or user experience.
  • Batching or periodic processing is feasible, reducing the cost of constant responsiveness.
  • Network infrastructure constraints make ultra-low latency impractical.
  • Costs, scalability, or compliance concerns outweigh the benefits of low latency.

Here are the use cases we've been collected from our customers and community members.

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The Bottom Line: Understanding the Value of Every Millisecond

Ultimately, choosing between low and high latency comes down to the time value of your data. For some applications, every millisecond matters—ultra-low latency can make or break the experience or profitability. But for other use cases, a slight delay won’t impact performance, and allowing some flexibility can free up resources for other critical areas without sacrificing functionality.

At StreamNative, we provide the flexibility to help you choose the right solution for your specific needs. For ultra-low latency requirements, our Classic Engine is optimized to deliver immediate data processing, perfect for high-stakes applications like trading and real-time monitoring. For workloads that can tolerate a bit more time—where near-real-time data is enough—our Ursa Engine is designed to handle relaxed latency with optimal efficiency.

With StreamNative, you can strike the right balance between performance and resource efficiency, making sure your data is there when it counts.

Ready to experience real-time data streaming tailored to your needs? Sign up today and get $200 in free credits to explore both the Classic Engine for ultra-low latency and the Ursa Engine for flexible, high-efficiency workloads.

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