As organisations scale, their systems often encounter growing pressure from increased data volumes and user traffic, which can impact application performance. With only 31% of organisations found to have successfully scaled AI, challenges such as slower response times, downtime, and system instability become more common without effective strategies in place.
To address these issues, it’s important to consider how systems can be optimised to handle large datasets, higher request volumes, and unpredictable traffic patterns.
There are several practical approaches available to help improve the performance of data-intensive applications, especially in high-demand or real-time environments. These include techniques for reducing latency, enhancing scalability, and strengthening system resilience.
Choose the right cache and database types
As applications grow and handle more data, maintaining speed and responsiveness becomes increasingly important. Users expect seamless, real-time experiences, and when performance lags, they often turn elsewhere. To meet these expectations, developers need infrastructure that can process and deliver data quickly and efficiently.
Caching plays a central role in achieving this. By storing frequently used data closer to the application, caching reduces the load on primary databases, leading to faster response times and better resource efficiency. Techniques like in-memory and query caching help avoid redundant processing, enabling real-time interactions even during periods of high demand. In AI-driven applications, semantic caching can store commonly used prompts and responses, while session caching allows user-specific data such as shopping carts or preferences to load instantly on return.
While cost has historically been a barrier to widespread caching, new approaches that optimise how data is stored are making high-performance caching more scalable and affordable. Beyond caching, tools like vector databases enable fast, contextual search for unstructured data, and NoSQL databases offer flexibility for managing diverse data types, particularly in mobile and transactional environments. Event streaming further enhances responsiveness by allowing apps to process and react to actions in real time, powering features like instant alerts and live updates.
Together, these strategies help modern applications deliver faster, more reliable performance at scale – supporting both growth and user expectations.
Adopt faster data infrastructure
Not all data infrastructure is built the same. Some systems are simply better suited to handle growing demand, helping everything run smoothly as more users join or data volumes increase.
Scaling your data architecture in the cloud, often referred to as hyperscaling, can help maintain fast and responsive performance even during busy periods. This is especially important for applications that depend on real-time interactions, such as e-commerce, gaming, or social media, where even small delays can affect the user experience.
These systems also make it easier to work with large volumes of information, which supports more personalised content and recommendations based on user behaviour. It is the kind of approach used by streaming platforms that suggest shows or films based on viewing history.
Hyperscaling in the cloud is one of the best methods for dramatically improving data infrastructure speeds and app performance.
Simplify data processing
To create a fast, responsive app experience, it’s essential to get the right data to the right user at the right time. When this happens quickly, users enjoy smoother interactions, which often leads to better engagement. Rather than building complex custom logic into the app itself, a smarter approach is to rely on a strong data setup that can deliver exactly what’s needed when it’s needed. This simplifies development, reduces errors, and improves overall performance.
A modern caching and data strategy is essential here. These strategies help apps manage large amounts of information quickly and handle spikes in demand without slowing down. The right setup should be flexible enough to grow with changing needs, resilient enough to stay available during high traffic or outages, and efficient enough to keep costs manageable. With the right tools in place, teams can spend less time managing data and more time improving the user experience.
Streamline development
Improving app performance doesn’t mean spending hours troubleshooting or writing complex code. A well-rounded development environment helps teams to build, test, improve features without getting bogged down by routine maintenance or trial and error. With the right setup, teams can spend more time creating and less time fixing, helping projects move forward faster.
Access to reliable tools and clear visibility into your data can make a big difference. Features like intelligent coding support and easy ways to test and refine queries help speed up workflows and reduce frustration. This is especially critical for applications powered by generative AI, where speed, memory and accuracy are essential. With these systems in place, teams can focus on developing more innovative features and exploring new ideas, rather than getting caught up in technical challenges. Ultimately, a streamlined development environment not only boosts progress but also enables teams to deliver better apps with fewer obstacles.
