Aveekshith Bushan, Vice President and GM, Asia Pacific and Japan, Aerospike
A hyperconnected world is now the norm. Since the Digital India initiative was introduced, the number of internet users in India has grown from 305 million in 2015 to 1.24 billion in 2023, according to a 2024 Statista report. The outcome is a digitally savvy population that readily accepts new technologies.
As digital literacy has expanded, artificial intelligence (AI) has moved from a futuristic concept to being an essential part of modern business-consumer interactions. These AI-driven consumer services are revolutionizing industries, enabling companies to provide personalized, real-time experiences so they can stay competitive. The challenge now is not just how to implement AI, but also how to scale it to meet the growing volumes of data, while maintaining infrastructure and cost constraints – especially when serving millions of users.
Data Overload and Processing Challenges
Data overload occurs when the volume of information surpasses the ability to process it effectively. Recently, X (formerly Twitter) faced a significant data overload issue, with users globally reporting problems such as delays in post loading and search functionality disruptions. This situation highlighted the platform’s struggles to manage real-time data processing effectively, leading to user dissatisfaction.
With advanced algorithms and machine learning techniques, AI holds immense potential for making sense of data, identifying patterns, and extracting valuable insights. But it is also a significant contributor to the data overload problem as it generates even more data at an unprecedented scale. In the above example, the data overload issue on X is partially exacerbated by content generated by AI bots, which increasingly mimic real users to exploit the platform’s engagement-driven algorithms. X’s infrastructure struggled to discern and filter real interactions from bot activity due to the sheer volume of transactions. The inability to distinguish authentic from inauthentic activities results in slower response times for genuine users, increased costs for maintaining and scaling inefficient systems, and user dissatisfaction.
Effectively controlling huge amounts of data requires a balanced and responsible approach such as investing in advanced data analytics and management solutions. Businesses can address this challenge by leveraging real-time databases, which seamlessly integrate with modern applications, handling large data volumes with low latency. They tackle data overload and ensure efficiency, eliminating delays caused by traditional infrastructure. In reference to the above example, real-time databases can quickly process and filter massive amounts of incoming data, differentiating between bot and genuine activity without delaying user responses.
Optimizing Costs in AI Scaling
AI comes with a cost. McKinsey’s estimates that developing a single generative AI model can cost up to $200 million, customizing an existing model with internal data can cost up to $10 million, and deploying an off-the-shelf model can cost up to $2 million. Moreover, AI scaling requires substantial computational power for training models, which again comes with a price. But cost optimization is both a financial and strategic imperative.
For example, Netflix uses AI to deliver personalized recommendations and content streaming to millions of users globally. It faces enormous computational demands to analyse user preferences, predict behaviour, and stream content seamlessly. Scaling AI to handle such high data volumes while maintaining real-time responsiveness is costly due to infrastructure needs such as GPU-based processing and cloud storage.
Efficient data management is key here, which can be achieved by real-time databases. Organizations need to develop a comprehensive understanding of the total cost of ownership. These databases help reduce scaling costs for AI by enabling efficient data processing with low latency, which minimizes the need for over-provisioning infrastructure. They optimize resource utilization by handling large volumes of transactions in real time, ensuring scalability without significant increases in operational expenses.
For instance, by leveraging real-time databases and scalable cloud resources, Netflix optimizes server usage and processes millions of transactions per second, such as viewing habits or search queries, with low latency. This ensures customer satisfaction without requiring exponential infrastructure expansion.
Achieving Balance
To meet the demands of the next generation of hyper-connected consumers, companies must ensure their platforms are equipped to handle vast, dynamic streams of real-time data with unmatched accuracy and resilience. With AI reshaping digital ecosystems, tools for real-time analytics and monitoring are becoming essential components for scaling the systems. These solutions not only enable instant adjustments to models based on live performance metrics but also foster adaptive AI ecosystems that seamlessly manage complexity, ensuring trust, agility, and operational efficiency in an ever-changing landscape.
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