How Aerospike is powering enterprise ops with AI, vector search, and real-time data

Srini V. Srinivasan is the Co-founder and Chief Technology Officer of Aerospike, and a recognized database pioneer from Silicon Valley. With over 20 years of experience building and running high-scale infrastructure systems, Srini brings deep technical expertise in distributed systems, real-time-data, and database architecture. He holds more than a dozen patents spanning web, mobile, and data technologies. Before founding Aerospike, he led engineering teams at Yahoo, where he encountered the scale challenges that would inspire Aerospike’s creation. Srini holds a B.Tech in Computer Science from IIT Madras, and an M.S. and Ph.D. in Computer Science from the University of Wisconsin-Madison.

In this exclusive virtual interview with DATAQUEST, Srini Srinivasan discusses the transformative role of vector databases, the challenges of scaling AI at real-time speed, the evolving architecture of data systems, and Aerospike’s deepening footprint in India’s digital economy. Drawing from rich experience in high-scale infrastructure, Srini offers sharp insights into how real-time data, NoSQL, and AI-driven innovation are converging to define the next chapter of enterprise technology. Excerpts.

Vector databases are gaining traction for scaling AI systems. How do you see them transforming real-time data handling, and what's Aerospike's role here?

Vector search is a key component in AI-powered search. For instance, in image or recommendation searches, you generate a vector for a query item and compare it to existing vectors to find the closest matches. This applies to various domains like security, person identification, and related text search. The ability to combine vector search with Retrieval-Augmented Generation (RAG), graph, and key-value search creates powerful, intelligent applications. Aerospike’s role is to offer a high-performance database infrastructure that supports these hybrid search techniques at scale. 

Given the data heterogeneity across SQL, NoSQL, and legacy systems, how do enterprises unify their data for real-time insights?

Many of our customers, like Airtel and Flipkart, operate at massive scale and rely on real-time insights. They have data across multiple legacy and modern systems. A common pattern we see is streaming data from diverse systems using tools like Kafka into Aerospike. This enables a real-time, unified customer 360 view. Over time, as confidence in Aerospike grows, some customers even make it their system of record. However, we often coexist with other databases, allowing seamless data aggregation without full migration.

Enterprises are struggling to scale AI projects and align them with business goals. How does Aerospike support this evolution?

We've supported AI/ML use cases for over a decade. Early applications included fraud detection and real-time recommendations. These require low-latency access to high-volume data. With today’s explosion in AI, including generative and agentic models, the challenge is integrating ever-changing real-time data. While tools like ChatGPT handle static content well, incorporating dynamic data streams into AI-driven decision-making is still evolving. We're actively working with customers to bridge this gap, ensuring ROI from real-time AI implementations.

Analysts say AI is reshaping data architectures. What are the trends you're seeing, and how is Aerospike adapting?

We see a strong shift across industries toward real-time analytics: fraud detection, threat assessment, recommendations, etc. A major trend is combining different data models—vector, graph, text, in a unified system. Aerospike's platform supports this convergence. Our ability to ingest massive volumes of streaming data and execute real-time queries allows companies to make instant decisions. The traditional limits of how much data can be processed in real time have been pushed back significantly.

Do you think batch processing is fading. Do you agree?

Absolutely. Historically, batch existed because source or target systems couldn’t handle real-time loads. We've seen this evolution in new age tech companies in gaming, adtech and fintech, and now it's reaching traditional enterprises. Real-time processing has become a necessity for delivering timely, personalized services at scale. Batch won’t disappear overnight, but all new systems are gravitating toward streaming and event-driven models.

How is Aerospike positioned in India, especially with the real-time push from UPI and digital ecosystems?

Aerospike has been part of India’s digital growth since 2011. Companies like Flipkart, Snapdeal, Airtel, and Dream11 use Aerospike for real-time personalization, payments, and customer insights. For example, during Flipkart’s Big Billion Day, we handled 95 million transactions per second, supporting personalized experiences for 450 million users. Through partners like Mindgate, we also support hundreds of millions of UPI transactions daily. India is at the forefront of real-time, consumer-grade workloads, and Aerospike is deeply embedded in this ecosystem.

What's your outlook on NoSQL as AI continues to mature? And where is Aerospike focusing in the next few years?

NoSQL is well-suited for AI because of its flexibility and scalability. Aerospike is focused on three priorities: first, being fully cloud-native and easy to consume on all major clouds; second, advancing vector search and graph capabilities relevant to AI; and third, strengthening community engagement. We aim to support AI strategies with high throughput, low latency, and large-scale data ingestion, especially for regions like India that operate at massive consumer scale. Our roadmap includes deeper AI integration, real-time data models, and continued leadership in performance-driven architectures.

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