On Deploying AI at the Enterprise Level. Q&A with Nadeem Asghar.

Q1. What are the main performance and scalability challenges when deploying AI at the enterprise level?

Generating reliable business insights at “the speed of now” requires getting an assortment of bespoke or legacy business systems to work in harmony…and that’s no easy feat. The main challenges come in data handling, real-time processing, and high concurrency.

Enterprise AI requires both transactional and analytic workloads, and traditional databases struggle to support both. For example, MySQL and Oracle are poor at analytics, while systems like Snowflake excel at queries but can’t handle fast data ingestion or high-concurrency transactions.

In addition, real-time AI apps (like fraud detection or recommendation engines) demand ultra-fast data ingestion and analysis. Legacy systems can’t keep up, often requiring costly scaling and still falling short on performance and consistency. Some vector databases also suffer from eventual consistency and limited support for diverse data types and advanced search.

SingleStore solves these problems by combining transactions, analytics, and vector search in one high-performance database—supporting real-time AI with fast ingestion, high concurrency, and hybrid search across varied data types.

Q2. You just announced new capabilities to deliver increasing performance required for deploying AI. What are these new capabilities?   

We focused on enhancing five areas: AI, performance at scale improvements, ingest and data integration, Iceberg integration, and improving the developer experience.

Under the heading of AI-specific enhancements, SingleStore’s Aura Container Service, a serverless compute platform optimized for AI and machine learning, now has the ability to host Cloud Functions. These are lambda-style serverless functions that can be used to build DataAPIs, agents, tools for agents, and Inference APIs for embedding models. We’ve also improved query efficiency by automatically re-optimizing queries and added index merger to make vector search 100X faster.

To improve performance at scale and increase scalability, we introduced a new multi-value index for JSON data. We also added cross-workspace-group database branching and attach so developers can quickly create a copy of a database to work on, without affecting the original, across different teams or environments.

To simplify ingest and data integration and strengthen the overall data integration experience known as SingleConnect, we’ve enabled the use of SingleStore Flow within Helios, making it easier to move data to SingleStore from various sources like Snowflake, PostgreSQL, SQL Server, Oracle, and MySQL. To support building intelligent applications on data lakehouses, we also added a speed layer on Apache Iceberg and easier and faster data exchange with Iceberg external tables.  

The bi-directional integration of SingleStore and Apache Iceberg, announced last summer, opened up new opportunities for building intelligent applications on data lakehouses. This year’s enhancements build on that ecosystem by adding a speed layer on Iceberg and offering easier and faster data exchange with Iceberg external tables.

And finally, to make life easier for developers, we made numerous SQrL improvements and added a Multi-Tab SQL Editor as well as features for scheduling and versioning Notebooks. We made it easier to manage sequences and monitor data pipeline, updated the Billing Forecasting UI and integrated with GitHub.  

These enhancements are part of SingleStore’s commitment to being a foundational layer for AI. We aim to help organizations to bring innovative ideas to life faster by providing a quantum leap forward in performance at scale and a faster, smoother path to development.

Q3. You mentioned that these capabilities will help builders and makers “feel the flow” as they deploy AI on their most valuable data. Data quality and accuracy are one of the biggest challenges to deploying AI and ML technology. What is your take on this? 

When we say we want to help developers “feel the flow,” we’re talking about boosting performance, improving data integration, and removing impediments to AI deployment.

You have to remember that AI is only as good as the data it’s built on. Without high-quality, accurate data, even the most advanced models will struggle to deliver value. Since data consistency and preparation underpins the accuracy of AI models, we place a lot of emphasis on real-time data consistency. 

SingleStore ensures synchronous updates immediately, meaning that as soon as data is written, it’s immediately available to read. (This contrasts with some vector databases that exhibit “eventual consistency,” where data might not be instantly available after writing.) Our Notebooks job service also facilitates data preparation, transformation, and validation, which are critical steps in ensuring data quality before it’s used for AI training or inference.

What’s more, new capabilities like database branching allow developers to create an almost instantaneous copy of a production database without duplicating the underlying data. When multiple developers can work together on a single copy of the data without damaging the integrity or quality of the main production database, AI experimentation becomes much faster and easier. 

Seamless data ingestion and integration is another thing that helps ensure data reliability, as the less you move your data around, the more accurate it will be. SingleStore Flow makes it easier to move data from various sources like Snowflake, PostgreSQL, SQL Server, Oracle, and MySQL into SingleStore, reducing the chance of data corruption or loss during transfer, contributing to the data’s overall reliability when it lands in SingleStore.

Accurate and effective AI requires a performant, consistent, and developer-friendly environment that allows for reliable data handling, immediate data availability, and safe data manipulation. That’s precisely what SingleStore is delivering.  

Q4. Let’s discuss in some details these new performance features: multi-value index for JSON, automatic query re-optimization and cross-workspace-group database branching and attach. What are these, and what are they useful for?   

Multi-Value Index for JSON optimizes query performance on JSON data by indexing arrays and nested values—ideal for logs, user profiles, and other semi-structured data.

Automatic Query Re-optimization dynamically improves query execution plans as data or workloads change, ensuring consistent high performance without manual tuning.

Cross-Workspace-Group Database Branching & Attach enables fast, space-efficient database branches across workspaces. Developers can work independently without impacting production, boosting productivity and enabling safer testing and data exploration.

Together, these features support faster development, better performance at scale, and smoother workflows for AI innovation.

Q5. Is it possible to see an overview of performance enhancements? 

All of the performance enhancements are showcased in this recording, which includes demos. You can watch it as a one-hour whole or zoom in on the individual features and enhancements via our YouTube playlist. 

Q6.  Please tell us what is new with SingleStore’s data migration and change data capture (CDC) solutions? 

We’re always focused on making data movement more and more seamless and ramping up real-time consistency.

Perhaps the biggest news coming out of our recent announcement is the integration of SingleStore Flow into Helios. Flow makes it easier to ingest data from sources like Snowflake, PostgreSQL, Oracle, and MySQL–supporting real-time use cases with fast pipelines and in-memory ingestion.

Easy data loading with Stages means users can upload files (e.g., CSVs) via the UI, which auto-generates setup scripts—simplifying initial data migration.

Improved integration with Apache Iceberg adds a high-speed layer and smoother data exchange with Iceberg external tables for better data lakehouse support.

And real-time consistency means that data, including vectors, is available to read immediately after it’s written—critical for real-time CDC and AI apps.

Together, these features streamline data flow, speed up development, and support real-time analytics and AI.

Q7. What are the new opportunities for building intelligent applications on data lake houses by using the bi-directional integration of SingleStore and Apache Iceberg?  

The bi-directional integration of SingleStore and Apache Iceberg, which we initially announced last summer, has received further enhancements. The latest improvements include adding a speed layer on Iceberg and improving the data exchange with Iceberg external tables.

The speed layer contributes to faster performance when working with Iceberg data and supports copy on write (COW) and merge on read (MER) semantics, while the faster data exchange streamlines the process of moving and accessing data between SingleStore and Apache Iceberg-based data lakes.

Collectively, these capabilities facilitate the development of intelligent applications by providing more efficient data handling and access within a data lakehouse architecture.

Q8. What is SingleStore’s Aura Container Service and what is it useful for?  

SingleStore’s Aura Container Service is a serverless compute platform built for running AI and machine learning workloads, with or without GPU support. It runs containerized apps and supports custom code execution, including SQL and Python, often through a notebook interface.

Aura can host cloud functions (lambda-style), build AI components like DataAPIs and intelligent agents, and provide inference APIs for embedding models. It supports generative AI use cases with GPU and memory-optimized options, and includes job scheduling via notebooks. It also enables hosting of third-party apps and large language models.

Overall, Aura simplifies and accelerates AI application development with a scalable, efficient environment.

Q9. Technology infrastructure to support AI goes beyond the AI tools themselves. What is your take on this?

You are absolutely correct. Effective AI deployment and intelligent application development require a robust underlying technology infrastructure that extends far beyond just the AI tools or models themselves. At SingleStore, we believe good AI starts with the data foundation, so we have a comprehensive strategy to perfect this foundational layer.

Our unified data foundation can transact, analyze and contextualize data in real time and handle a wide variety of data formats (including structured data, JSON, geospatial, time series, key-value pairs, and vectors), all within a single platform. Our multi-model capability means you don’t need separate databases for different data types, simplifying the infrastructure. The ability to handle the entire data lifecycle in one go is crucial for feeding and supporting AI models.

Our ultra-fast ingest, scalability, immediate synchronous updates and universal storage offer performance at scale for AI workloads. Our combination of Vector Search and FullText Search allow us to do hybrid search for AI in real-time and at massive scale, minimizing hallucination.

For mission-critical applications, we offer resiliency features like availability groups (data always on two leaves) and cross-availability zone deployment in premium options to ensure high availability even during power outages or entire cloud availability zone failures. This fundamental infrastructure ensures that AI applications remain operational.

In essence, SingleStore’s offerings address the complete AI lifecycle: from ingesting vast amounts of data, handling it efficiently in real-time for both transactions and analytics, providing specialized capabilities like vector search, and offering a robust, flexible, and developer-friendly environment for building and deploying intelligent applications. This holistic approach indeed goes beyond merely providing AI tools, focusing on the underlying infrastructure that enables their effective use at enterprise scale. When your foundation is rock-solid, anything you build on top of it will benefit.

Qx. Anything else you wish to add?

This isn’t our only product announcement for the year. We’ll have even bigger news at SingleStore Now 2025, to be held October 1 at Nasdaq Market Site in New York City. You can sign up here. Stay tuned to hear some mega AI announcements coming out of that event. 

……………………………….

Nadeem Asghar is Chief Technology Officer of SingleStore. 

He is a hands-on technology leader with over 25 years of extensive experience scaling early-stage startups to $1B+ revenue from the ground up. Prior to joining SingleStore, Nadeem held key roles in product, pre-sales, technology and strategy at Cloudera, Hortonworks, Morgan Stanley, Incentify, Capgemini and Lucent Technologies. Nadeem holds a Master’s degree in Computer Science from NJIT.

Sponsored by SingleStore.