Thank you for joining our webinar!
Please find here the Q&A of our session:
What’s driving the resurgence of interest in on-prem data platforms?
Several factors are at play. First, data sovereignty and compliance are making organisations rethink how much they want to move to the public cloud, especially in financial services and government. Second, cost predictability is a growing concern many cloud-first initiatives have run into unexpected egress and compute costs. Third, latency and performance requirements for real-time decisioning or AI workloads are often better served on-prem or in hybrid setups. So it’s not a rejection of cloud, but rather a move to right-size architectures.
Which industries benefit most from hybrid data architectures?
Industries with high regulatory oversight, data locality needs, or intense analytical workloads tend to benefit most. That includes banking, healthcare, telco, and parts of the public sector. Hybrid allows them to leverage cloud innovation while keeping sensitive data on-prem, or near to the source, especially where latency matters, think fraud detection, industrial IoT, or real-time analytics.
What are the biggest misconceptions about on-prem vs cloud?
A big one is that cloud automatically equals agility, and on-prem equals legacy. That’s simply not true anymore. Modern on-prem platforms, especially those designed for hybrid can deliver incredible speed, scale, and flexibility, sometimes outperforming cloud-native platforms on key metrics like query response times, concurrency, or TCO. Another misconception is that cloud migration is ‘all or nothing’. In reality, the future is hybrid, driven by business use case needs, not infrastructure ideology.
What makes Exasol suited to AI/ML workloads in hybrid or on-prem settings?
Exasol’s architecture is built for extreme performance, in-memory, massively parallel processing, and the ability to work across on-prem, hybrid, and even disconnected environments. That means data scientists can run advanced models on large datasets with minimal prep time. Our native Python, R, and Java support, plus integration with tools like Jupyter and Apache Kafka, make it easy to operationalize models wherever the data lives not just in the cloud.
How does Exasol support the ML lifecycle from prototype to production?
We help reduce the friction between data scientists and data engineers. With embedded analytics, teams can train and score models directly inside Exasol, reducing data movement. And with our performance tuning and integration APIs, we make it easy to plug Exasol into pipelines or apps where models are being used, whether it’s batch inference, real-time scoring, or dashboarding. We give teams speed, scale, and control throughout the lifecycle.
How does Exasol bridge the performance gap between local data and cloud-based tooling?
By acting as a high-performance data processing layer close to the source. Many AI/ML teams store data on-prem due to compliance or latency, but want to use cloud tools. Exasol lets them process and prepare that data at lightning speed, then only push out aggregated or relevant slices to cloud-based tools like SageMaker, Databricks, or Azure ML. This minimizes data movement and maximizes efficiency, making hybrid AI architectures practical and powerful.
Many organisations feel their current data warehouse is ‘good enough’, what would you say to those who are resistant to change or modernisation?
It’s a valid sentiment, especially when systems are stable and teams are under pressure to deliver without disruption. But ‘good enough’ often means teams are spending too much time waiting on queries, struggling with concurrency, or working around bottlenecks that are limiting what they can actually achieve.
The real question isn’t whether the current system works, it’s whether it empowers innovation. Can your analysts run complex models quickly? Can you support new AI initiatives without offloading to another platform? Can you scale without adding cost or technical debt?
Exasol often complements existing environments first, helping teams move faster, explore more, and deliver more value without forcing a rip-and-replace. That’s when people realise: the current setup wasn’t actually good enough it was just all they knew.
Doesn’t data volume based pricing just make analysts afraid of asking for more data?
That’s a really important point and yes, data volume-based pricing can unintentionally create fear or hesitation. When every gigabyte comes with a price tag, analysts may second-guess asking bigger or better questions. That’s the exact opposite of what a data-driven culture should encourage.
At Exasol, our philosophy is different. We focus on performance and value delivered not how much data you’re storing. Our pricing model is designed to remove those psychological barriers and support freedom to explore, iterate, and model at speed.
In the age of AI and complex analytics, the most valuable insights often come from bringing more data into the picture, not less. So, our goal is to make accessing and working with that data fast, efficient, and cost-predictable without discouraging curiosity or innovation.
