SQream Platform
GPU Powered Data & Analytics Acceleration
Enterprise (Private Deployment) SQL on GPU for Large & Complex Queries
Public Cloud (GCP, AWS) GPU Powered Data Lakehouse
No Code Data Solution for Small & Medium Business
Scale your ML and AI with Production-Sized Models
By SQream
Most data leaders don’t realize how much they’re compromising.
They assume delayed reports, sampled data, and bloated infrastructure are just the cost of doing business at scale. That’s just how it goes, right?
But what if the real bottleneck isn’t your data?
What if it’s your database?
This isn’t just a semantic twist. It’s a mindset shift — one that changes how you look at every delay, every cost spike, every analysis that never got run.
Over the past decade, enterprises raced to modernize their data stacks. “Cloud-first” became the north star. Distributed systems, data lakes, and MPP engines became table stakes.
It made sense at the time. More data meant more complexity. So teams bought tools to manage it: ETL platforms, orchestrators, query engines, warehouses, BI layers. Each tool did one thing. Collectively, they became a sprawling ecosystem held together with duct tape and hope.
On paper, this meant unlimited scale. In reality? Limited performance.
The truth: most of these systems still run on CPU-bound execution engines — built in a world of gigabytes, now choking on petabytes.
When these stacks hit limits, the knee-jerk response is always the same:
What you rarely get more of? Insight.
Here’s where it starts to hurt.
You’re making trade-offs daily. And most teams don’t even notice anymore.
And the downstream effect? Risk models built on partial data. Dashboards running on stale snapshots. AI teams slowed by prep time that eats up their iteration cycles.
The truth is: your platform isn’t just slow. It’s holding your business back from asking better questions.
Forward-thinking teams are rethinking the entire foundation.
They’re not layering on more compute. They’re not buying more cloud.
They’re flipping the execution model — by putting GPUs at the center of the pipeline.
And not just for deep learning models. For ingestion. For preparation. For joins. For real-time scoring. For massive SQL queries that would bring a CPU-bound stack to its knees.
That change doesn’t just improve one step. It accelerates everything.
A major African bank needed to modernize its regulatory reporting. Their compliance jobs were taking 18+ hours — and that was after years of tuning.
The stack was considered “mature.” It just wasn’t fast enough.
They didn’t rewrite applications. They didn’t migrate data lakes. They simply swapped the execution engine — from CPU to GPU.
With SQream, the same jobs dropped to under 4 hours. BI dashboards that took nearly two hours to refresh now updated in under 15 minutes.
Same data. Same tools. New speed.
That’s what happens when the bottleneck isn’t your workload — it’s your engine.
There’s a new way to think about this: the AI Factory.
Just like a modern manufacturing line automates, accelerates, and scales physical production, an AI Factory does the same for intelligence.
But most so-called AI factories are built on old conveyor belts: CPU-based engines, disconnected pipelines, costly handoffs.
SQream’s model replaces that with an integrated, GPU-native infrastructure — a data highway that feeds your ML, BI, and decision systems at full throttle.
No sampling. No delay. No rewrites. Just throughput.
This isn’t about building from scratch. It’s about replacing the one part of your stack that’s holding back everything else.
Ask yourself:
These are signs that it’s not the data that’s the problem. It’s how you’re handling it.
You already own the data. Your team already knows what to do with it.
Now give them the engine that makes it possible.
→ Book a demo and see SQream in action
