The engine achieves sub-100 millisecond latency at 12,000 queries per second by speeding up query planning, routing, and delivery to free hardware resources faster. In an operational deployment, customer Aurasell consolidated several legacy clusters into a single warehouse that ran 250 queries per second, boosted query speeds by up to five times, and cut overall expenses by tenfold. Additionally, the platform eliminates the wasteful overprovisioning typical of multi-cluster scaling by adjusting resources incrementally.
Key points
AI agents generate continuous query volumes that make conventional data warehouse pricing…: AI agents generate continuous query volumes that make conventional data warehouse pricing models cost-prohibitive.
Faster query lifecycles free up compute capacity immediately to lower billable runtime ac…: Faster query lifecycles free up compute capacity immediately to lower billable runtime across millions of daily tasks.
Single-node scaling allows organizations to absorb sudden 10% demand spikes without doubl…: Single-node scaling allows organizations to absorb sudden 10% demand spikes without doubling their existing infrastructure footprint.
Sources checked by our analysis pipeline: 1
AI agents can multiply your query volume. Lakehouse//RT keeps your bill from multiplying with it.
by JooHo Yeo and Kaitlin Baumgardner
AI agents are pushing the limits of data warehouses. A human analyst might run a few dozen queries in an afternoon. An agent can run hundreds per second, around the clock. And with growing demand for use cases like customer-facing dashboards that refresh in milliseconds and applications with embedded analytics, real-time workloads are quickly becoming a standard part of how businesses use data.
Most data warehouses were designed for human-scale query volume, not the sustained concurrency agents generate. And even when a warehouse can deliver low latency, the bill grows as fast as demand: 10x the queries could easily mean 10x the cost.
We built Lakehouse//RT not only to serve real-time workloads directly on the lakehouse, but to do so without growing your costs at the same rate as your queries.
Most data warehouses scale capacity in step with demand: more queries, more compute, more cost. That works well for many analytics workloads, where query volume tracks the number…
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