Lakehouse//RT: Scale real-time analytics, not your bill

AI agents can generate hundreds of queries per second, far exceeding human analysts' capacity, which strains traditional data warehouses designed for human-scale workloads. Most warehouses scale compute linearly with query volume, leading to disproportionate cost spikes as demand grows. Lakehouse//RT is engineered to serve real-time workloads directly on the lakehouse while maintaining efficiency, avoiding the typical cost escalation tied to query growth.

Real-time workloads often require overprovisioning to handle peak demand, resulting in idle compute and wasted spend during off-peak periods. Lakehouse//RT scales efficiency rather than cost, enabling the same infrastructure to handle significantly more queries without a proportional increase in expenditure. Its architecture supports sub-100 millisecond latency at 12,000 queries per second, optimizing resource utilization for spiky, high-concurrency scenarios.

Traditional warehouses struggle with concurrency limits, forcing organizations to deploy multiple clusters to maintain low latency, even for small queries. Lakehouse//RT runs far more queries concurrently on a single warehouse, reducing the need for additional clusters and associated costs. Customer migrations demonstrate substantial improvements, such as Aurasell reducing costs by 10x while increasing query throughput and speed.

Lakehouse//RT eliminates the need for separate real-time serving layers like Apache Druid or Pinot, which require duplicated data and always-on infrastructure. Migrations to Lakehouse//RT have shown dramatic cost reductions, such as VisionHeight cutting costs by over 90% and nOps reducing infrastructure spend by more than 6x while removing duplicated data and sync pipelines.