In the dark ages of 2015, Snowflake users with unpredictable workloads had to either manually manage traffic volumes or waste money overprovisioning their warehouses to be able to absorb spikes. Snowflake solved this by launching multicluster warehouses, enabling users to point all their queries at one logical warehouse and automatically scaling the number of clusters to match load.
This does a great job of reducing operational complexity, but there’s a catch: you need to upgrade to Enterprise and pay 50% more on your entire Snowflake bill to take advantage of this feature.
We’ve built a standalone implementation of multicluster scheduling that doesn’t cost you an arm and a leg. Our system not only provides autoscaling to Snowflake customers on the Standard plan - it also uses AI for smarter autoscaling, saving an average of 30% on credit costs with no impact to latency.
Stay tuned for future blog posts where we go into how Espresso AI’s scheduler does a better job of scheduling queries.
In the meantime, if you use multicluster, we can save you 30-50% on your bill. Contact us at multicluster@espresso.ai and stop paying Snowflake for multicluster!
Frequently Asked Questions
Snowflake multicluster warehouses are a compute feature that automatically scales the number of clusters behind a single logical warehouse to handle fluctuating query loads. This eliminates the need for manual capacity management by spinning up additional clusters during demand spikes and reducing them during low-usage periods, ensuring consistent performance.
Snowflake Enterprise Edition includes multicluster capabilities as part of its advanced workload management features. Unlike Snowflake Standard Edition, Enterprise allows users to configure warehouses with multiple clusters for automatic scaling. However, this comes at a cost premium of approximately 50% on the total Snowflake bill.
Snowflake Standard Edition does not natively support multicluster warehouses. Users on the Standard plan typically scale workloads manually by resizing warehouses, which can lead to either overprovisioning or performance bottlenecks during demand spikes. Espresso AI’s scheduler offers an alternative autoscaling solution for Standard Edition users.
Autoscaling in Snowflake refers to the automatic adjustment of compute resources—specifically, the number of clusters—based on real-time query demand. With autoscaling, Snowflake can add clusters during peak load periods and scale them down during idle times, optimizing performance without manual intervention.
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