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Unlocking Efficiency: A Guide to Snowflake Cost Optimization

By February 22, 2024No Comments

In the intricate world of data management, Snowflake stands as a beacon of innovation. As businesses embrace this powerful platform, understanding and optimizing costs become paramount. In this blog post, we’ll delve into the art of Snowflake cost optimization.

Understanding Snowflake Cost:

Snowflake costs are composed of several key elements, each contributing to the overall expenses of using the platform.

Snowflake costing without optimization
Compute Cost

Compute costs are tied to the computational power used for processing queries and operations within Snowflake. Snowflake encompasses three distinct types of compute resources, each contributing to credit consumption:

  • Virtual Warehouse Compute: Virtual warehouses are user-managed compute resources responsible for tasks like loading data, executing queries, and performing various DML operations.
    Billing for virtual warehouses follows a per-second model, with a minimum of 60 seconds each time the warehouse is active. Warehouses are billed exclusively for the credits they consume during active work periods.
  • Serverless Compute: Certain Snowflake features, including Search Optimization and Snowpipe, utilize Snowflake-managed compute resources instead of virtual warehouses.
    To optimize costs, these serverless compute resources undergo automatic resizing and scaling based on the requirements of each workload.
  • Cloud Services Compute: The cloud services layer of Snowflake’s architecture incurs credits for background tasks like authentication, metadata management, and access control. Charges for cloud services usage apply only if the daily consumption of these resources exceeds 10% of the daily warehouse usage.
Snowflake costing without optimization
Storage Cost

The cost of storing data is influenced by how much space data occupies within Snowflake. The monthly cost for storing data in Snowflake is based on a flat rate per terabyte (TB). The current rate varies depending on your type of account (Capacity or On Demand) and region (US or EU). Storage is calculated monthly based on the average number of on-disk bytes stored each day in your Snowflake account.

Data Transfer Cost

Costs associated with data movement within and outside of Snowflake. Snowflake does not charge data ingress (inbound) fees to bring data into your account but does charge for data egress (outbound). Snowflake charges a per-byte fee when you transfer data from a Snowflake account into a different region on the same cloud platform or into a completely different cloud platform. This per-byte fee for data egress depends on the region where your Snowflake account is hosted.

When Snowflake features are not used properly the cost of operating can increase drastically over time. Let’s discuss a few strategies to optimize the cost.

Cost Optimization Strategies:

As we already know the Snowflake costs are composed of several key elements, each contributing to the overall expenses of using the platform; we will see if certain aspects of the components can be tweaked to reduce costs:

Compute Cost

To minimize compute costs, consider the following strategies:

  • Right-Sized Warehouses: Experiment with smaller warehouse sizes, such as small or extra-small, to assess query performance.
    For instance, execute a query with an extra-small warehouse and monitor its performance before making warehouse size adjustments based on actual workload requirements.
  • Query Optimization: Optimize queries by implementing appropriate filter conditions to limit data processing.
    Consider a scenario where a query processing large datasets can be enhanced by incorporating filter conditions to focus on specific data subsets, thereby reducing overall compute costs.
Storage Cost

Optimizing storage costs involves making strategic choices in table types and data handling:

Choose the appropriate table type to significantly reduce storage costs.
For example, consider a scenario where selecting the right table type for a dataset with infrequently accessed data can lead to substantial cost savings in terms of storage.

For instance, consider a scenario where we are loading a complex table, and to facilitate low-latency data transfer from source to destination, we create a staging table of 200 GB. If the jobs run 20 times a day, the total storage for this 200 GB table would be calculated as follows:

Activity Total Data size
Active 200 GB
Time Travel (One Day Storage) 20 X 200 GB = 4 TB
Fail Safe (7 days storage) 7 X 4 TB = 28 TB
Total Storage 4 TB + 28 TB = 32 TB

However, by making this table transient with zero Time Travel retention, the storage would be drastically reduced:

Activity Total Data size
Active 200 GB
Time Travel (One Day Storage) 1 X 200 GB = 200 GB
Fail Safe (7 days storage) 7 X 200 GB = 1.4 TB
Total Storage 1.4 TB + 200 GB = < 2 TB

This example illustrates how opting for transient tables without Time Travel retention significantly reduces the total storage requirements, resulting in substantial cost savings, especially in scenarios where fail-safe features are not critical.

Data Transfer Cost

To reduce data transfer costs, focus on minimizing unnecessary data movement:

  • Regional Data Transfer: Minimize data transfer between regions within the same cloud platform.
    For instance, in a data replication scenario, optimize workflows to reduce the need for inter-region data transfers, thus lowering associated costs.
  • Evaluate Data Replication Necessity: Assess the necessity of data replication and halt unnecessary replication processes.
    Consider a scenario where halting data replication for non-essential datasets results in a notable reduction in data transfer costs between different regions or platforms.
    Implementing these strategies across compute, storage, and data transfer dimensions empowers organizations to not only optimize costs but also enhance overall efficiency in their Snowflake usage. Regularly assess and adjust these strategies based on evolving requirements to maintain a cost-effective and high-performance data environment.


By integrating these methodologies to optimize virtual warehouses, storage, and data transfer, organizations gain the capability to efficiently navigate and manage Snowflake costs. A well-rounded and proactive strategy, coupled with routine evaluations, is essential for securing enduring cost savings while capitalizing on the extensive capabilities offered by Snowflake.

In practical application, Modern Data Hub at Astraa has successfully employed these cost-saving methodologies for our clients, resulting in a substantial reduction in overall costs.

References :
Snowflake Documentation:

Anupam Kushwah

Technical Architect, Data & Analytics at Astraa.

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