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Snowflake cost cutting

· 4 min read
Aytan Jalilova

In the ever-evolving landscape of data analytics and cloud computing, cost optimization has become a paramount concern for businesses, both large and small. One recent example that has garnered attention is Instacart's journey to reduce its Snowflake bill, shedding light on the importance of managing data architecture costs efficiently in these economic times.

Instacart's Snowflake Bill: A Case Study​

According to recent S-1 filings, Instacart's expenditure on Snowflake, a cloud-based data warehousing solution, was a staggering $13 million in 2020, which escalated to $28 million in 2021, and further ballooned to $51 million in 2022. However, in a surprising turn of events, their projected Snowflake bill for 2023 is expected to decrease to $15 million. While the specifics of their cost-cutting strategies have not been publicly disclosed, it's safe to assume that Instacart has implemented substantial changes behind the scenes.

How to cut your Snowflake bills?​

Speculating on the strategies that Instacart might have employed to optimize their costs, we can consider several possibilities:

  1. Limiting Access: One approach could involve limiting employee access to Snowflake's warehouse units. By restricting access to only those who require it for their specific tasks, Instacart could reduce the demand for resources.
  2. Caching Layers: Another cost-saving measure could be the implementation of caching layers for reporting purposes. This would reduce the need for real-time querying of data in Snowflake, as cached results can be served faster and more cost-effectively.
  3. Resource Management: Snowflake's costs can be heavily influenced by resource management. Instacart might have fine-tuned their resource allocation to ensure optimal performance while minimizing expenses.
  4. Alternative Solutions: Instacart might have explored alternative solutions for specific use cases. For instance, Snowflake might not be the most cost-effective option for real-time analytics, leading them to adopt more suitable and affordable tools.

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Snowflake cost cutting solution - IOMETE​

One noteworthy data warehousing solutions like Snowflake is IOMETE. Here are some key aspects of that make it an attractive option:

  1. Query Federation: IOMETE offers computing data directly from storage itself without migrating.
  2. Scalability: IOMETE enables separate scaling of data storage and concurrency, providing flexibility and cost efficiency.
  3. Fast Queries: The system is known for its high-speed query processing, ensuring quick access to insights.
  4. AI Integration: IOMETE recently introduced an text to AI solution for built-in SQL editor
  5. Data policy: IOMETE allows you to create limitations on access for user based on rows, columns, provided tag name and masking.
  6. Jupyter Notebook: to explore and analyze data stored in IOMETE's data lake directly from your local environment.

How much you can save Snowflake costs?​

In general, we can say that Snowflake and IOMETE together can reduce your cloud bills 50%. Snowflake savings calculator can help you to get exact numbers on your cost optimization journey.

The Need for Specialized Solutions​

While some platforms attempt to offer hybrid solutions that combine real-time analytics with traditional data warehousing, there is a growing recognition that dedicated solutions are often more effective. These specialized tools prioritize solving the unique combination of challenges presented by real-time analytics.

Key Takeaways​

Instacart's journey to cut costs with Snowflake is a compelling example of the ongoing need for cost optimization in the world of data analytics. While the specifics of their strategies remain undisclosed, their experience highlights the importance of managing costs effectively in these economically challenging times.

The realm of real-time analytics presents its own set of challenges, and businesses are increasingly turning to specialized solutions like IOMETE and others to address these issues. As technology continues to evolve, finding the right balance between cost, performance, and functionality will remain a top priority for organizations striving to stay competitive in the data-driven era.