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Top 4 Challenges to Scaling Snowflake for AI

Organizations are transforming their industries through the power of data analytics and AI. A recent McKinsey survey finds that 75% expect generative AI (GenAI) to “cause significant or disruptive change in the nature of their industry’s […]

  • 3 min read

Organizations are transforming their industries through the power of data analytics and AI. A recent McKinsey survey finds that 75% expect generative AI (GenAI) to “cause significant or disruptive change in the nature of their industry’s competition in the next three years.” AI enables businesses to launch innovative new products, gain insights into their business, and boost profitability through technologies that help them outperform competitors. Organizations that don’t leverage data and AI risk falling behind.

Despite all the opportunities with data and AI, many find ROI with advanced technologies like IoT, AI, and predictive analytics elusive. For example, companies find it difficult to get accurate and granular reporting on compute and storage for cloud data and analytics workloads. In speaking with enterprise customers, we hear several recurring barriers they face to achieve their desired ROI on the data cloud.

Cloud data spend is challenging to forecast

About 80% of 157 data management professionals express difficulty predicting data-related cloud costs. Data cloud spend can be difficult to reliably predict. Sudden spikes in data volumes, new analytics use cases, and new data products require additional cloud resources. In addition, cloud service providers can unexpectedly increase prices. Soaring prices and usage fluctuations can disrupt financial operations. Organizations frequently lack visibility into cloud data spending to effectively manage their data analytics and AI budgets.

  • Workload fluctuations: Snowflake data processing and storage costs are driven by the amount of compute and storage resources used. As data cloud usage increases for new applications, dashboards, and uses, it becomes challenging to accurately estimate the required data processing and storage costs. This unpredictability can result in budget overruns that affect 60% of infrastructure and operations (I&O) leaders.
  • Unanticipated expenses: Spikes in streaming data volumes, large amounts of unstructured and semi-structured data, and shared warehouse consumption can quickly exceed cloud data budgets. These unforeseen usage peaks can catch organizations off guard, leading to unexpected data cloud costs.
  • Limited visibility: Accurately allocating costs across the company requires detailed visibility into the data cloud bill. Without query-level or user-level reporting, it becomes impossible to accurately attribute costs to various teams and departments. The result is confusion, friction and finger-pointing between groups as leaders blame high chargeback costs on reporting discrepancies.

Organizations can establish spending guardrails and implement controls by implementing a FinOps approach and leveraging granular data to implement smart and effective controls over their data cloud spend, set up budgets, and utilize alerts to avoid data cloud cost overruns.

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Data cloud workloads constrained by budget and staff limits

In 2024, IT organizations expect to shift their focus towards controlling costs, improving efficiency, and increasing automation. Cloud service provider price increases and growing usage add to existing economic pressures, while talent remains scarce and expensive. These cost and bandwidth factors are limiting the number of new data cloud workloads that can be launched.

“Data analytics, engineering & storage” are among the top 3 biggest skill gaps and 54% of data teams say the talent shortage and time required to upskill employees are the biggest challenges to adoption of their AI strategy.

Global demand for AI and machine learning professionals is expected to increase by 40% over the next five years. Approximately one million new jobs will be created as companies look to leverage data and AI for a wide variety of use casesfrom automation and risk analysis, to security and supply chain forecasting.

AI adoption and data volume demand

Since ChatGPT broke usage records, generative AI is driving increased data cloud demand and usage. Data teams are struggling to maintain productivity as AI projects scale “due to increasing complexity, inefficient collaboration, and lack of standardized processes and tools” (McKinsey).

Data is foundational for AI and much of it is unstructured, yet IDC found most unstructured data is not leveraged by organizations. A lack of production-ready data pipelines for diverse data sources was the second-most-cited reason (31%) for AI project failure.

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Data pipeline failures slow innovation

Data pipelines are becoming more complex, increasing the time required for root cause analysis (RCA) for breaks and delays. Data teams struggle most with data processing speed. Time is a critical factor that pulls skilled and valuable talent into unproductive firefighting. The more time they spend dealing with pipeline issues or failures, the greater the impact on productivity and delivery of new innovation.

Automated data pipeline monitoring and testing is essential for data cloud applications, since teams rapidly iterate and adapt to changing end-user needs and product requirements. Failed queries and data pipelines create data issues for downstream users and workloads such as analytics, BI dashboards, and AI/ML model training. These delays and failures can have a ripple effect that impacts end user decision-making and AI models that rely on accurate, timely content.


Unravel for Snowflake combines the power of AI and automation to help you overcome these challenges. With Unravel, Snowflake users get improved visibility to allocate costs for showback/chargeback, AI-driven recommendations to boost query efficiency, and real-time spend reporting and alerts to accurately predict costs. Unravel for Snowflake helps you optimize your workloads and get more value from your data cloud investments.

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