Snowflake Cortex Search – Adoption Trends and Co-Usage Data

Snowflake Cortex Search – Adoption Trends and Co-Usage Data

Snowflake Cortex Search adoption is rising and reached 10% in September 2025, which is up 2 points since March. Snowflake’s AI tooling appears to be spreading beyond the data warehouse, with adoption clustering across the stack. Organizations using Cortex Search are 252% more likely to also use Snowflake Cortex and 180% more likely to use Snowflake ML. For financial services teams, this can speed AI app delivery on governed data. Cost and scaling controls remain the main watch-outs.

 

Key Takeaways

  • AI and data consolidation fuel growth. Enterprises expanding with Snowflake cite AI/ML development, Cortex adoption, and data platform modernization as central drivers. The platform is increasingly tied to long-term data lake and GenAI strategies.
  • Expansion is broad and cross-functional. Snowflake is gaining traction across multiple divisions and industries, with several respondents citing the replacement of Oracle, Teradata, or Palantir as part of company-wide standardization.
  • Databricks and cloud-native rivals pulling share. Organizations, particularly in Financials and Healthcare, are moving workloads to Databricks, GCP, and Azure, favoring tighter cloud-native integration, architectural fit for AI, and better cost control.
  • Complexity and cost are prompting reevaluation. Some customers cite Snowflake’s high cost, integration friction, and architectural rigidity as reasons for exiting or platform consolidation in favor of native cloud services.
  • Bifurcation intensifies as AI leaders double down. Snowflake remains critical to many enterprise data strategies, but long-term growth will hinge on delivering AI-native value at scale while defending against defections to cloud-native stacks.

This commentary reflects a dynamic landscape for Snowflake, with companies making strategic decisions based on their evolving data and AI needs. While many are increasing investment to leverage Snowflake's expanding capabilities in data modernization and AI/ML, others are pivoting to alternative platforms that better align with their specific cloud or AI strategies. The consistent thread across respondents is the emphasis on long-term strategic planning, whether that involves deepening Snowflake integration or transitioning to competing solutions. This bifurcation underscores Snowflake's position at a critical juncture, where its ability to demonstrate value and innovation in AI and data management will be crucial for maintaining and expanding its market position.

 

Top Quotes

  • "We envision Snowflake to be our primary cloud warehouse for reporting and analytics." - Director, North America, IT/TelCo, Fortune 100 company (Increasing)

  • "Snowflake aligns with our long-term IT strategy by providing a scalable, cloud-agnostic data foundation that consolidates disparate data sources into a single platform. It is a solution that has enabled us to fasten our analytics program at a reasonable cost as we are working towards modernizing our data architecture and prioritizing data-driven decision making." - Senior Advisor / Consultant, EMEA, Services/Consulting, Small company (Increasing)

  • "Snowflake fits well into both our broader tech and IT strategies with all the AI/ML tools and services as well as the data lake and data platform enhancements." - Senior Director, North America, IT/TelCo, Fortune 100 company (Increasing)

  • "Long-term strategic goals [are] to leverage GCP leadership (and their technical stack) in AI and AI native services." - Department Head, North America, Financials/Insurance, Large company (Decreasing)

  • "Developing AI agents with Snowflake that can perform multi-step tasks, reason across diverse data sources." - VP, APAC, Financials/Insurance, Global 1000 company (Increasing)

  • "We are consolidating platforms and moving away from Snowflake over the next year. We will be ramping down and replacing [it] next year." - Department Head, North America, IT/TelCo, Midsize company (Decreasing)

  • "As continued growth happens, like all businesses, you want to be nimble and efficient." - VP, North America, Retail/Consumer, Fortune 100 company (Increasing)

 

Additional End-User Commentary

What are the key drivers behind this change in spending?

Increasers of Snowflake

  • Increased data consumption and new workloads
  • Data platform modernization and legacy system replacement
  • Expansion to new divisions and acquisitions
  • AI/ML innovations and Cortex agent adoption
  • Increased usage across teams and migration from on-premises systems
  • Implementation of generative AI use cases

Decreasers of Snowflake

  • Migration to alternative platforms (e.g., Databricks, Azure, Google Cloud)
  • Vendor consolidation efforts
  • Cost reduction and ROI improvement
  • Strategic shift to in-house solutions
  • Complexity and ease-of-use concerns

 

Is this change driven by more short-term needs or long-term strategic goals?

The majority of respondents, both increasers and decreasers, cite long-term strategic goals as the primary driver for their spending decisions on Snowflake. Increasers often mention aligning Snowflake with their future data and AI strategies, while decreasers are looking to consolidate vendors or shift to platforms that better fit their evolving needs. Some companies also note short-term requirements influencing their decisions, particularly when it comes to immediate project needs or cost savings.

 

Is this change in spend coming at the expense or benefit of another vendor or product? If so, which one(s) and why?

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