Snowflake Just Got Crunchy: The $250M Bet That Could Change the AI Data Game!

In a bold step to strengthen its AI-focused data platform ambitions, Snowflake (NASDAQ:SNOW) has announced the acquisition of Crunchy Data, a leading enterprise-grade Postgres provider, for approximately $250 million. This move comes just weeks after rival Databricks acquired Neon, another Postgres-based database startup, in a $1 billion deal. Snowflake, traditionally known for its analytics-driven data cloud, is now doubling down on transactional and unstructured data ingestion capabilities through its acquisitions of Crunchy Data and Datavolo. The acquisitions are part of a broader plan to expand Snowflake’s footprint across the full data lifecycle, from ingestion to AI application development. With the launch of “Snowflake Postgres” and increased focus on making unstructured data “AI-ready,” the company is positioning itself as a comprehensive, AI-centric data platform—directly challenging Databricks, OpenAI, and other tech giants chasing enterprise AI dominance. Let us analyze these recent acquisitions in detail and understand how they align with Snowflake’s evolving AI vision.

Expanding Into the Transactional Data Layer With Crunchy Data

Snowflake’s acquisition of Crunchy Data marks a significant strategic expansion beyond its traditional analytical workloads. By integrating Crunchy Data’s enterprise-grade PostgreSQL capabilities, Snowflake can now address the transactional data layer—one of the foundational layers needed for AI agent memory, session persistence, and preference storage. This move allows Snowflake to better support application developers and enterprises that are building interactive AI agents requiring real-time contextual memory and session-based data. With a mature and battle-tested Postgres product, Crunchy Data also brings over 100 employees and deep domain expertise into Snowflake, accelerating productization under the new “Snowflake Postgres” banner. This transactional capability complements Snowflake’s five-year effort on Unistore, which has faced engineering challenges due to the inherent complexity of combining OLTP (online transaction processing) and OLAP (online analytical processing). Crunchy Data provides Snowflake a proven, scalable Postgres engine to compete directly with Databricks’ recent Neon acquisition. While Databricks' approach is more developer-centric and open-source-first, Snowflake aims to win by integrating Postgres tightly within its broader enterprise-grade data cloud. This vertical integration—stretching from transactional data ingestion to analytics and AI—allows Snowflake to become a full-stack AI infrastructure player rather than just an analytics provider. It also signals a shift in Snowflake’s product philosophy, moving toward versatility, faster time-to-value, and extensibility for a new generation of enterprise AI workloads.

Strengthening Data Ingestion Capabilities With Datavolo

Another critical component of Snowflake’s evolving AI strategy is its acquisition of Datavolo, a company focused on data ingestion, particularly from unstructured and legacy systems. Acquired six months ago and now brought to public preview at record speed, Datavolo is at the core of “Openflow,” Snowflake’s new ingestion framework. The acquisition enables Snowflake to support upstream stages of the data lifecycle where raw data is born—IoT devices, legacy apps, and enterprise file shares. Openflow is designed to unify structured and unstructured data ingestion, providing users with an abstraction layer that dramatically simplifies how disparate data sources are brought into the Snowflake platform. This capability becomes especially important in the context of AI, where unstructured data—PDFs, emails, logs, and documents—is becoming just as valuable as structured rows and columns. AI agents often need to access a wide array of formats, and legacy ingestion pipelines have historically been brittle and siloed. With Datavolo’s technology, Snowflake users can now ingest and transform these data types in a more seamless and performant manner. This end-to-end data ingestion capability strengthens Snowflake’s competitive positioning against Databricks, which already offers tight Spark-based pipelines for ingestion and processing. Moreover, Snowflake's ingestion enhancements, when combined with its AI products like Cortex Search and semantic layer developments, improve the overall usability of both structured and unstructured data for AI model consumption. By eliminating ingestion bottlenecks, Snowflake is making its platform more attractive for enterprises looking to deploy AI agents that require large, diverse, and often messy data sets.

Enabling End-to-End Data Lifecycle Support For AI Workloads

Snowflake’s recent acquisitions are designed to solidify its presence across the entire data lifecycle, enabling it to transition from being a downstream analytics engine to an upstream-and-downstream AI infrastructure layer. The company's long-term vision involves participating at every step of this lifecycle: from the moment data is generated (via apps or devices), to ingestion and transformation (via Datavolo/Openflow), to storage and computation (via Snowflake Postgres and Unistore), and finally to analytics and AI model execution (via Cortex, Snowpark, and Snowflake Intelligence). The Crunchy Data and Datavolo acquisitions plug critical gaps in Snowflake’s ability to serve AI agents that require real-time memory and rich contextual awareness. Moreover, this vertically integrated strategy ensures that AI-ready data—defined as well-governed, semantically annotated, and readily consumable—is available for intelligent automation and decision-making. Snowflake's emphasis on metadata, semantic views, and governance also means that customers can build secure and compliant AI agents without the typical friction associated with deploying large language models on enterprise data. While Databricks continues to cater to data scientists and engineers with its MLflow and lakehouse ecosystem, Snowflake is differentiating by creating a more intuitive, secure, and business-friendly stack. By controlling the end-to-end pipeline, Snowflake not only reduces vendor sprawl but also shortens the time required to prototype and deploy AI agents. This platform-centric approach supports a broader customer base—including business users, data engineers, and IT administrators—who want usable and compliant AI solutions, not just tooling.

Building A Competitive Moat Through Simplicity, Governance & Native AI Integration

At the heart of Snowflake’s AI strategy is its belief in simplifying enterprise AI development while maintaining strong governance and security. The acquisitions of Crunchy Data and Datavolo directly support this philosophy. Crunchy Data enables first-party transactional capabilities so that AI agents can be stateful and context-aware, while Datavolo simplifies ingestion from non-traditional sources, allowing broader and faster data availability. Snowflake’s unified governance model—already a market leader—extends across these new layers, giving enterprises control over how data is used, shared, and audited. This approach contrasts with Databricks’ developer-centric flexibility, which often requires more tooling and hands-on integration. Additionally, Snowflake’s new offerings like Cortex Agents, semantic layer integration, and native support for AI SQL-based analysis position it to deliver faster business outcomes with fewer dependencies. For enterprises looking to avoid copying data across platforms or building complex ETL pipelines, this matters. By embedding AI into the core fabric of the Snowflake Data Cloud—rather than treating it as an add-on—Snowflake creates a compelling value proposition for customers looking to deploy AI in real-world use cases such as personalized recommendations, intelligent reporting, and operational automation. The ability to capture both structured and unstructured data, provide governed access, and support real-time interactivity through AI agents gives Snowflake a competitive moat that is hard to replicate quickly. However, this also increases complexity for Snowflake’s internal engineering teams and go-to-market organization, which now must support a broader suite of products and use cases without losing focus or efficiency.

Final Thoughts

Source: Yahoo Finance

As we can see in the above chart, Snowflake’s stock has had a solid performance since the revival of the market after mid-April and it has zoomed close to its 52-week high. The company’s LTM EV/ Revenue valuation of 18.80x appears massive especially when we consider the fact that it is loss-making at EBITDA level. While such a high valuation does not make Snowflake a buying opportunity for investors, it is definitely a case study worth exploring. Its acquisitions of Crunchy Data and Datavolo reflect a deliberate strategy to expand beyond analytics into transactional processing, data ingestion, and enterprise AI enablement. These moves allow Snowflake to participate in a broader range of workloads and compete more directly with Databricks, which has historically led in data science and machine learning. As the competition intensifies in enterprise AI infrastructure, Snowflake's ability to deliver simplicity, governance, and end-to-end capabilities will be a key determinant of its long-term market position.