While Databricks was celebrating their $1 billion Neon acquisition in May, Snowflake executives were facing an uncomfortable reality: their AI Data Cloud suddenly looked incomplete without support for an industry standard operational database. Snowflake has been developing Hybrid tables (under the Unistore workload capability) for a couple of years now, but this feature doesn’t address the broader transactional database workload needs that AI agents will require. Three weeks later, they wrote a $250 million check for Crunchy Data, instantly transforming from PostgreSQL skeptics to enterprise Postgres evangelists.
This wasn’t just competitive positioning, it’s Snowflake’s strategic pivot toward what we call the Augmented Enterprise, where Agentic and AI-infused smart applications require seamless integration between analytical and operational data systems. The market dynamics underscore the urgency: 60% customer overlap between Snowflake and Databricks signals massive untapped revenue potential¹, while AI/ML services are projected to grow 30%+ annually through 2028². Snowflake’s internal UniStore development clearly couldn’t match the pace needed for this converged transactional-analytical future.
The Market Reality: Analytics Platforms Face an Existential Convergence Moment
The data platform battlefield has fundamentally shifted from traditional Foundation-layer competition to enabling what we term the Augmented Enterprise. Where Snowflake and Databricks once competed primarily on analytics and ML workloads, the emergence of Agentic AI applications has created new architectural requirements. In 7Rivers’ Data Native™ model, organizations need seamless progression from Foundation (modern database storage) through Insights (advanced analytics) to Actions (intelligent experiences, Agentic AI, and enterprise LLMs)—and this requires operational databases that can be provisioned in seconds, scaled elastically, and destroyed at will – all autonomously.
The traditional boundaries between analytics and operational workloads are dissolving as AI applications require hybrid architectures that span both domains.
PostgreSQL has become the de-facto standard transactional database of this Foundation-to-Actions progression. Stack Overflow’s 2024 survey shows 49% developer adoption, surpassing MySQL as the preferred relational database³. More critically for Snowflake’s Augmented Enterprise strategy, PostgreSQL’s vector extensions and JSON capabilities make it the natural choice for RAG (Retrieval Augmented Generation) applications and AI-native architectures. Without PostgreSQL capabilities, Snowflake risked being relegated to the Foundation and Insights layer while competitors captured the high-value Actions layer where Agentic AI experiences and AI-infused smart apps deliver direct business impact.
The growth projections underscore the urgency. The market for AI and ML services is expected to expand at 30%+ annually through 2028, but this growth increasingly depends on platforms that can support the full spectrum of AI application requirements. Snowflake’s traditional strength in structured analytics becomes a liability when AI agents need to manage unstructured data, real-time transactions, and ephemeral compute resources across multiple data sources.
Snowflake's AI-First Repositioning: Completing the Data Native™ Architecture
Snowflake’s Crunchy Data acquisition represents more than database expansion; it’s completing their Data Native™ model to support the full Foundation-to-Actions progression. In our framework, the Foundation layer requires modern cloud database storage and management, the Insights layer delivers advanced analytics and process intelligence, and the Actions layer enables intelligent experiences and enterprise LLMs. Snowflake’s traditional strength in the Insights layer becomes a limitation when AI-centric applications need operational databases that integrate seamlessly with analytical workloads.
This shift explains why Snowflake prioritized Crunchy Data’s enterprise compliance credentials over pure technical innovation. With FedRAMP and FISMA certifications, Crunchy Data provides immediate access to federal and regulated markets where the many of their current and most desirable customers must operate within strict governance frameworks. The compliance angle isn’t just foundational, it’s essential for enterprise AI adoption where data governance and security underpin every layer of the modern enterprise data architecture.
The architectural implications extend beyond traditional data integration. In our Augmented Enterprise model, Agentic AI applications require access to diverse data sources: structured data from transactional systems, unstructured content from document stores, real-time streams from IoT devices, and historical analytics from data warehouses. Snowflake’s traditional approach of centralizing data becomes a bottleneck when intelligent experiences need to operate across multiple data environments simultaneously.
Snowflake’s positioning emphasizes becoming the orchestration layer for this hybrid environment, what we see as the control plane for progressing seamlessly from Foundation through Insights to Actions. Rather than forcing all data into their warehouse, they’re betting on unified management, governance, and cost controls across the entire Data Native™ stack. This unified management orientation is evident in their suite of Cortex features as well. The PostgreSQL integration isn’t about replacing existing operational databases, it’s about providing native operational capabilities that enable the full spectrum from advanced analytics to intelligent experiences within a single governance framework.
Enterprise Architecture Impact: Positioning for Augmented Enterprise Transformation
For architects and engineers alike, Snowflake’s Postgres integration fundamentally alters how to position the platform in enterprise transformation discussions. The value proposition shifts from individual workload optimization to enabling a complete Data Native™ architecture, where clients can progress systematically from Foundation modernization through Insights generation to Actions implementation.
The unified governance model presents the strongest competitive differentiation. While hyperscalers offer individual services optimized for specific layers, Snowflake can now provide consistent security policies, cost allocation, and resource management across the entire stack. For enterprises pursuing Augmented Enterprise transformation, this unified control plane reduces operational overhead, compliance complexity, and the architectural friction that typically prevents organizations from progressing beyond advanced analytics to intelligent experiences.
However, the positioning challenge involves demonstrating value at each layer without undermining the integrated approach. Clients often want best-of-breed solutions for Foundation (database performance), Insights (analytics speed), and Actions (SOTA AI model serving). Snowflake’s differentiation must come from seamless progression between layers and unified governance across the Data Native™ model, not pure technical superiority in any single component.
Nonetheless, cost concerns remain significant. Snowflake’s consumption-based pricing model works well for analytics workloads with predictable usage patterns, but operational databases often require constant availability and baseline resource allocation. The economics of running high-frequency transactional workloads on Snowflake’s infrastructure may limit adoption to specific AI agent use cases rather than general-purpose operational systems.
The second-order impact involves traditional OLTP applications potentially migrating to Snowflake for simplified enterprise-wide data architecture, assuming the costs concerns above can be assuaged. Organizations already standardized on Snowflake for analytics might consolidate operational workloads to reduce vendor complexity. But this requires Snowflake to demonstrate that their platform can handle mission-critical transactional systems without performance degradation or cost escalation.
The Future Battleground: Data Native™ Architecture Maturity and Enterprise Readiness
Snowflake’s success with PostgreSQL integration hinges on demonstrating complete Data Native™ architecture capabilities rather than individual database features. With established managed Postgres offerings already available from every major cloud provider, Snowflake’s window for differentiation lies in proving they can enable seamless Foundation-to-Actions progression that competitors cannot match.
The critical milestone extends beyond getting PostgreSQL into production, it’s publishing comprehensive Augmented Enterprise reference architectures that demonstrate how Foundation (PostgreSQL + existing data management), Insights (Snowflake Intelligence + advanced analytics), and Actions (enterprise LLMs + Agentic AI apps) create compelling business outcomes. The reference architecture becomes the Snowflake enterprise blueprint for positioning against best-of-breed alternatives and the validation framework for C-suite executives evaluating platform consolidation strategies.
More strategically, enterprises are increasingly evaluating vendors on their ability to support the complete transformation journey rather than individual technical capabilities. Companies like Salesforce, ServiceNow, and Microsoft are building AI-infused smart applications that require mature Foundation and Insights layers. Snowflake’s positioning as the architectural backbone for these intelligent experiences could capture significant market share if they can prove that their unified data infrastructure delivers better business outcomes than fragmented technical solutions.
Snowflake’s $250 million Postgres bet ultimately isn’t about databases; it’s about whether they can become the architectural foundation for the Augmented Enterprise. In a market where competitive advantage increasingly comes from seamless progression between Foundation, Insights, and Actions, Snowflake just bought themselves the missing piece of their vertically integrated AI architecture. The real test will be execution speed and enterprise adoption; can they integrate PostgreSQL seamlessly enough to convince organizations to abandon specialized solutions in favor of unified Data Native™ architecture?
Sources
¹ Databricks vs. Snowflake: Not a Zero-Sum Game. SiliconANGLE. July 27, 2024.
https://siliconangle.com/2024/07/27/databricks-vs-snowflake-not-zero-sum-game/
² A Deep Dive Into IDC’s Global AI and Generative AI Spending. IDC. August 16, 2024.
https://blogs.idc.com/2024/08/16/a-deep-dive-into-idcs-global-ai-and-generative-ai-spending/
³ Stack Overflow Developer Survey 2024 – Technology: Databases. Stack Overflow.
https://survey.stackoverflow.co/2024/technology
