On February 3, 2026, Snowflake Inc. (NYSE: SNOW) announced significant updates to Snowflake Postgres, emphasizing open interoperability and making enterprise data AI-ready by design. These enhancements allow Snowflake Postgres to run natively in the AI Data Cloud, consolidating transactional, analytical, and AI workloads on a single, secure platform. Powered by pg_lake, a set of PostgreSQL extensions, it enables direct querying, managing, and writing to Apache Iceberg tables using standard SQL, eliminating costly data movement between systems. This move addresses fragmentation in data architectures, reducing costs, risks, and delays while fostering real-time AI applications. As AI transitions from experimentation to production, these updates position Snowflake as a unified foundation for enterprise intelligence.
Key Features: Multimodal Processing and Beyond
Snowflake Postgres introduces advanced features that make data inherently AI-ready, including support for multimodal processing through integration with Cortex AI. Multimodal capabilities allow handling of diverse data types—text, images, audio, and video—enabling complex AI workflows like object detection, visual Q&A, and speech recognition without external pipelines. For instance, users can ingest real-time unstructured data from sources like SharePoint or Google Drive, processing it alongside structured transactional data.
Other features include enterprise-grade security with role-based access, data masking, and audit logs, ensuring compliance for sensitive AI workloads. The platform’s serverless architecture provides automatic scaling, in-place upgrades, and insights into key metrics, streamlining management. These enhancements reduce operational silos, allowing developers to build apps and AI agents faster with zero code changes for existing Postgres applications.
Benefits for Marketing and Healthcare Sectors
In marketing, Snowflake Postgres enables optimization of campaigns by correlating visual elements in promotional assets with conversion metrics using multimodal AI. Teams can augment first-party data with third-party sources from Snowflake Marketplace, improving segmentation and ROI predictions. For example, real-time ingestion via Snowpipe supports dynamic personalization, enhancing customer experiences and launch effectiveness.
In healthcare, the platform improves patient outcomes by integrating imaging metadata with treatment protocols and demographics. It supports HL7/FHIR message processing for interoperability, enabling collaborative systems. Practical applications include clinical decision support, medical record analysis, and real-time monitoring from devices, accelerating trials and reducing costs.
Market Projections to 2030
The AI market is poised for explosive growth, projected to surge from $279.2 billion in 2024 to $1.81 trillion by 2030, a sixfold increase. Cloud computing, integral to AI, is expected to expand at a 21.2% CAGR, reaching massive scales by 2030. Snowflake’s AI Data Cloud captures this momentum, with 15% market share in cloud-native platforms and 29% YoY revenue growth to $3.63 billion in 2024. AI influences 50% of bookings, positioning Snowflake for 23.6% average growth over five years. Agentic AI markets could hit $52 billion by 2030, with Snowflake’s tools enabling widespread adoption.
User Adoption Statistics
Snowflake boasts over 12,600 customers globally, with more than 7,300 accounts using AI weekly—a record high. AI revenue run rate exceeds $100 million, ahead of schedule, with 92% of early adopters seeing ROI and 98% planning increased investments in 2026. Customer base grew 21% YoY to 12,062, driven by AI capabilities. Over 4,000 customers use Snowflake for AI/ML weekly, highlighting rapid adoption.
Comparisons to Other Databases
Compared to Oracle or MySQL, Snowflake Postgres offers superior multi-cloud neutrality and unified architecture, eliminating ETL pipelines. It supports low-latency CDC for real-time AI, unlike traditional OLTP systems requiring separate analytics. Vs. Databricks, it excels in SQL-focused workflows with less overhead. Full Postgres compatibility allows seamless migrations, with pg_vector for RAG applications.
Integration with Cortex AI
Integration with Cortex AI enables agents to reason over fresh transactional data, building context-aware apps. Cortex handles multimodal inference, with tools like Cortex Code for ML pipelines. This unification powers real-time features like recommendations without data silos.
Practical Examples and Expert Quotes
BlueCloud uses Snowflake Postgres to simplify architectures, running AI on connected data without silos. Sigma Computing delivers low-latency workloads on a unified platform. In healthcare, a demo shows real-time appointment tracking with CDC pipelines.
Snowflake CEO Sridhar Ramaswamy states, “With our latest product advancements, we’re reimagining how teams build and operate by embedding AI directly into the development lifecycle.” Sigma’s Mike Willett notes, “Snowflake Postgres handles this high transaction volume easily.” BlueCloud’s rep says, “With Snowflake Postgres, we can work directly on fresh transactional data.”
Conclusion: Scaling AI Confidently
Snowflake Postgres enhancements empower enterprises to scale AI with trusted, real-time data, bridging transactions and intelligence. As AI adoption surges—78 million net jobs by 2030—platforms like this mitigate disruptions, enabling governed, efficient scaling. With robust interoperability and Cortex integration, Snowflake drives innovation, ensuring AI delivers real business impact.