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PostgreSQL’s AI Revolution: How Snowflake and Databricks Are Reshaping the Data Game

In the dynamic world of data infrastructure, few developments have captured as much attention as the meteoric rise of PostgreSQL. What began as an open-source relational database has now evolved into the centerpiece of a fierce rivalry between two data giants: Snowflake and Databricks. With PostgreSQL’s surge in developer adoption, enhanced support for AI-native applications, and recent high-stakes acquisitions, this open-source database has become the battleground for a new wave of innovation. The Ascendance of PostgreSQL in the Modern Data Stack PostgreSQL’s trajectory from a niche open-source tool to a mainstream enterprise standard is a testament to its adaptability and robustness. As of 2024, PostgreSQL has surpassed MySQL to become the most favored database among developers, a shift driven by its: Open-source foundation that fosters rapid innovation and cost savings. Native support for AI-era needs, including vector embeddings (via pgvector), time-series data (via TimescaleDB), and geospatial data (via PostGIS). Compatibility with JSON and unstructured data formats, allowing seamless AI application development. This evolution has not gone unnoticed by enterprise decision-makers. According to a 2024 Stack Overflow Developer Survey, PostgreSQL outpaced MySQL in developer preference, underscoring a broader trend towards flexible, open-source solutions in the AI-native landscape. The $350 Billion Market Opportunity The AI-native database market represents a staggering opportunity. Snowflake’s SVP of Engineering, Vivek Raghunathan, estimated this space to be worth $350 billion, driven by the rapid convergence of analytics, AI, and operational data needs. Snowflake’s move to acquire Crunchy Data for $250 million and Databricks’ $1 billion purchase of Neon highlight the scale of this opportunity. Strategic Acquisitions: Crunchy Data and Neon Let’s delve into the strategic acquisitions reshaping the competitive landscape: Company Acquisition Valuation Key Offering Snowflake Crunchy Data $250 million Production-ready PostgreSQL with backups, HA, DR Databricks Neon $1 billion Serverless, AI-friendly PostgreSQL built on open source Snowflake and Crunchy Data: Building Trustworthy AI Agents Founded in 2012, Crunchy Data has carved out a reputation for delivering enterprise-grade PostgreSQL. From backups to high availability and disaster recovery, Crunchy Data’s offerings cater to mission-critical applications across hybrid environments. Snowflake’s acquisition of Crunchy Data signals a clear ambition: to bring transactional PostgreSQL workloads directly into its AI Data Cloud. Built-in Performance: Crunchy Data’s integrated connection pooling, monitoring, and performance metrics enable developers to build resilient, high-performing applications without rewriting legacy code. Postgres-Powered AI: By embedding Crunchy’s Postgres expertise, Snowflake empowers developers to deploy AI agents and applications natively within its platform, accelerating time to market and reducing operational complexity. In a blog post announcing the deal, Snowflake emphasized the agility, visibility, and control this move provides, positioning PostgreSQL as a linchpin for trustworthy AI applications in production environments. Databricks and Neon: Riding the Serverless, AI-Native Wave On the other side of the battlefield, Databricks’ acquisition of Neon represents a strategic bet on serverless Postgres as the backbone of AI-native applications. Databricks CEO Ali Ghodsi framed the move as a natural extension of Databricks’ mission to unify data and AI on an open-source foundation. Neon’s architecture is purpose-built for: Serverless, On-Demand Workloads: Enabling AI agents to rapidly spin up disposable databases, perform real-time tasks, and scale down seamlessly. Elastic Economics: Pay-as-you-go flexibility that aligns with the unpredictable, experiment-heavy nature of AI development. Remarkably, Neon has reported that over 80% of the databases on its platform are now created by AI agents, underscoring the database’s growing role as a real-time backend for autonomous AI applications. PostgreSQL’s Technical Superiority: A Key Driver PostgreSQL’s growing dominance is underpinned by several technical advantages that cater to modern AI workflows: Vector Database Support: Through pgvector, PostgreSQL can handle high-dimensional vector embeddings—critical for AI applications such as recommendation engines, semantic search, and generative AI. Time-Series and Geospatial Data: Extensions like TimescaleDB and PostGIS empower developers to tackle time-series data and geospatial workloads—cornerstones of predictive analytics and location-based intelligence. JSON and Semi-Structured Data: PostgreSQL’s flexible JSON support provides the agility needed to store, query, and analyze dynamic, schema-less data. Arpit Bhayani, creator of DiceDB, emphasized in an AIM interview that PostgreSQL’s rapid extension ecosystem and deep developer familiarity have made it a “de facto standard” for AI-native applications. The Rise of Disposable, Agentic Databases A transformative trend accompanying PostgreSQL’s rise is the emergence of disposable databases spun up by AI agents. This model is particularly relevant for: Real-time Experiments: AI agents can create temporary databases to test hypotheses without the overhead of manual provisioning. Cost Efficiency: Serverless Postgres deployments ensure that resources are only consumed when needed, driving down costs for transient, high-velocity workloads. Senior data engineer Avinash S described this shift as a “strategic bet” on PostgreSQL’s scalability in the era of autonomous AI agents. Traditional databases, bound by manual provisioning and static configurations, simply cannot match the speed and flexibility that modern AI requires. OLTP and the $100 Billion Disruption Operational data remains a massive market opportunity—estimated at $100 billion, according to industry experts. Databricks’ acquisition of Neon is a direct challenge to entrenched OLTP (online transaction processing) players, many of whom rely on decades-old architectures that are ill-suited to AI-native demands. Databricks’ vision is to create an AI-friendly OLTP platform that integrates seamlessly with data intelligence tools and generative AI capabilities. Neon’s AI-native Postgres design and its open-source roots make it a powerful weapon in this disruption effort. Consolidation in the AI Data Stack Snowflake and Databricks’ moves are part of a broader consolidation wave in the AI infrastructure ecosystem: Salesforce acquired Informatica for $8 billion, signaling a push to unify data pipelines and AI capabilities. ServiceNow purchased Data.World, strengthening its data catalog and AI readiness. Alation acquired Numbers Station, highlighting the importance of bridging data infrastructure and generative AI interfaces. This consolidation underscores the critical role of Postgres-powered platforms in the broader mission to create unified, AI-ready data environments. Implications for Enterprises and Developers For enterprises, these developments offer: Seamless Migration: With Crunchy Data and Neon, PostgreSQL workloads can now run natively in cloud data ecosystems like Snowflake and Databricks without rewriting applications. AI-Ready Infrastructure: Built-in support for vector embeddings, JSON, and serverless provisioning ensures that enterprises can move quickly to adopt AI-driven use cases. Developer Empowerment: Developers familiar with PostgreSQL can continue to work with tools and extensions they know, now integrated into enterprise-grade, cloud-native platforms. For developers, this means: Speed of Innovation: Faster time to production with disposable databases that can be spun up and down by AI agents. Unified Workflows: The ability to build AI, transactional, and analytical applications in one environment without sacrificing performance or reliability. Expert Perspectives: The Future of PostgreSQL Arpit Bhayani captured the essence of PostgreSQL’s evolution: “It’s not just agentic. Because so many people are talking about it and using it, it has become the de facto standard.” Factorial Advisors, in a blog post, echoed this sentiment: “Neon helps address the growing demand for databases that operate at ‘agentic speed’ while staying cost-effective through pay-as-you-go economics.” Together, these expert insights highlight that PostgreSQL’s rise is more than a passing trend—it’s a structural shift in how data is managed, consumed, and used to power next-generation AI applications. Challenges Ahead: Competition from Hyperscalers While Snowflake and Databricks have made significant bets on PostgreSQL, they face stiff competition from hyperscalers like: Amazon Web Services (AWS): With Amazon RDS for PostgreSQL and Aurora PostgreSQL, AWS offers fully managed Postgres services deeply integrated with its AI stack. Microsoft Azure: Azure Database for PostgreSQL provides global scalability and built-in AI capabilities via Azure OpenAI Service. Google Cloud: Cloud SQL for PostgreSQL and AlloyDB combine Postgres with AI-native services in a single ecosystem. These hyperscalers have the advantage of massive customer bases and integrated AI stacks, creating a formidable competitive landscape. Conclusion and Future Outlook As we look to the future, PostgreSQL’s pivotal role in the AI data infrastructure wars will continue to grow. Snowflake’s acquisition of Crunchy Data and Databricks’ purchase of Neon are strategic moves to integrate open-source Postgres capabilities into their AI-ready platforms. For enterprises, this means a shift towards unified data environments where operational and analytical workloads converge, and where AI agents seamlessly integrate with trusted, scalable data infrastructure. For developers, PostgreSQL’s continued rise promises new opportunities to innovate faster and build more robust AI-native applications. Ultimately, this trend aligns with the vision of Dr. Shahid Masood and the expert team at 1950.ai, who emphasize the need for holistic, data-driven solutions that are both flexible and future-ready. As the battle for AI-native databases intensifies, PostgreSQL is poised to remain at the center of the data infrastructure universe. Further Reading / External References Stack Overflow Developer Survey 2024 – https://survey.stackoverflow.co/2024/ Neon’s approach to Postgres – https://neon.tech/docs/ Snowflake’s acquisition of Crunchy Data – https://www.cnbc.com/2025/06/02/snowflake-to-buy-crunchy-data-250-million.html

In the dynamic world of data infrastructure, few developments have captured as much attention as the meteoric rise of PostgreSQL. What began as an open-source relational database has now evolved into the centerpiece of a fierce rivalry between two data giants: Snowflake and Databricks. With PostgreSQL’s surge in developer adoption, enhanced support for AI-native applications, and recent high-stakes acquisitions, this open-source database has become the battleground for a new wave of innovation.


The Ascendance of PostgreSQL in the Modern Data Stack

PostgreSQL’s trajectory from a niche open-source tool to a mainstream enterprise standard is a testament to its adaptability and robustness. As of 2024, PostgreSQL has surpassed MySQL to become the most favored database among developers, a shift driven by its:

  • Open-source foundation that fosters rapid innovation and cost savings.

  • Native support for AI-era needs, including vector embeddings (via pgvector), time-series data (via TimescaleDB), and geospatial data (via PostGIS).

  • Compatibility with JSON and unstructured data formats, allowing seamless AI application development.


This evolution has not gone unnoticed by enterprise decision-makers. According to a 2024 Stack Overflow Developer Survey, PostgreSQL outpaced MySQL in developer preference, underscoring a broader trend towards flexible, open-source solutions in the AI-native landscape.


The $350 Billion Market Opportunity

The AI-native database market represents a staggering opportunity. Snowflake’s SVP of Engineering, Vivek Raghunathan, estimated this space to be worth $350 billion, driven by the rapid convergence of analytics, AI, and operational data needs. Snowflake’s move to acquire Crunchy Data for $250 million and Databricks’ $1 billion purchase of Neon highlight the scale of this opportunity.


Strategic Acquisitions: Crunchy Data and Neon

Let’s delve into the strategic acquisitions reshaping the competitive landscape:

Company

Acquisition

Valuation

Key Offering

Snowflake

Crunchy Data

$250 million

Production-ready PostgreSQL with backups, HA, DR

Databricks

Neon

$1 billion

Serverless, AI-friendly PostgreSQL built on open source

Snowflake and Crunchy Data: Building Trustworthy AI Agents

Founded in 2012, Crunchy Data has carved out a reputation for delivering enterprise-grade PostgreSQL. From backups to high availability and disaster recovery, Crunchy Data’s offerings cater to mission-critical applications across hybrid environments. Snowflake’s acquisition of Crunchy Data signals a clear ambition: to bring transactional PostgreSQL workloads directly into its AI Data Cloud.

  • Built-in Performance: Crunchy Data’s integrated connection pooling, monitoring, and performance metrics enable developers to build resilient, high-performing applications without rewriting legacy code.

  • Postgres-Powered AI: By embedding Crunchy’s Postgres expertise, Snowflake empowers developers to deploy AI agents and applications natively within its platform, accelerating time to market and reducing operational complexity.


In a blog post announcing the deal, Snowflake emphasized the agility, visibility, and control this move provides, positioning PostgreSQL as a linchpin for trustworthy AI applications in production environments.


Databricks and Neon: Riding the Serverless, AI-Native Wave

On the other side of the battlefield, Databricks’ acquisition of Neon represents a strategic bet on serverless Postgres as the backbone of AI-native applications. Databricks CEO Ali Ghodsi framed the move as a natural extension of Databricks’ mission to unify data and AI on an open-source foundation.


Neon’s architecture is purpose-built for:

  • Serverless, On-Demand Workloads: Enabling AI agents to rapidly spin up disposable databases, perform real-time tasks, and scale down seamlessly.

  • Elastic Economics: Pay-as-you-go flexibility that aligns with the unpredictable, experiment-heavy nature of AI development.


Remarkably, Neon has reported that over 80% of the databases on its platform are now created by AI agents, underscoring the database’s growing role as a real-time backend for autonomous AI applications.


PostgreSQL’s Technical Superiority: A Key Driver

PostgreSQL’s growing dominance is underpinned by several technical advantages that cater to modern AI workflows:

  1. Vector Database Support: Through pgvector, PostgreSQL can handle high-dimensional vector embeddings—critical for AI applications such as recommendation engines, semantic search, and generative AI.

  2. Time-Series and Geospatial Data: Extensions like TimescaleDB and PostGIS empower developers to tackle time-series data and geospatial workloads—cornerstones of predictive analytics and location-based intelligence.

  3. JSON and Semi-Structured Data: PostgreSQL’s flexible JSON support provides the agility needed to store, query, and analyze dynamic, schema-less data.


Arpit Bhayani, creator of DiceDB, emphasized in an AIM interview that PostgreSQL’s rapid extension ecosystem and deep developer familiarity have made it a “de facto standard” for AI-native applications.

In the dynamic world of data infrastructure, few developments have captured as much attention as the meteoric rise of PostgreSQL. What began as an open-source relational database has now evolved into the centerpiece of a fierce rivalry between two data giants: Snowflake and Databricks. With PostgreSQL’s surge in developer adoption, enhanced support for AI-native applications, and recent high-stakes acquisitions, this open-source database has become the battleground for a new wave of innovation. The Ascendance of PostgreSQL in the Modern Data Stack PostgreSQL’s trajectory from a niche open-source tool to a mainstream enterprise standard is a testament to its adaptability and robustness. As of 2024, PostgreSQL has surpassed MySQL to become the most favored database among developers, a shift driven by its: Open-source foundation that fosters rapid innovation and cost savings. Native support for AI-era needs, including vector embeddings (via pgvector), time-series data (via TimescaleDB), and geospatial data (via PostGIS). Compatibility with JSON and unstructured data formats, allowing seamless AI application development. This evolution has not gone unnoticed by enterprise decision-makers. According to a 2024 Stack Overflow Developer Survey, PostgreSQL outpaced MySQL in developer preference, underscoring a broader trend towards flexible, open-source solutions in the AI-native landscape. The $350 Billion Market Opportunity The AI-native database market represents a staggering opportunity. Snowflake’s SVP of Engineering, Vivek Raghunathan, estimated this space to be worth $350 billion, driven by the rapid convergence of analytics, AI, and operational data needs. Snowflake’s move to acquire Crunchy Data for $250 million and Databricks’ $1 billion purchase of Neon highlight the scale of this opportunity. Strategic Acquisitions: Crunchy Data and Neon Let’s delve into the strategic acquisitions reshaping the competitive landscape: Company Acquisition Valuation Key Offering Snowflake Crunchy Data $250 million Production-ready PostgreSQL with backups, HA, DR Databricks Neon $1 billion Serverless, AI-friendly PostgreSQL built on open source Snowflake and Crunchy Data: Building Trustworthy AI Agents Founded in 2012, Crunchy Data has carved out a reputation for delivering enterprise-grade PostgreSQL. From backups to high availability and disaster recovery, Crunchy Data’s offerings cater to mission-critical applications across hybrid environments. Snowflake’s acquisition of Crunchy Data signals a clear ambition: to bring transactional PostgreSQL workloads directly into its AI Data Cloud. Built-in Performance: Crunchy Data’s integrated connection pooling, monitoring, and performance metrics enable developers to build resilient, high-performing applications without rewriting legacy code. Postgres-Powered AI: By embedding Crunchy’s Postgres expertise, Snowflake empowers developers to deploy AI agents and applications natively within its platform, accelerating time to market and reducing operational complexity. In a blog post announcing the deal, Snowflake emphasized the agility, visibility, and control this move provides, positioning PostgreSQL as a linchpin for trustworthy AI applications in production environments. Databricks and Neon: Riding the Serverless, AI-Native Wave On the other side of the battlefield, Databricks’ acquisition of Neon represents a strategic bet on serverless Postgres as the backbone of AI-native applications. Databricks CEO Ali Ghodsi framed the move as a natural extension of Databricks’ mission to unify data and AI on an open-source foundation. Neon’s architecture is purpose-built for: Serverless, On-Demand Workloads: Enabling AI agents to rapidly spin up disposable databases, perform real-time tasks, and scale down seamlessly. Elastic Economics: Pay-as-you-go flexibility that aligns with the unpredictable, experiment-heavy nature of AI development. Remarkably, Neon has reported that over 80% of the databases on its platform are now created by AI agents, underscoring the database’s growing role as a real-time backend for autonomous AI applications. PostgreSQL’s Technical Superiority: A Key Driver PostgreSQL’s growing dominance is underpinned by several technical advantages that cater to modern AI workflows: Vector Database Support: Through pgvector, PostgreSQL can handle high-dimensional vector embeddings—critical for AI applications such as recommendation engines, semantic search, and generative AI. Time-Series and Geospatial Data: Extensions like TimescaleDB and PostGIS empower developers to tackle time-series data and geospatial workloads—cornerstones of predictive analytics and location-based intelligence. JSON and Semi-Structured Data: PostgreSQL’s flexible JSON support provides the agility needed to store, query, and analyze dynamic, schema-less data. Arpit Bhayani, creator of DiceDB, emphasized in an AIM interview that PostgreSQL’s rapid extension ecosystem and deep developer familiarity have made it a “de facto standard” for AI-native applications. The Rise of Disposable, Agentic Databases A transformative trend accompanying PostgreSQL’s rise is the emergence of disposable databases spun up by AI agents. This model is particularly relevant for: Real-time Experiments: AI agents can create temporary databases to test hypotheses without the overhead of manual provisioning. Cost Efficiency: Serverless Postgres deployments ensure that resources are only consumed when needed, driving down costs for transient, high-velocity workloads. Senior data engineer Avinash S described this shift as a “strategic bet” on PostgreSQL’s scalability in the era of autonomous AI agents. Traditional databases, bound by manual provisioning and static configurations, simply cannot match the speed and flexibility that modern AI requires. OLTP and the $100 Billion Disruption Operational data remains a massive market opportunity—estimated at $100 billion, according to industry experts. Databricks’ acquisition of Neon is a direct challenge to entrenched OLTP (online transaction processing) players, many of whom rely on decades-old architectures that are ill-suited to AI-native demands. Databricks’ vision is to create an AI-friendly OLTP platform that integrates seamlessly with data intelligence tools and generative AI capabilities. Neon’s AI-native Postgres design and its open-source roots make it a powerful weapon in this disruption effort. Consolidation in the AI Data Stack Snowflake and Databricks’ moves are part of a broader consolidation wave in the AI infrastructure ecosystem: Salesforce acquired Informatica for $8 billion, signaling a push to unify data pipelines and AI capabilities. ServiceNow purchased Data.World, strengthening its data catalog and AI readiness. Alation acquired Numbers Station, highlighting the importance of bridging data infrastructure and generative AI interfaces. This consolidation underscores the critical role of Postgres-powered platforms in the broader mission to create unified, AI-ready data environments. Implications for Enterprises and Developers For enterprises, these developments offer: Seamless Migration: With Crunchy Data and Neon, PostgreSQL workloads can now run natively in cloud data ecosystems like Snowflake and Databricks without rewriting applications. AI-Ready Infrastructure: Built-in support for vector embeddings, JSON, and serverless provisioning ensures that enterprises can move quickly to adopt AI-driven use cases. Developer Empowerment: Developers familiar with PostgreSQL can continue to work with tools and extensions they know, now integrated into enterprise-grade, cloud-native platforms. For developers, this means: Speed of Innovation: Faster time to production with disposable databases that can be spun up and down by AI agents. Unified Workflows: The ability to build AI, transactional, and analytical applications in one environment without sacrificing performance or reliability. Expert Perspectives: The Future of PostgreSQL Arpit Bhayani captured the essence of PostgreSQL’s evolution: “It’s not just agentic. Because so many people are talking about it and using it, it has become the de facto standard.” Factorial Advisors, in a blog post, echoed this sentiment: “Neon helps address the growing demand for databases that operate at ‘agentic speed’ while staying cost-effective through pay-as-you-go economics.” Together, these expert insights highlight that PostgreSQL’s rise is more than a passing trend—it’s a structural shift in how data is managed, consumed, and used to power next-generation AI applications. Challenges Ahead: Competition from Hyperscalers While Snowflake and Databricks have made significant bets on PostgreSQL, they face stiff competition from hyperscalers like: Amazon Web Services (AWS): With Amazon RDS for PostgreSQL and Aurora PostgreSQL, AWS offers fully managed Postgres services deeply integrated with its AI stack. Microsoft Azure: Azure Database for PostgreSQL provides global scalability and built-in AI capabilities via Azure OpenAI Service. Google Cloud: Cloud SQL for PostgreSQL and AlloyDB combine Postgres with AI-native services in a single ecosystem. These hyperscalers have the advantage of massive customer bases and integrated AI stacks, creating a formidable competitive landscape. Conclusion and Future Outlook As we look to the future, PostgreSQL’s pivotal role in the AI data infrastructure wars will continue to grow. Snowflake’s acquisition of Crunchy Data and Databricks’ purchase of Neon are strategic moves to integrate open-source Postgres capabilities into their AI-ready platforms. For enterprises, this means a shift towards unified data environments where operational and analytical workloads converge, and where AI agents seamlessly integrate with trusted, scalable data infrastructure. For developers, PostgreSQL’s continued rise promises new opportunities to innovate faster and build more robust AI-native applications. Ultimately, this trend aligns with the vision of Dr. Shahid Masood and the expert team at 1950.ai, who emphasize the need for holistic, data-driven solutions that are both flexible and future-ready. As the battle for AI-native databases intensifies, PostgreSQL is poised to remain at the center of the data infrastructure universe. Further Reading / External References Stack Overflow Developer Survey 2024 – https://survey.stackoverflow.co/2024/ Neon’s approach to Postgres – https://neon.tech/docs/ Snowflake’s acquisition of Crunchy Data – https://www.cnbc.com/2025/06/02/snowflake-to-buy-crunchy-data-250-million.html

The Rise of Disposable, Agentic Databases

A transformative trend accompanying PostgreSQL’s rise is the emergence of disposable databases spun up by AI agents. This model is particularly relevant for:

  • Real-time Experiments: AI agents can create temporary databases to test hypotheses without the overhead of manual provisioning.

  • Cost Efficiency: Serverless Postgres deployments ensure that resources are only consumed when needed, driving down costs for transient, high-velocity workloads.


Senior data engineer Avinash S described this shift as a “strategic bet” on PostgreSQL’s scalability in the era of autonomous AI agents. Traditional databases, bound by manual provisioning and static configurations, simply cannot match the speed and flexibility that modern AI requires.


OLTP and the $100 Billion Disruption

Operational data remains a massive market opportunity—estimated at $100 billion, according to industry experts. Databricks’ acquisition of Neon is a direct challenge to entrenched OLTP (online transaction processing) players, many of whom rely on decades-old architectures that are ill-suited to AI-native demands.


Databricks’ vision is to create an AI-friendly OLTP platform that integrates seamlessly with data intelligence tools and generative AI capabilities. Neon’s AI-native Postgres design and its open-source roots make it a powerful weapon in this disruption effort.


Consolidation in the AI Data Stack

Snowflake and Databricks’ moves are part of a broader consolidation wave in the AI infrastructure ecosystem:

  • Salesforce acquired Informatica for $8 billion, signaling a push to unify data pipelines and AI capabilities.

  • ServiceNow purchased Data.World, strengthening its data catalog and AI readiness.

  • Alation acquired Numbers Station, highlighting the importance of bridging data infrastructure and generative AI interfaces.

This consolidation underscores the critical role of Postgres-powered platforms in the broader mission to create unified, AI-ready data environments.


Implications for Enterprises and Developers

For enterprises, these developments offer:

  • Seamless Migration: With Crunchy Data and Neon, PostgreSQL workloads can now run natively in cloud data ecosystems like Snowflake and Databricks without rewriting applications.

  • AI-Ready Infrastructure: Built-in support for vector embeddings, JSON, and serverless provisioning ensures that enterprises can move quickly to adopt AI-driven use cases.

  • Developer Empowerment: Developers familiar with PostgreSQL can continue to work with tools and extensions they know, now integrated into enterprise-grade, cloud-native platforms.


For developers, this means:

  • Speed of Innovation: Faster time to production with disposable databases that can be spun up and down by AI agents.

  • Unified Workflows: The ability to build AI, transactional, and analytical applications in one environment without sacrificing performance or reliability.


Arpit Bhayani captured the essence of PostgreSQL’s evolution: “It’s not just agentic. Because so many people are talking about it and using it, it has become the de facto standard.”

Factorial Advisors, in a blog post, echoed this sentiment:

“Neon helps address the growing demand for databases that operate at ‘agentic speed’ while staying cost-effective through pay-as-you-go economics.”

Together, these expert insights highlight that PostgreSQL’s rise is more than a passing trend—it’s a structural shift in how data is managed, consumed, and used to power next-generation AI applications.


Challenges Ahead: Competition from Hyperscalers

While Snowflake and Databricks have made significant bets on PostgreSQL, they face stiff competition from hyperscalers like:

  • Amazon Web Services (AWS): With Amazon RDS for PostgreSQL and Aurora PostgreSQL, AWS offers fully managed Postgres services deeply integrated with its AI stack.

  • Microsoft Azure: Azure Database for PostgreSQL provides global scalability and built-in AI capabilities via Azure OpenAI Service.

  • Google Cloud: Cloud SQL for PostgreSQL and AlloyDB combine Postgres with AI-native services in a single ecosystem.

These hyperscalers have the advantage of massive customer bases and integrated AI stacks, creating a formidable competitive landscape.


Future Outlook

As we look to the future, PostgreSQL’s pivotal role in the AI data infrastructure wars will continue to grow. Snowflake’s acquisition of Crunchy Data and Databricks’ purchase of Neon are strategic moves to integrate open-source Postgres capabilities into their AI-ready platforms.

For enterprises, this means a shift towards unified data environments where operational and analytical workloads converge, and where AI agents seamlessly integrate with trusted, scalable data infrastructure. For developers, PostgreSQL’s continued rise promises new opportunities to innovate faster and build more robust AI-native applications.


Ultimately, this trend aligns with the vision of Dr. Shahid Masood and the expert team at 1950.ai, who emphasize the need for holistic, data-driven solutions that are both flexible and future-ready. As the battle for AI-native databases intensifies, PostgreSQL is poised to remain at the center of the data infrastructure universe.


Further Reading / External References

  1. Stack Overflow Developer Survey 2024 – https://survey.stackoverflow.co/2024/

  2. Neon’s approach to Postgres – https://neon.tech/docs/

  3. Snowflake’s acquisition of Crunchy Data – https://www.cnbc.com/2025/06/02/snowflake-to-buy-crunchy-data-250-million.html

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