PostgreSQL enters its AI era: Run PostgreSQL like a pro in the era of AI and rapid growth

More and more, businesses are turning to open-source tools as a strategic choice to future-proof their ecosystems rather than an affordable alternative. Increasingly, our customers are selecting PostgreSQL as a first-choice migration destination for building new, intelligent applications. Three converging forces have driven this shift: the explosion of AI workloads, cost pressures, and the need for the portability and extensibility favored for cloud-native architectures.

The evidence is everywhere. As requirements have moved beyond basic availability, Postgres continues to deliver. Postgres is the powerhouse behind everything from rapidly scaling AI apps to Fortune 500 modernization initiatives. It supports OpenAI’s 800 million ChatGPT users, and enterprises like PTC moved their databases to Azure Database for PostgreSQL for flexibility, cost optimization, and performance.

Whether it’s extensibility to support vector search, embeddings and retrieval-augmented generation pipelines, or global read distribution, leaders can be confident that Postgres can handle intelligent workloads and scale without sacrificing security, performance, reliability, or operational ease. Azure’s latest platform investments, including Premium SSD v2 storage and cascading read replicas, deliver foundational improvements that remove constraints and make it easier to run Postgres workloads regardless of size, stage, or level of expertise.

No more overprovisioning for higher throughput

Organizations have historically been forced to make difficult tradeoffs when it came to storage. IOPS and throughput are tied to disk size in traditional Premium SSD architectures, meaning if you needed more IOPS and throughput, you’d have to provision bigger disks even if you didn’t need more storage. This cost-performance discrepancy is especially troubling for engineering leaders trying to justify spend.

Premium SSD v2, which is now generally available in Azure Database for PostgreSQL, solves that problem while enabling better performance. It decouples storage capacity, IOPS, and throughput into individually adjustable dimensions. You can right-size your infrastructure, configuring your servers with just the right amount of IOPS and storage you need to support your workloads without overprovisioning and overpaying. Additionally, you can make these configuration changes without downtime so you can adjust as your workloads evolve. You’re not tied to a decision—and a price—that works for your app today but might not be a fit tomorrow.

Our internal testing has already proven the benefits to performance and cost optimization. Benchmark results show a 279% improvement in performance and about 15ms reduction in latency under load compared to Premium SSD. With monthly costs held stable, Premium SSD v2 delivers up to 169% higher throughput. These are changes that will delight your CFO and users alike. We’re also offering a baseline performance tier for dev/test environments and startup workloads so you can keep costs low during these early phases without sacrificing performance predictability.

No more growth-induced performance bottlenecks

Read replicas have become common practice for global applications to offload read traffic across regions for lower latency. In traditional configurations, each replica connects directly to the primary. However, as these global apps scale, bottlenecks can arise if the primary becomes overloaded from supporting many replicas. This would normally require complex configurations to resolve.

Azure is solving that problem with cascading read replicas. Intermediate replicas relay write-ahead logging to downstream replicas, creating multi-tier hierarchies. Organizations can now scale up to 30 replicas without overwhelming the primary instance. OpenAI maintains near-zero replication lag with this approach at massive scale. That’s production-grade consistency for unpredictable read workloads like AI inference, analytics, and global SaaS platforms.

The use cases are practical. Multi-tenant SaaS platforms can serve customers in specific geographies with local read latency while maintaining centralized write authority. Disaster recovery deployments can keep cascading replicas in secondary regions without increasing primary-instance load.

A mature infrastructure for professional workloads

Migrations come with inherent challenges and are sometimes compounded when considering a move to open source. For IT leaders evaluating a migration to PostgreSQL, they often cite concerns about performance gaps, operational maturity, and fear of hidden costs. However, what’s changed in 2026 is the maturity of the infrastructure supporting Postgres. New capabilities like Premium SSD v2 and cascading replicas reduce the risk of open source and improve readiness for AI workloads. These new features, combined with Entra ID, KeyVault Encryption, and Private Endpoints, make Postgres ideal for enterprise workloads. OpenAI’s proven scale at 800 million users eliminates the “will it handle our workload?” question. Plus, the total cost of ownership advantage that comes with open source compounds over time as workloads grow. PTC’s migration from a proprietary database to Postgres delivered cost savings while improving developer productivity.

Keep betting on open source and run Postgres like the pros

Organizations are consolidating databases rather than proliferating them, so the strategic question is no longer about optimizing for today’s constraints. It’s now about building on the platform that will support the next decade of growth. PostgreSQL’s extensibility backed by Azure makes it a viable platform choice. The Premium SSD v2 and cascading read replica announcements signal continued platform investment, not just version support stagnation.

What this enables for the Postgres community is meaningful. AI workloads can use native vector search and embeddings without separate vector databases. Global applications get low-latency read access across regions with operational simplicity. Cost remains predictable because infrastructure scales with business needs rather than forcing upfront overprovisioning. PostgreSQL’s versatility is proven across retail, industrial IoT, and SaaS platforms. The infrastructure is ready, the operational maturity is real, and the economics finally align with how teams actually want to build and scale applications.

To learn more, check out the PostgreSQL Like a Pro video series for technical deep dives on AI workloads, performance optimization, migration strategies, and scaling with PostgreSQL.