In recent years, the database landscape has witnessed a series of transformative shifts, characterized by the rise and fall of various technologies such as vectors, graph databases, and NoSQL systems. Each of these innovations promised enhanced development experiences and new capabilities, yet many ultimately fell short of their lofty expectations. The advent of artificial intelligence (AI) has fundamentally altered this narrative, challenging the very foundations of managed database services and exposing the limitations of previous models.
As organizations grapple with the demands of AI workloads, a notable trend has emerged: a resurgence in the adoption of PostgreSQL (Postgres). This relational database management system is increasingly being recognized as the backbone for modern AI applications, offering the flexibility, performance, and cost control that teams require. Developers are now more likely to incorporate Postgres into their tech stacks, with its popularity surging as the most favored database system in 2025.
How AI Workloads Broke the Managed Database Model
The managed database ecosystem flourished during a time of predictable workloads, where lift-and-shift migrations to cloud platforms like Amazon RDS and Azure SQL were commonplace. Applications typically followed a straightforward SaaS model, characterized by modest working sets and gradual scaling. However, the introduction of AI workloads has disrupted this paradigm. AI applications are inherently bursty, demanding high parallelism and continuous ingestion of large datasets, which contrasts sharply with the predictable patterns of traditional applications.
Engineering teams have reported challenges when scaling managed Postgres instances during AI model rollouts, encountering issues such as IOPS limits, throttling, and latency spikes. These constraints become particularly problematic as AI workloads reach production scale, prompting teams to reconsider the viability of managed database solutions.
The Convergence on Postgres for Modern Development
In response to these challenges, major database vendors are increasingly emphasizing PostgreSQL compatibility. This trend reflects a growing recognition of developers’ needs for a stable, well-understood SQL system that offers strong transactional support and broad tooling compatibility. Postgres stands out due to its decades of refinement, production-proven reliability, and versatility across various workloads, including OLTP, analytics, and time series data.
Postgres has decades of refinement that newer systems cannot match. And it’s production-proven and rock solid.
As AI teams seek to streamline their operations, they are gravitating towards solutions that minimize complexity. The ability to unify diverse workloads within a single database system not only reduces operational overhead but also enhances performance by keeping data local. This convergence is evident in the evolving product roadmaps across the industry.
Why Managed Postgres Cannot Handle AI Scale
Despite the advantages of Postgres, the traditional managed database model presents significant limitations. Managed solutions often rely on network storage, which introduces latency and IOPS constraints that are ill-suited for the demands of AI workloads. As teams encounter these bottlenecks, they find themselves resorting to overprovisioning, leading to escalating costs that undermine the efficiency of their operations.
The Rise of BYOC Postgres
A new model is emerging among teams developing AI features: Bring Your Own Cloud (BYOC) Postgres. This approach allows organizations to maintain control over their cloud environments while leveraging the benefits of managed services. By colocating data with compute resources, teams can achieve unlimited IOPS and eliminate the performance bottlenecks associated with traditional managed databases.
The BYOC model aligns with compliance frameworks, ensuring that data remains within the organization’s cloud account and adheres to security protocols. This shift not only enhances performance but also simplifies operational complexity, allowing teams to focus on innovation rather than infrastructure management.
How Data Locality and Local Storage Improve Performance
Utilizing local NVMe storage in conjunction with Postgres can significantly enhance performance by reducing storage latency and eliminating IOPS limitations. Solutions like Vela enable teams to deploy Postgres on instances with local storage, resulting in faster data access and improved query performance. This architecture not only supports high-speed ingestion but also facilitates the creation of extensive vector indexes without compromising system stability.
Cloning and Branching Become Central To AI Development
The rapid pace of AI development necessitates efficient experimentation and testing workflows. Traditional managed databases often struggle with cloning processes that are slow and resource-intensive. In contrast, modern Postgres platforms utilize thin clones that leverage copy-on-write semantics, allowing teams to create and manage multiple environments without the overhead of full dataset copies.
Once a team experiences clone-based workflows, they rarely go back.
This capability is particularly advantageous for AI development, enabling parallel experiments and rapid validation of models against real data. The efficiency gained from this approach translates into shorter feedback loops and faster iteration cycles, ultimately enhancing developer productivity.
The Importance of the Postgres Ecosystem for AI
Postgres’s robust ecosystem allows it to handle a diverse array of workloads, from transactional processing to vector search and time series analytics. This versatility reduces the need for multiple specialized databases, streamlining data management and minimizing operational complexity. The ability to run various workloads on a single system not only improves performance but also simplifies deployment patterns and enhances reproducibility.
Developer Velocity: The Hidden Driver of the Shift
While performance and cost are critical metrics, developer velocity plays an equally vital role in the shift back to Postgres. The iterative nature of AI development requires rapid feedback and safe testing environments. Modern Postgres platforms facilitate this by providing developers with intuitive tools for creating branches, running tests, and merging changes seamlessly into their workflows.
As teams embrace these new capabilities, they recognize the strategic advantage of faster iteration and reduced time spent on manual processes. The result is a more agile development environment that empowers teams to innovate and respond to market demands with confidence.
In summary, the resurgence of PostgreSQL as a foundational technology for AI applications reflects a broader trend toward unifying data management under a single, powerful system. As organizations seek to harness the full potential of AI, Postgres stands poised to play a central role in shaping the future of data architecture.
