Why Ai Workloads Are Fueling A Move Back To Postgres

I person spent nan past mates of years watching nan database scenery move done waves of excitement and disappointment. Vectors, graph, multimodal and NoSQL systems each took turns successful nan spotlight. Each activity promised simpler improvement and caller possibilities. Some delivered. Some did not. Most made consciousness successful their moment.

Then AI arrived. AI did not simply agelong existing systems. It collapsed nan assumptions that shaped nan past procreation of managed database services. It exposed hidden trade-offs that were easy to disregard erstwhile workloads were lighter and changes were slower.

It besides pushed teams to rethink really they activity pinch data. Today, I spot a clear displacement successful nan market. Teams are moving backmost to Postgres. More and much caller applications commencement pinch Postgres successful nan stack. Postgres is becoming nan database of AI. If an technologist is building immoderate caller exertion today, they are very apt going to usage Postgres successful their stack. It’s nan most celebrated database strategy successful 2025 by far.

I want to explicate why this displacement is happening (at slightest successful my humble opinion). I want to picture why Postgres is silently becoming nan anchor of modern AI development. I besides want to explicate why galore teams should see leaving afloat managed databases behind.

This is not astir nostalgia and self-hosting successful nan aged sense. It is astir a caller exemplary that keeps nan benefits of managed services while giving teams nan performance, costs power and information locality they request for nan adjacent decade. The caller exemplary is BYOC (Bring Your Own Cloud).

How AI Workloads Broke nan Managed Database Model

The full managed database ecosystem grew during a play of predictable workloads. Lift-and-shift migrations into nan unreality were nan backbone of nan maturation of services for illustration Amazon Relational Database Service (RDS) aliases Azure Managed SQL. First, you lift-and-shift onto nan plain Elastic Compute Cloud2 (EC2) instance, and past you move to RDS. Straightforward playbook, everyone did nan aforesaid thing. No brainer.

Most applications behaved for illustration classical Software arsenic a Service (SaaS) products. They had humble moving sets. They utilized straightforward online transaction processing (OLTP) patterns. They scaled gradually. They relied heavy connected network-attached storage, autoscaling groups and unchangeable indexing structures. Performance was usually bully enough. Latency was acceptable. Costs were manageable. And past AI showed up.

AI workloads behave very differently. They are bursty. They trust connected dense parallelism. They usage vector hunt and high-dimensional embeddings. They ingest ample datasets continuously. They require predominant experiments, accelerated cloning and galore isolated environments. They besides require tight proximity betwixt compute nodes and information storage. The spread betwixt aged and caller patterns creates clash that managed databases cannot hide anymore.

I speak pinch engineering teams each week. They each picture akin experiences. They effort to standard a managed Postgres lawsuit during a exemplary rollout. They deed IOPS limits. They deed throttling windows. They spot latency spikes astatine nan nonstop infinitesimal they request predictability. They besides spot costs blowups because nan only measurement to stay safe is to overprovision each environment. These problems accumulate slow astatine first. Then they go unmanageable erstwhile AI workloads scope accumulation scale.

This is nan infinitesimal erstwhile teams commencement questioning nan managed exemplary itself.

The Convergence connected Postgres for Modern Development

Almost each awesome database vendor now talks astir PostgreSQL compatibility. Some dainty it arsenic elemental marketing. They consciousness FOMO and want to “jump connected nan Postgres ship.” It’s unclear really their connection adds worth to nan already competitory Postgres market, but they make nan jump first and interest astir nan go-to-market strategy later. Others rebuild their full motor astir it.

These vendors do that because they expect developers’ needs. Developers want a unchangeable and well-understood SQL system. They want beardown transactions. They want predictable joins. They want wide tooling support. They want a database that does not fastener them into a azygous institution aliases architecture. They want unfastened source.

Postgres has decades of refinement that newer systems cannot match. And it’s production-proven and stone solid.

Postgres delivers each of this without forcing teams into a specialized model. It is elastic capable to service arsenic an OLTP engine. It tin grip analytics. It tin shop vectors. It tin tally clip bid workloads. It tin service arsenic a cache. It has extensions for almost everything. It has decades of refinement that newer systems cannot match. And it’s production-proven and stone solid.

AI strengthens this convergence. AI teams want less moving parts. They want simpler pipelines. They want transactional information mixed pinch analytical capability, arsenic they don’t person clip to fig retired caller database architectures.

They want to move accelerated successful this emerging market. They want vector hunt without maintaining a abstracted vector store. They want to trial caller features connected existent information without analyzable information sync jobs. They want to query crossed information models. Postgres gives them nan opportunity to unify these workloads successful 1 place.

I spot much teams removing full layers of their information stack because they recognize that Postgres tin grip nan immense mostly of their needs pinch nan correct infrastructure down it.

I spot much teams removing full layers of their information stack because they recognize that Postgres tin grip nan immense mostly of their needs pinch nan correct infrastructure down it. They get little latency. They get less operational surprises. They get a simpler improvement workflow. Most importantly, they get a single, well-understood information strategy that fits some nan exertion and nan AI pipeline.

The displacement is not theoretical. It is visible successful merchandise roadmaps crossed nan industry.

Why Managed Postgres Cannot Handle AI Scale 

We person now established that Postgres is nan caller halfway of gravity. The adjacent mobility is wherever and really to tally it. For years, nan default reply was simple. Use RDS. Use Aurora. Use Cloud SQL. The transportation was simple: Let personification other tally Postgres.

“The days of DBAs are gone,” they said. Most developers liked this idea. It removed infrastructure work from nan captious path. It reduced operational overhead. It shifted nan work of managing databases to nan unreality vendor.

But nan exemplary has a hidden constraint. A managed database intends a one-size-fits-all solution. Users dangle heavy connected web storage. They judge web latency. They judge fixed IOPS limits. They judge multisecond acold starts. They judge nan costs building that comes pinch these designs. These trade-offs made consciousness 10 years ago. But why would you request to salary for IOPS successful 2025? The pricing exemplary still treats IOPS arsenic scarce, moreover though modern Non-Volatile Memory Express (NVMe) changes nan equation.

AI workloads request highly accelerated retention and predictable performance. They besides require ample and predominant database clones for testing and experimentation.

Managed databases struggle successful some areas. The soul retention layers of managed systems create unavoidable bottlenecks. The cloning mechanisms dangle connected snapshot-restore cycles aliases full-blown beingness copies. Both approaches are slow and expensive, particularly astatine scale.

Once a squad hits these limits, nan only hole is overprovisioning. You support expanding nan lawsuit size. You support oversized replicas. You tally afloat staging environments 24 hours a day, moreover erstwhile they beryllium idle. Your costs turn faster than your product. This is nan other of what teams want successful nan AI era.

This is nan constituent wherever teams statesman looking for alternatives that springiness them nan afloat powerfulness of Postgres without nan restrictions of managed systems.

The Rise of BYOC Postgres 

I spot a caller shape emerging crossed teams building superior AI features. They want Postgres successful their ain unreality account. They want power complete compute and storage. They want to colocate information pinch GPUs. They want unlimited IOPS. But first and foremost, they still want nan benefits of an automated acquisition that gives them backups, replication and monitoring.

This is nan BYOC model. It is not accepted self-hosting. It is simply a managed level that runs wrong your ain unreality environment. You support afloat power complete infrastructure. You support your unreality discounts. You support your information posture. You besides support power complete wherever information physically lives, which matters for information residency and regulatory requirements.

This exemplary aligns people pinch compliance frameworks for illustration SOC 2, HIPAA, GDPR and CCPA. Data ne'er leaves your account. Encryption is handled pinch your ain keys. Key guidance integrates pinch your existing cardinal guidance work setup. Tenant isolation follows nan aforesaid boundaries you already spot crossed nan remainder of your infrastructure.

The level takes attraction of operational complexity for illustration backups, replication, upgrades and nonaccomplishment handling. You enactment successful power of policies, entree and audit boundaries. For galore teams, this is nan first clip managed Postgres really fits their information and compliance exemplary alternatively of fighting it.

How Data Locality and Local Storage Improve Performance

To further adhd to BYOC benefits, pinch nan correct tooling, this exemplary resolves capacity problems by removing nan networked retention bottleneck. Solutions specified arsenic Vela fto you deploy Postgres connected nan aforesaid lawsuit wherever your retention is, leveraging nan velocity and capacity of section NVMe devices attached to nan instance. Using distributed simplyblock storage “under nan hood,” it provides resilience and scalability, arsenic good arsenic copy-on-write functionality, which are different not disposable pinch section storage. And that’s each deployed and managed successful your ain cloud. All you request to do is proviso a unreality lawsuit pinch section NVMe devices.

Results? Storage latency drops into nan microsecond range. IOPS limits disappear. Parallel ingestion becomes not only applicable but required to scope nan database’s limits. Extensive vector indexes nary longer punish nan strategy during rebuilds. Queries enactment predictable moreover nether dense load.

BYOC besides solves nan costs problem because you salary nan unreality supplier straight for compute, RAM and storage. There is nary markup. There are nary IOPS charges. There is nary forced overprovisioning of galore full-size environments. You only tally nan compute you really need, and further environments are spun up successful seconds, pinch aliases without an existing dataset. This exemplary useful particularly good erstwhile mixed pinch database cloning.

And this brings maine to nan astir captious workflow shift.

Cloning and Branching Become Central To AI Development 

AI improvement depends connected accelerated experimentation. Teams request to trial caller models connected existent data. They request to validate prompts and embeddings. They request to tally migrations. They request to isolate characteristic branches. They request to replay events. They request to measure pipelines pinch safety. This workflow requires a changeless watercourse of cleanable environments.

Traditional managed databases create clones by copying nan full dataset. This attack is slow, costly and wasteful. It limits nan number of environments you tin maintain. It forces developers to trim corners. It besides delays testing because each clone takes existent clip to produce.

Once a squad experiences clone-based workflows, they seldom spell back.

Modern Postgres platforms alteration this pinch bladed clones that trust connected copy-on-write semantics. A clone starts instantly because it shares nan underlying information pinch nan accumulation database. Storage depletion grows only arsenic nan clone diverges. Performance remains stable. You tin create arsenic galore clones arsenic you want. You tin connect them to and automate them for your CI pipelines. You tin necktie them straight to characteristic branches. You tin destruct them arsenic soon arsenic nan trial ends.

This exemplary fits AI improvement perfectly. It lets you tally parallel experiments without waiting for terabytes of information to copy. It enables you to comparison results crossed environments. It allows you to build assurance earlier deploying changes. It besides reduces nan number of full-sized databases you request to salary for.

Once a squad experiences clone-based workflows, they seldom spell back.

The Importance of nan Postgres Ecosystem for AI

AI systems usually dangle connected galore specialized databases. You had nan transactional database for nan product. You had a vector shop for embeddings. You had a information storage for analytics. You had a clip bid strategy for metrics. You had a full-text hunt motor for retrieval. You had pipelines that moved aliases synchronized information betwixt them. This architecture created complexity and costs because information had to move constantly.

One of nan cardinal strengths of Postgres is its ecosystem. Thanks to its community, Postgres handles embedding hunt pinch pgvector. It handles analytical workloads astatine debased to mid-range scales because NVMe-backed retention removes galore humanities publication bottlenecks (and PostgreSQL 18 adds async-read support). It handles clip bid information pinch and without extensions. It handles caching patterns done materialized views. It handles arena ingestion and watercourse processing pinch logical replication. And it still handles OLTP pinch nan beardown consistency it’s known for.

The expertise to tally each of these workloads connected a azygous strategy changes nan style of nan AI backend. You get less moving parts. You get little latency because information stays local. You get simpler deployment patterns. You get reproducible pipelines. And you get little operational overhead.

The Postgres ecosystem provides nan expertise to move nan database into “something much than conscionable a database,” which is what everyone really wants successful nan property of AI. SQL is simply a 50-year-old technology, and its (re)adoption is not meant to beryllium a measurement back, astatine all. It is meant to beryllium a measurement forward. Postgres provides a unchangeable base, and nan worth is extracted connected nan different layers of its ecosystem.

Developer Velocity: The Hidden Driver of nan Shift

Performance and costs are easy to measure. Developer velocity is much challenging to quantify but arsenic important. AI improvement involves changeless iteration. Developers request accelerated feedback. AI agents request moreover faster feedback. Both request safe environments. Developers besides request a reliable measurement to trial schema changes and validate caller ideas connected existent information without fear.

I powerfully judge that managed databases were ne'er designed for developers to build modern applications connected them. They do not connection clone-based aliases branch-based workflows. They were designed to supply a unchangeable endpoint. Everything other happened extracurricular nan database. This increases nan spread betwixt codification changes and database changes and increases nan magnitude of information to beryllium transmitted betwixt nan database and application. It besides slows down nan feedback loop.

Modern Postgres platforms, specified arsenic Vela, Neon aliases Supabase, adjacent this gap. They springiness developers a elemental interface for creating branches, moving tests and merging changes. The database behaves for illustration portion of nan improvement process alternatively than a distant service. The consequence is faster loop and less surprises successful production.

Once teams acquisition this workflow, they commencement to mobility why they ever accepted a slower model. The effect connected merchandise cycles is measurable. Developers walk little clip waiting. They walk much clip building. They drawback issues earlier. They deploy pinch much confidence.

Velocity becomes a strategical advantage. The teams that tin trial and vessel faster summation much crushed each week. Postgres pinch branching and cloning supports this pace. It gives you nan information nett you ever wanted but could ne'er execute pinch manual processes.

So, Why Is Everything Moving Back to Postgres? 

After speaking pinch hundreds of teams and watching their infrastructure evolve, I judge nan displacement backmost to PostgreSQL is not a impermanent trend. It is simply a semipermanent people correction brought connected by nan demands of AI and modern exertion development.

Postgres has nan correct operation of features, maturity and extensibility. It useful for OLTP. It useful for OLAP astatine reasonable scales. It useful for vector search. It useful for clip series. It useful for real-time analytics. It useful for event-driven systems. In astir of nan cases, it conscionable useful for immoderate type of workload.

The problem was ne'er Postgres. The problem was nan situation successful which Postgres ran. Managed systems utilized designs that nary longer fresh nan needs of AI. BYOC platforms hole that. They harvester nan power of self-hosting pinch nan convenience of a managed service. They fto teams support their unreality relationship and their information posture while gaining high-performance Postgres pinch instant cloning and modern storage.

This exemplary brings Postgres backmost to nan halfway of nan architecture. It besides brings power backmost to nan teams who trust connected it. AI demands this level of control.

The caller stack is built astir Postgres successful your ain cloud, supported by a level that handles nan operational complexity.

This is why everyone is moving backmost to Postgres. It is nan due instauration for nan adjacent decade of AI applications. It gives teams nan flexibility, capacity and costs power they need. It lets developers build pinch confidence. And it simplifies nan full information scenery successful a measurement that matches nan velocity of modern development.

I judge this displacement has only conscionable begun. The adjacent procreation of AI platforms will not beryllium built connected a patchwork of specialized systems. They will beryllium built connected a unified information foundation. That instauration is Postgres. It will tally adjacent to compute. It will grip each workloads. And it will springiness teams complete power complete their astir important asset: their data.

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