AI Adoption Without New Infrastructure

AI is moving faster than any enterprise technology wave we’ve seen in decades. Every board conversation now includes AI readiness, every roadmap includes AI integration, and every engineering leader is under pressure to make it happen yesterday.

But I see the same question come up again and again: Do we need to rebuild everything to adopt AI effectively? Where do we start?

The truth is simple. You can adopt AI without new infrastructure. In this article, I’ll guide you through how to think about smarter data infrastructure for AI workloads. One that lets you move fast, stay compliant, and control costs without starting over.

The AI Infrastructure Paradox

Over the past two years, the global AI conversation has become louder and more expensive. Analysts at Goldman Sachs recently published a report called “The AI Spending Boom Is Not Too Big.” In it, they show two things that matter deeply for how we think about infrastructure.

On the left side of their chart, the number of companies deploying AI is exploding. On the right, the market share of a few leaders, such as Nvidia, TSMC, OpenAI, continues to concentrate.

When I look at that, I don’t see risks. I see opportunities. It means the heavy lifting of AI infrastructure is already done for us. The global compute layer (GPUs, accelerators, and model APIs) is mature and accessible. We don’t need to wait for NVIDIA to be disrupted. We don’t have to compete with hyperscalers to participate in AI.

That’s why I believe the next phase of AI infrastructure isn’t about building bigger clusters. It’s about making data systems smarter, more connected, and more adaptive. At least for the vast majority of enterprises.

How SQL Became the Foundation of AI Infrastructure

Our CTO, Michael Schmidt, recently wrote about the history of SQL and why it still rules the data world in 2025. That evolution is accelerating again. This time because of AI.

SQL used to be where you stored facts. Now it’s where you derive intelligence. Modern SQL systems can handle real-time analytics, vector embeddings, and hybrid workloads that serve both transactional and analytical queries.

If you think about it, this is a quiet revolution in AI infrastructure. It means we don’t have to abandon relational systems to build intelligent applications. Instead, we can extend them. SQL has become the connective tissue between traditional business data and AI-driven insights.

The problem is that while the SQL layer has evolved, the infrastructure underneath it hasn’t caught up. Traditional databases weren’t designed for continuous model feedback, retraining cycles, or the parallelism AI requires. They struggle to deliver the performance and elasticity that AI workloads demand.

That’s the missing piece in most enterprise AI strategies. Not the model, but the infrastructure connecting the model to the data.

The Integration Gap Between Data and AI Models

Right now, most AI teams live in two worlds. The model world, optimized for iteration and experimentation. And the data world, optimized for durability and governance. The gap between them creates friction.

In almost every organization we work with, the data and AI worlds still live on opposite sides of a wall. On one side, there’s the core business data usually running on something like Amazon RDS, Cloud SQL, or Azure Database for PostgreSQL. These systems hold the operational truth of the business: customers, transactions, usage, telemetry, logs. They’re durable, trusted, and tightly governed.

On the other side sit the AI and ML platforms like Vertex AI, SageMaker, Databricks, or a growing ecosystem of open-source tools. They’re optimized for experimentation, retraining, and feature engineering. Data scientists love them because they’re flexible. But that flexibility often comes at a price: distance from the production data they actually need.

Between Data and AI/ML Worlds

What happens in between is the real infrastructure problem. Data has to be exported, transformed, and copied into separate storage before models can use it. Pipelines break whenever schemas change. Retraining depends on last night’s snapshot instead of real-time data. Inference pipelines, in turn, end up depending on stale or incomplete datasets. I see this everywhere: from fintech firms trying to score transactions in real time to SaaS platforms building AI copilots that need the latest customer context.

This pattern slows everyone down. Engineers spend their days maintaining sync jobs between RDS and object stores. Data scientists burn weeks rebuilding feature sets that already exist elsewhere. Platform teams juggle permissions, duplication, and compliance reviews across two parallel worlds.

That gap isn’t about tooling; it’s architectural. The core data layer and the AI layer were never designed to speak the same language. The relational database was built for consistency and safety. The AI stack was built for scale and iteration.

Closing that gap is the next frontier of AI infrastructure. We need systems where the production database can safely expose data to models without full replication. Where SQL and Python coexist natively. Where data can flow continuously between operational tables and model features without brittle pipelines.

The database needs to become the place where data and intelligence meet. A foundation that bridges the gap rather than widening it.

Why Scalability and Performance Are the New AI Infrastructure Bottlenecks

Every AI system eventually runs into the same limits: data access, latency, and scalability. Models are only as powerful as the infrastructure feeding them.

If your database can’t scale in milliseconds, your model’s predictions won’t be real-time. If your data pipelines rely on nightly batch jobs, your AI insights will always lag behind business reality.

This is why scalability and performance have quietly become the defining characteristics of modern AI applications infrastructure. AI workloads are unpredictable as they spike during inference, taper during training, and fluctuate across time zones and applications. Legacy systems built on fixed, provisioned instances can’t adapt to that rhythm.

What we need are infrastructure layers that can branch, clone, and scale dynamically. We need data platforms that can serve both OLTP and OLAP workloads in the same environment, without rearchitecture. And we need tight integration between those systems and AI toolchains so the data that powers your business is immediately available to the models that learn from it.

That’s what I mean when I say smarter infrastructure. Not more servers. More intelligence inside the infrastructure itself.

From Thought to Execution: Building AI Infrastructure Intelligently

When I talk to CTOs and CIOs, the conversation often shifts from ambition to realism. Everyone wants to integrate AI. Few want to re-architect their entire tech stack to do it.

The smartest organizations are starting from where they are. They’re modernizing incrementally, introducing data versioning, instant cloning, and compute elasticity step by step. They’re adopting platforms that extend their current databases rather than replacing them.

That approach—modernization instead of reinvention—is how enterprises will win this decade of AI.

At simplyblock, this philosophy shaped our approach when building Vela. We built on Postgres because it already powers the world’s data. Then we extended it with the scalability, performance, and automation AI infrastructure demands. Instant branching, compute decoupling, and BYOC flexibility aren’t gimmicks. They’re enablers of this smarter, connected future.

But the message isn’t about one platform. It’s about a mindset: the belief that AI success doesn’t come from new infrastructure. It comes from smarter use of the infrastructure you already have.

Where to Start: A Practical Path Toward Smarter AI Infrastructure

When leaders ask me where to start, I always tell them: begin with the database you already have. Most of the organizations I talk to already run Postgres, MySQL in some managed version like Amazon RDS. That’s your foundation. You don’t need to replace it. You need to make it more adaptable to start creating an AI backend.

The first step is bridging your operational data with your AI systems. Instead of exporting tables nightly into data lakes or ML platforms, look for ways to make your production data safely accessible in real time. Modern Postgres extensions like pgvector, or database platforms that offer instant branching and cloning, make it possible to create isolated AI environments without touching production.

If you’re using Vertex AI, SageMaker, or Databricks, connect them directly to your database layer. The goal is to reduce duplication and latency while keeping governance intact. You want a single, living data layer that your models can query, learn from, and feed back into without the constant rebuild cycle.

For teams further along, think about modularity and control. Choose architectures that decouple compute from storage, allow dynamic scaling, and support in-database versioning. That’s the direction the industry is already moving in and it’s the mindset we built into Vela.

You don’t have to rebuild your stack to adopt AI. You just need a database platform smart enough to evolve with you. Start by modernizing the data foundation. The rest of the AI infrastructure will follow naturally.

The Future of AI Infrastructure Is Already Here

If I look ahead, the line between database and AI platform will keep fading. Databases will understand vectors natively. They’ll support model inference as part of query execution. They’ll scale horizontally without human intervention.

In that future, AI infrastructure won’t be a separate system. It’ll be an evolution of what we’ve been building for decades.

And if you start rethinking your data layer now, you won’t need to rebuild for AI later. You’ll already be running on infrastructure intelligent enough to evolve with it. Start with Vela.

Topics

Share blog post