Artificial intelligence has quickly become the cornerstone of digital innovation. From text generation to image recognition and intelligent automation, AI is redefining how organisations extract value from data.

At OVHcloud, we believe this transformation shouldn’t only belong to the tech elite – it should be open, accessible, and built on trusted, sovereign infrastructure.

This vision drives everything from our AI Endpoints and AI Deploy solutions to our Hugging Face partnership, which empowers developers to run open inference models directly in the cloud. But beyond those flagship initiatives, AI also lives in the everyday – in the data that powers recommendations, insights and smarter user experiences.

And that’s where PostgreSQL + Vector capabilities come in.

Vectors: Where data meets understanding

At its core, AI systems function by decoding relationships between words, images or user behaviours. To do that, machine learning models translate these entities into vectors — mathematical representations that capture meaning and similarity.

A vector representation allows a system to measure how close two pieces of data are. It is the foundation of semantic search, recommendation engines, facial recognition and anomaly detection systems.

Traditionally, companies needed to move their datasets from transactional databases into specialised “vector databases.” While vector databases are effective for purely vector-centric workloads, this approach often comes with higher complexity, data duplication, and integration overhead. These challenges are not ideal for production-grade systems that demand reliability and compliance.

PostgreSQL + pgvector: AI where your data already lives

Instead of creating yet another database to maintain, PostgreSQL offers an elegant solution: the pgvector extension. With pgvector, organisations can store, query and compare vectorised data alongside traditional relational data, using the same SQL syntax they already know. pgvector also allows you to build full or partial indexes to speed up similarity search.

In other words, PostgreSQL becomes not just your source of truth, but also your foundation for AI experimentation and delivery.

Here’s what this means in practice:

• Simplified architecture: Keep data in one place. No ETL pipelines or synchronisation risks.
• Familiar SQL workflow: Run similarity searches directly in SQL, with ACID guarantees intact.
• Faster time to value: Build and iterate AI use cases faster, without learning a new database technology.

This is AI grounded in operational reality — a pragmatic path for enterprises to explore machine learning use cases safely and efficiently.

A practical use case: Real-time product recommendations

Imagine an e-commerce company managing both product and customer data in Managed PostgreSQL at OVHcloud, ensuring access to the latest, most performant features.

By combining pgvector with embeddings generated from an open-source model, the team can:

1. Convert product descriptions and user preferences into vector representations.
2. Store these vectors in PostgreSQL columns alongside stock levels, pricing and metadata.
3. Run a similarity search that finds relevant products instantly: for example, recommending ‘eco-friendly alternatives’ or ‘similar styles’ while ensuring only in-stock items are shown.

The entire process happens within PostgreSQL — no need for external vector databases or data duplication.

The result: real-time, AI-enhanced customer experiences powered by trusted, open technology.

The enterprise reality: AI without reinventing the wheel

In the rush to ‘go AI’, many organisations risk overcomplicating their architectures by chasing the latest dedicated vector databases. While those solutions have their place, PostgreSQL’s maturity, ecosystem and extensibility make it uniquely suited for the vast majority of enterprise AI workloads.

For most companies exploring AI, starting with what they already know, PostgreSQL, means solid foundations, less risk, faster learning and lower cost.

The OVHcloud advantage: Open, managed, secure

OVHcloud’s partnership with Aiven, which brings proven expertise in managing PostgreSQL at scale, ensures our customers benefit from the latest capabilities as soon as they are production-ready, without operational difficulties. Let your teams focus on their product rather than worry about database resources and infrastructure.

Additionally, OVHcloud customers can benefit from a service-level agreement (SLA) of up to 99.99% via its Multi-Availability Zone (3-AZ) regions. These regions feature geographically separated zones with independent power, cooling and network systems, providing true fault isolation.

At OVHcloud, we see PostgreSQL as more than a database. It’s a bridge between today’s workloads and tomorrow’s intelligent systems. And as AI workloads evolve, we’ll continue to integrate the technologies that matter: from vector search and AI embeddings to seamless connections with AI Endpoints and Hugging Face models.