Relational Databases Are Dead

Why ClickHouse will win in the era of AI-centric apps

Why Relational Databases Are Obsolete in the Age of AI

Relational databases are dead — and ClickHouse is poised to win in this era of AI-centric apps.

Limitations of Relational Databases for AI Workloads

The knock on ClickHouse has always been: “but it’s not relational.” People cite the lack of foreign keys, row-level updates, and the absence of traditional ACID transactions. However, relational databases are rapidly going out of style — primarily because artificial intelligence can infer complex data relationships in a far more meaningful, flexible way than any rigid schema.

How AI Surpasses Relational Data Modeling

With relational databases, a foreign key is a human manually declaring “these two things are connected.” But an LLM-powered system can dynamically discover those connections — and hundreds more you never thought to model — spanning unstructured logs, embeddings, and event streams. The value proposition of a relational schema was always based on encoding human knowledge about data relationships into structure. Modern AI does this better, faster, and without the brittleness of static schemas.

ClickHouse vs. Postgres for AI-Centric Applications

When your AI layer can semantically join a support transcript to a usage pattern and correlate it with a churn signal — all without a single SQL JOIN statement — what does Postgres offer that ClickHouse can’t?

The so-called “gaps” in ClickHouse aren’t shortcomings; they’re deliberate design decisions for the AI era.

• No row-level updates? GenAI and LLM applications are append-only by design.
• No foreign keys? Your AI layer discovers relationships traditional schemas never could.
• No traditional transactions? Event-driven architectures prioritize throughput over locking.

ClickHouse: Optimized for Modern AI Data

What does ClickHouse deliver?

• Sub-second queries over billions of rows.
• 10–20x compression on AI telemetry.
• Columnar ingestion purpose-built for modern AI workloads: inference logs, vector metadata, feature stores, observability traces.

Why Postgres Falls Short for AI Workloads

Postgres earned its reputation solving last-generation challenges: normalized data, static schemas, human-defined relationships, and moderate write volumes. Today’s AI-centric applications have fundamentally different needs: massive data ingest, blazing analytical speed, and cost efficiency at volumes that bring traditional relational databases to their knees.

The Future: Intelligence Over Structure

Stop forcing AI workloads into a relational database box. In the AI era, value lies in the intelligence layer — where relationships and insights are dynamically discovered and leveraged by your applications, not proscribed by your schema.