Modernizing PostgreSQL for AI Workloads Using Azure

Background

A growing company was using on-premises PostgreSQL for many years. As the company expanded, they wanted to build:

• AI chatbots

• Document summarization tools

• Fraud detection

• Recommendation engines

But their old database system was slow, hard to manage, and not ready for AI, as an Azure SRE how would you solve this. Below is a case study that I have prepared for better understanding.

 The Problem

The company faced five major issues:

1. Performance problems

Data was growing fast, queries were slow, and systems lagged during peak hours.

2. Too much manual work

DBA/SRE team spent a lot of time on:

• Patching

• Backups

• Scaling

• Failover

• Monitoring

This reduced focus on innovation.

3. No support for AI features

The existing PostgreSQL system did not support:

• Vector search

• Embeddings

• RAG

• AI model integration

So AI projects couldn’t even start.

4. Security and compliance risks

No identity-based access, old security patches, and hardware limits increased risks.

5. No easy scaling

To scale, they had to buy hardware → wait weeks → schedule downtime.

Objective

As an SRE, we would define clearer goals:

• Make the database faster and more reliable

• Reduce manual tasks and automate processes

• Add support for AI search, LLMs, and embeddings

• Improve security: encryption, identity login, policies

• Enable on-demand scaling

• Reduce costs of running on-prem hardware

Proposed Solution:

We should migrate the entire workload to Azure Database for PostgreSQL – AI-ready version.

This will give us:

• Fully managed database

• No manual patching or backups.

• Built-in AI tools

  
  

• pgvector → vector search

• azure_ai → generate embeddings inside SQL

• Semantic operators → better search accuracy

• DiskANN → super-fast vector queries

• Apache AGE → graph-based retrieval (GraphRAG)

  
  

• High availability

  • Zero data loss failover, automatic replication.

• Security

  • Entra ID login, encryption, managed keys.

    
    

• Developer productivity

  • VS Code + GitHub Copilot support.

• Cloud scaling

  • Scale up or down in seconds.

This is a major upgrade.

How to Implement It

Step 1 — Assessment

• We measured performance, downtime, and defined reliability targets.

  
  

Step 2 — Migration

• Using Azure DMS, we moved data with minimal downtime.

  
  

Step 3 — Enable AI features

• Turned on pgvector, azure_ai, DiskANN, and graph support.

  
  

Step 4 — Reliability setup

• Automatic backups, geo-redundancy, failover testing.

  
  

Step 5 — Monitoring

• Azure Monitor + Log Analytics for performance insights.

  
  

Step 6 — Cost optimization

• Right-sizing and auto-scaling policies.

Results

Operational Improvements

• 80% less manual work

• Patching/scaling completely automated

• Failover time reduced from hours → minutes

Performance Improvements

• Query latency reduced by ~50%

• Vector search became 10x faster

• No more slowdowns during peak load

AI Enablement

The company will quickly build:

• “Chat with documents” internal assistant

• Fraud detection using graph relationships

• Recommendation engine

• Document summarization workflows

  
  

Cost Savings

• No more buying hardware

• Scaled only when needed

• 25–30% reduction in total cost

  
  

What I Learn as an SRE

1. AI needs a modern database

You can’t run AI on old systems. You need vector search, embeddings, and fast retrieval.

2. Reliability must be built, not assumed

Cloud features like HA, geo-replication, and automated failover change everything.

3. Automation frees teams to innovate

When patching and backups are automated, SREs can focus on solving real problems.

4. Security improves when identity replaces passwords

Entra ID makes systems cleaner and safer.

5. Cloud PostgreSQL with built-in AI is a modern blessing

Instead of connecting 10 different systems, everything runs inside one database.