Migrating On-Prem SQL Server to Azure SQL PaaS: How We Cut Infrastructure Costs by 25%

Malavnaik — Thu, 15 Oct 2026 11:43:47 +0000

At S&P Global Market Intelligence I helped migrate on-premise SQL Server databases to Azure SQL PaaS. Our team moved between 10 and 50 databases to Azure SQL Managed Instance. The headline result was a reduction in infrastructure costs of about 25%. The more important result was that the people using those databases barely noticed it happened.

Here's what I learned about doing a migration like that well.

1. Inventory before you plan

Before choosing a target, list everything that touches the database: ETL jobs, reports, linked servers, SQL Agent jobs, cross-database queries, CLR assemblies. PaaS options don't support every on-prem feature in the same way, and you want to find the gaps on a spreadsheet, not in production.

Microsoft's Data Migration Assistant and Azure Migrate help here, but they don't know about the Excel workbook someone in finance refreshes every Monday. Talk to users.

2. Pick the right PaaS flavour

Azure SQL Database and Azure SQL Managed Instance trade compatibility against simplicity. Managed Instance keeps more instance-level features (Agent jobs, cross-database queries), so it suits lift-and-shift. Single databases are simpler and can be cheaper, but may need refactoring. Decide per workload, not per estate. We went with Managed Instance, which kept most instance-level features in place and meant fewer changes to existing code.

3. Right-size, don't copy

Most of the savings came from not paying for capacity we didn't use. On-prem servers are sized for peak load plus growth plus fear. In the cloud you can measure actual usage and scale. Look at real CPU, memory and IO over several weeks before choosing service tiers.

4. Migrate in waves

Start with low-risk databases: development, reporting copies, internal tools. Each wave teaches you something about networking, permissions or performance that makes the next wave smoother.

5. Test the boring things

Performance gets attention. In our migration, the real problems came from elsewhere:

Connection strings hard-coded in old applications and reports
SSIS packages and SQL Agent jobs that needed reworking to run against Azure
Jobs that relied on local file paths on the old servers

Also worth checking: time zone and collation differences, firewall rules and private endpoints.

6. Define "done" with the users

A migration isn't finished when the data moves. It's finished when every report, job and application works as before and the old servers are switched off. Agree that checklist with stakeholders up front.

The takeaway

Cloud migration savings come mostly from right-sizing and retiring infrastructure. Keeping users happy comes from inventory, testing and communication. Neither is glamorous, and both matter more than the migration tooling.

Hi, I'm Malav Naik: from Gujarat to Dublin, building AI systems and VEDASRA

Malavnaik — Wed, 14 Oct 2026 20:06:04 +0000

I'm Malav Naik, an AI systems builder based in Dublin, Ireland, and the creator of VEDASRA, an anime-style project. This is my first post here, so here's who I am, what I build, and what I'll be writing about.

From Gujarat to Dublin

I grew up in Gujarat, India, and studied for a Bachelor of Computer Applications at Veer Narmad South Gujarat University. My career started in data: for over five years I worked at S&P Global Market Intelligence as a Data Analyst & ETL Developer, building SQL pipelines, migrating databases to the cloud, and making sure business intelligence reports were built on data people could trust.

In September 2024 I moved to Dublin to do an MSc in Data Analytics at the National College of Ireland. I graduated in 2025 with a 2:1. My dissertation looked at how knowledge graphs and prompt engineering can support AI-driven longitudinal analytics: in short, giving language models structured facts to reason over instead of letting them guess.

What I build now

Today I'm a BIS Analyst / Applications Analyst & Developer at Trinity Care Nursing Homes in Dublin. I design AI-driven automation and analytics that support 13 nursing homes: consolidating operational reporting, integrating business systems, and testing an AI integration with Oracle NetSuite that lets authorised users ask financial questions in plain English, with role-based access and human approval for anything sensitive.

The lesson I keep relearning: AI is only as reliable as the data engineering underneath it. Most of the work is validation, audit logs, exception handling and deciding where a human should stay in the loop.

A project I'm proud of: SkillBot

During my MSc I built SkillBot, a skill-recommendation chatbot that combines a fine-tuned GPT-3.5 model, retrieval-augmented generation (RAG) and a knowledge graph of about 2.3 million RDF triples. Grounding answers in the graph got it to 97% factual accuracy in its reported evaluation and noticeably reduced hallucinations. I'll write a full breakdown soon.

VEDASRA

Outside my day job I'm building VEDASRA, an anime-style creative project, here in Dublin. More on that in a future post.

What I'll write about

Every week I'll share something real from my work:

How SkillBot combines RAG with a knowledge graph
What my dissertation taught me about prompt engineering
Lessons from five years of enterprise ETL
Designing AI automations that ask before they act
Behind the scenes of VEDASRA

If you're working on AI agents, RAG or data engineering, especially in Ireland, I'd love to connect.

Malav Naik · Dublin · creator of VEDASRA
Portfolio: malavnaik99.github.io · GitHub: malavnaik99

DEV Community: Malavnaik