The more cores, the higher your licensing costs. I do need more compute power, what’s next? Do your research and choose the most appropriate instance type, not just next the one up. Try not to rush a decision, there are so many instance types and sizes to choose from, each one optimised for a different use case. If you can hold off scaling your instance to give you time to fix your expensive queries first, I would recommend it. In my experience, this rarely happens and they just accept the new instance size as normal. Some people will choose to scale up and then plan fix their queries, with the ambition to scale back down again later. Scaling up is one option, improving your queries is another. If your CPU is regularly pinned above 85%, you need to take action. Cutting CPUs will give you the biggest financial saving in Azure due to the way that licensing works. In an ideal world, you should run load tests against the old and new instance types, and compare them before making a decision. If your CPU is always below 50%, including spikes and you haven’t identified memory pressure, you can look at scaling down. You should be collecting metrics on CPU and memory, as well as looking at your throughput and disk latencies to see how well you are sized. Unlike days gone by where you would have a bought a giant SQL Server and ran about 10% utilisation (just to allow room for future growth), you can be much smarter now and only pay for what you need. One great thing about the cloud is that you can change your mind, or adjust as you go along. To avoid repetition of my my post regarding the size of your Azure SQL DBs, please read that to understand the concepts of elasticity, scalability and CAPEX vs OPEX. Cost optimisation is crucial to any organisation that operates in the cloud, as costs can and do run away from away you without regular attention. This is post #4 in my series on 7 ways for data teams to save money in Azure.
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