(Disclaimer: blog author and Timescale employee) There are two main points here. 1. To complete this benchmark workload, it took less than an hour in two diff

(Disclaimer: blog author and Timescale employee) There are two main points here. 1. To complete this benchmark workload, it took less than an hour in two different environments (Digital Ocean self-managed & Timescale Forge fully managed) to ingest 1 billion metrics and run all 30K queries. It took us a week of work (testing, modifying code to try and make Timestream better) to get 40% of the metrics into Timestream and then query it. 1 hour vs 7 days. 2. If you look at the bill/costs, the main driver was querying. We (attempted) to run the same 30K queries on less than half the data (410 million metrics) in Timestream and somehow scanned 21TB of data. I have no idea why and there's nothing we could do to change it. As a developer, that's going to be your biggest unknown. If you're ingesting millions or billions of metrics a day and querying it with a real application, you could really get hit with crazy query costs. With a more traditional server architecture, at least you know your day-to-day costs and can set a known capacity to achieve the performance you need (or scale in understandable ways when you need it)