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Run ML/AI or any type of workload with optimal performance and infrastructure cost. OptScale allows ML teams to multiply the number of ML/AI experiments running in parallel while efficiently managing and minimizing costs associated with cloud and infrastructure resources. OptScale MLOps capabilities include ML model leaderboards, performance bottleneck identification and optimization, bulk run of ML/AI experiments, experiment tracking, and more.

...Quick cost estimate: write a code block and get a cost estimate without having to use AWS, Azure or Google cost calculators, or read the long/complicated pricing web pages. Catch costly typos: if you accidentally type 22 instead of 2 as the instance count, or 1000GB volume size instead of 100, the cost estimate will immediately pick that up and let you know. See cost estimates right above their Terraform definitions. Infracost's output updates on file save. Both resource and module blocks are supported. 3rd party module blocks are also supported!

...The site aggregates key metrics — including CPU, memory, storage, networking specs, and pricing — across many instance families, and lets users filter, sort, and visually compare configurations side by side. It was created to simplify decision-making for cloud provisioning, making it easier to choose the best instance type for performance and cost goals, and it’s maintained by the Vantage community to stay updated with current AWS offerings and user suggestions. Contributors use automated scrapers and workflows in the repository to fetch, update, and curate the underlying data that powers the comparison interface, and the site itself supports API access for programmatic integration into tools or dashboards.