I built a tool to compare cloud and AI costs, because I couldn’t trust the numbers anymore

If you’ve ever worked with cloud infrastructure, you probably had this feeling: “Are we overpaying for this?” Now add AI into the mix — OpenAI, Anthropic, Gemini — and it gets even harder. The prob...

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I built a tool to compare cloud and AI costs, because I couldn’t trust the numbers anymore

Source: DEV Community

If you’ve ever worked with cloud infrastructure, you probably had this feeling: “Are we overpaying for this?” Now add AI into the mix — OpenAI, Anthropic, Gemini — and it gets even harder. The problem Most tools today focus on what you already spent. But that’s not the real question. The real questions are: What would this workload cost on another cloud provider? Is AWS really more expensive than OCI for this setup? How much more am I paying by choosing one LLM over another? What happens to my cost when I scale this? Surprisingly, it’s still hard to answer these quickly. What I built I created a simple tool called: 👉 https://costifyhq.com The idea is straightforward: Input a workload (CPU, RAM, storage, traffic) Or simulate AI usage (tokens, requests) Instantly compare costs across: AWS, Azure, GCP, OCI OpenAI, Anthropic, Gemini And get a quick answer like: “You could save ~25% by switching providers” Why this approach I didn’t want another dashboard. I wanted something that answers:

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