Been burning through API credits way faster than expected lately. The worst part, I had no idea where it was all going until I looked at my bill at the end of the week. The breaking point for me was a debugging session where I was iterating on a prompt, running it 10 times, trying to get the output format right. Didn’t realize I was sending the full context window every single time. That one hour cost me more than an entire day of normal usage. Started using Lumen after that. It’s a free open-source proxy that sits between your tools and the API and shows you live token rates, cost per call, cache hits, basically Activity Monitor but for your LLM spend. macOS gets a native menu bar app, Linux/Windows get a browser dashboard. Nothing is sent to the cloud, and content is never stored. The first thing I noticed was how much I was bleeding on output tokens during iteration. Completely changed how I structure my prompts and when I actually run full completions vs quick tests. After getting visibility into where the spend was actually going, I looked into DataGrout. They have an LLM cost optimization layer (semantic caching, context compression, inference routing) that’s been quietly cutting my costs further without me changing much code. Anyway, if you’re not measuring your token spend yet, start there. You can’t optimize what you can’t see. submitted by /u/Mavericks_poker
Originally posted by u/Mavericks_poker on r/ClaudeCode
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