July 26, 2026
Shocking bills, zappy comments
Wattage: A token-spend profiler and cost-regression gate for AI agents
This new AI money meter has commenters joking, copying, and asking why it talks like a toaster
TLDR: Wattage is a new tool that shows where AI bots are wasting money and can stop pricier changes before they go live. Commenters were instantly charmed by the joke-y "Kill-A-Watt" pitch, while one person added classic tech-thread drama by saying they’d built a similar thing first.
A new tool called Wattage is pitching itself as the electric bill tracker for AI bots: feed it a record of what your AI agent did, and it points to exactly where the bot is wasting words, repeat work, and cash. It can even block new code if a change makes your AI helper more expensive. In plain English, it’s trying to stop companies from quietly burning money every time their chatbot rambles, repeats itself, or gets stuck in a loop.
But the real entertainment is the comment section, where the first reaction was basically: "Wait, I built this too." That instantly gives the launch a delicious whiff of indie tech drama — not hostile, just very online. Commenter bbor came in with the classic mix of praise and pain, saying they made something very similar and admitting, with a tiny dramatic bow, that this version is probably better. That’s the kind of comment developers read while smiling and silently screaming.
The biggest joke? The name and the framing. Calling it a "Kill-A-Watt" for AI absolutely landed, and people immediately latched onto the appliance-energy gag. Then came the wonderfully nerdy side quest: why measure this in watts instead of microjoules? Translation for normal people: the community instantly turned a cost-cutting tool into a naming debate, because of course they did. So yes, the product promises serious savings, but the vibe online is equal parts "this is useful," "I had this idea first," and "please explain your fake electricity metaphor."
Key Points
- •Wattage is presented as an offline tool that analyzes AI agent traces, prices token usage in dollar terms, identifies waste patterns, and can enforce cost regressions in CI.
- •The tool accepts OTLP JSON trace exports with no config file or API key and can generate either terminal reports or self-contained HTML flame graphs.
- •The article highlights Wattage's nonconvergence detector, which is designed to catch looping and stalled agent behavior that exact-match duplicate detection misses.
- •On a benchmark of 10 labeled synthetic loops, the article reports Wattage achieving precision 1.00, recall 1.00, and F1 1.00 versus a SHA-256 exact-match baseline with precision 1.00, recall 0.14, and F1 0.25.
- •The article says a fix simulation on a captured agent trace showed a 44.7% cost reduction, from $0.000199 to $0.000110, by enabling prompt caching on a stable prefix.