And by viral I mean from $0 to $31.
Umami told me Clew Directive got 14 visits last month. AWS told me I owed $31 for it. That works out to $2.21 a visitor, which would make it the most expensive free learning-path tool in California.
Spoiler alert: 14 visitors, $31, and not a single one of them was the reason.
Something was off. Here is how Amazon Q, Claude, and a few hours of reading my own code untangled it. The app turned out to be innocent.
What Clew Directive is, quickly
A free, stateless tool that builds you a personalized AI learning-path PDF. You take a 60-second Vibe Check, four questions about your goals and how you learn, and it maps you to free, verified resources and hands you a briefing. No accounts, no database, no paywall, nothing stored about you. It runs on Amazon Nova, which is why it costs close to nothing to operate, which is also why a $31 bill made no sense.
The name is the Theseus kind of clew. A ball of thread to find your way out of the maze. Less hype, more direction. Live at clewdirective.com.
The number that didn't add up
Twelve visitors, 14 visits, 93% bounce, average session about a minute. Referrers from Bing, Google, Yahoo, GitHub. Visitors from the US, India, Netherlands, Egypt, Ethiopia, Singapore. Mostly crawlers stopping by to say hello.
A few curious humans and a parade of bots is not a $31 month. So either every visit was doing something enormous, or the bill was never about visits at all.
The dashboard lied, politely. An Amazon Q Story
My cost tracker said Clew Directive was running on Claude Sonnet. Sonnet is the expensive one. Case closed, right?
I opened the repo. Clew Directive does not run Sonnet. The Navigator agent runs Amazon Nova 2 Lite. Scout and Curator run Nova Micro. The IAM policy is scoped to Nova ARNs only, so a Sonnet call from these functions would come back AccessDenied. The app physically cannot bill Sonnet.
The math agreed. A full learning-path generation on Nova costs about two-tenths of a cent. Fourteen visits, even with the agents fanning out to a few calls each, rounds to lunch money. Nothing here gets you to $31.
One detail I want to flag, because it set the tone for the whole hunt. The same assistant, Q, that mislabeled the model also quoted me Haiku pricing at a quarter of the real rate. So here is the rule I kept coming back to: trust what a tool retrieves, verify what it remembers. Those are two different things.
Asking a better question
The question stopped being "why is my app so expensive" and became "what is actually spending, and why is it wearing my app's name."
Q pulled the breakdown. The month was 28 million tokens across only 8 active days, and two of those days did 70% of the work. May 24 and 25. Memorial Day weekend.
The shape of the cost was the real tell (Sonnet only):
- Cache writes: 4.1M tokens, $15.33 (55%)
- Cache reads: 23.8M tokens, $7.14 (26%)
- Output: 346K tokens, $5.20
- Input: 120K tokens, $0.36
The fingerprint
A web app serving 14 visitors does not look like that. Heavy cache write up front, heavy cache read after, almost no real input or output, is the signature of an agent reasoning over a big fixed context. It loads that context once, caches it, then re-reads it on every turn.
Clew Directive does no prompt caching at all. So whatever ran up the bill, it was an agent chewing on a large cached context, not an app answering users. Which pointed me at a very different project.
It was me, over a long weekend sprint
Clew Directive had zero commits on May 24 or 25. Last time I touched it was May 9.
A different repo lit up. vigil-crest. Created May 23, four commits on May 24. And I know exactly what it is, because I wrote a whole article about it and published it on, of course, May 24.
Vigil Crest is a challenge-triage agent I talk to on Telegram. It browses the live DEV challenge feed and tells me which hackathons are worth my time. Its stack, in my own published words: AWS Bedrock running Claude Sonnet 4.6, reached through an EC2 instance role so no credentials sit on the box, hosted on an always-on t3.micro. Read that back against what CloudTrail handed me: an assumed role, vigil-crest-bedrock-role, on an EC2 instance, calling claude-sonnet through the streaming API. (I am not pasting the full ARN. Account IDs stay home.)
Same project. Same box. Same model. The weekend I shipped it.
So the $28 was vigil-crest, on Sonnet, while I spent two days hammering on it before submission. Each triage run caches a fat context, the agent persona, the stack file, the rendered challenge feed, then re-reads it across turns. That is the cache-heavy shape, exactly. Whether it was the agent's own test runs or the tooling I built it with, both ran on that one EC2 box under that one role. Real work, priced correctly, just filed under the wrong project.
Q was Sherlock. Claude was the Watson who argued back.
I want to shine a light on how this got solved, because neither tool did it alone and neither did I.
Amazon Q in the console has one thing Claude does not: keys to the building. It reads my live account. CloudTrail, Cost Explorer, the actual deployed config, the IAM principal behind a single call. That is the force multiplier. I do not have CloudTrail memorized and I am not going to hand-read every IAM policy at 9pm. Q walked the crime scene and came back with the role name, the instance, the timestamp, the model, in minutes. That is the legwork no amount of reasoning replaces.
But access is not the same as the right conclusion. Q pulled clean evidence and attached the wrong story to it three times. It was a runaway process. It was your app. Check your application logs. Every pass, perfect data, wrong suspect. The evidence was never the problem. The narrative on top of it was.
Claude could not see my account at all. What it could do was refuse the easy story and push the evidence back through the actual code and the math. The repo says Nova. The IAM says AccessDenied for Sonnet. The token shape says agent, not app. It also said, out loud, that it could only see my commits and not my deployments, so part of this was inference and I should confirm it. A tool telling me where its own knowledge stops is worth more than one that sounds certain.
So Q was the detective with the magnifying glass on the real scene, and Claude was the Watson who kept asking does that actually follow. The twist is that in this case Watson is the one who pushed back on the detective. Sherlock had the keys. Watson had the doubt. I had to point them at each other and refuse to take the first confident sentence either one offered.
There is a tidy irony in here. Vigil Crest, the agent that ran up the bill, is built on exactly this idea: a verdict that knows how sure it is beats a confident guess. It hedges its calls on purpose. Solving its own bill came down to making my tools do the same thing, separate what they pulled from what they assumed. The agent's whole design philosophy is what cracked the case it caused.
What I learned, hopefully
The bill was never about traffic. Umami is client-side JavaScript. It counts browsers that run my script. It cannot see bots, it cannot see API calls, and it has nothing to do with Bedrock spend. I had tied two unrelated numbers together and scared myself.
The project label was a guess, not a fact. Bedrock charges are account-level. They do not inherit the tags of whatever called them. Unless you set up Application Inference Profiles and call those, every model dollar lands in a bucket marked "no project," and something has to claim it. Mine claimed Clew Directive by assumption.
The cost that bites you is the quiet one. The $31 sprint is over. June reads $0. The token burst was loud and self-limiting, it ended when I closed the laptop. The thing that does not end is the EC2 box under Vigil Crest, billing by the hour because it is meant to stay on. A small always-on t3.micro is cheap, but cheap and forgotten is how standing costs sneak up. Know what you keep running, and why.
The AI tools were useful and wrong in the same breath. Q read CloudTrail and Cost Explorer cleanly and narrated the wrong story three times, blaming my app on every pass. Claude caught the bad pricing and read the repos, and still had to admit it could see commits, not deployments. The actual work was pinning each claim to a source instead of to a confident sentence. Trust retrieval. Verify recall.
So no, Clew Directive did not go viral. It served 14 people and a crowd of crawlers and cost me almost nothing, which is exactly what it was built to do.
The bill was me, in a trench coat made of EC2, building the next thing.
Tell AWS. I want them to know it was me.
Clew Directive is free and open source. Find your way out of the AI-course maze at clewdirective.com.
AI Assisted. Human Approved. Powered by NLP.
