Semantr reads the Bedrock invocation logs you already have, groups the invocations back into conversations, and works out what kinds of work your organisation is actually doing with AI.
curl -LsSf https://semantr.com/install.sh | sh
Then run semantr. It finds your invocation logs on AWS, prices the run, and analyses them.
$ semantr semantr: looking for your invocation logs found invocation logs at s3://acme-bedrock-logs/AWSLogs/ (ap-southeast-2) Semantr will read those logs, rebuild the conversations, and name the kinds of work it finds. Continue? [Y/n] y sessionise 2,761 invocations → 1,007 conversations discover 221 distinct prompts → 21 named use cases aggregate $2,145.30 total spend analysed report semantr-report.html
Sign in once, with Google, so we can fund the model that names what it finds. Your account also keeps history, trends and shareable links.
Every discovered use case, named from its own contents, with its own spend, conversations and cost improvements attached.
Example report output:
| Use case | Tags | Conv | Spend |
|---|---|---|---|
| Customer Support Responses | non-coding repeatable | 206 | $1,204.60 |
| Invoice Data Extraction | non-coding repeatable | 238 | $58.30 |
| Pull Request Review | coding repeatable | 131 | $14.90 |
| Churn Analysis Queries | coding experimental | 44 | $616.40 |
| Database Script Development | coding experimental | 34 | $18.20 |
Every use case is tagged coding vs non-coding, and repeatable vs experimental — so you can tell, at a glance, how much of your spend is automation running in production and how much is people still trying things out.
Model right-sizing, prompt caching, retry loops, untagged spend: each with the annual saving worked out, so it is a decision rather than a suggestion.
Cost dashboards report spend by team, key and model — an accounting answer to a question nobody actually asked. What people want to know is what the money bought.
"$40k on Bedrock last quarter" is a number, not an answer. Spend by API key does not tell anyone whether the money bought customer support or someone's side project.
Every team knows what they built. No one knows what the other eleven teams built, and the list nobody maintains is always out of date.
Classifying every invocation with an LLM costs tens of thousands of dollars at real volume. Which is why nobody does it, and why nobody knows.
The naming step runs about 150 model calls, whether your corpus is a million invocations or fifty million — which is what keeps this in cents, not tens of thousands of dollars.
Bedrock logs are stateless. A twenty-turn conversation is twenty unrelated records. But they are self-describing: each invocation contains the one before it. That makes rebuilding conversations an exact join, not a guess.
2,761 invocations → 1,007 conversations
Similar conversations are grouped, and each group is named from its own contents. There is no fixed taxonomy and therefore no other bucket. The categories come from what your organisation is actually doing, including the things nobody thought to ask about.
1,007 conversations → 221 distinct prompts → 21 named use cases
Spend, tokens and time roll up against each discovered use case, with the cost improvements that follow from them. Cost per conversation, not per request: a twenty-turn session is one unit of work.
$2,145.30 across 21 use cases · analysis cost $0.03
Security & privacy
Bedrock invocation logs contain full prompts and completions — the most sensitive data class a company produces. This is not a footnote. Every design decision below follows from taking that seriously, starting with what is and isn't allowed to leave the machine it runs on.
Naming is the only step that calls a model, and it only ever sees prompt snippets: system prompt, tool names, first turn, each capped at 600 characters. By default that runs on Semantr's hosted inference, against your account. Point --classifier-model at your own provider, including something fully local, and that path is never touched.
Every run keeps a copy of its aggregates, counts, cost, and the names it discovered, so the report has a home and a working share link, with or without an account. Prompt text is stripped before anything is sent, and the egress schema is published in the README rather than buried in a policy.
The CLI, the pipeline and the report are Apache 2.0. Read the source, or run the whole thing yourself, and nothing you analyse has to leave your account at all.
One run tells you what your organisation is using AI for right now. Signed in, every run adds to that picture instead of replacing it.
Run it again next month and see exactly what moved: use cases that appeared, spend that shifted between teams, adoption that grew or quietly stalled.
Runs are kept against your organisation, not against whoever happened to run the CLI — so the picture survives someone leaving the team, and a shared link stays live for whoever needs it next.
The CLI, the pipeline and the report are Apache 2.0 — free to read, run and change.
Running Semantr costs you a few cents of inference per run, paid to your own provider. We do not mark that up and we never see it.