Most teams running AI can't answer basic questions about it: where the tokens go, which prompts and tools actually work, how the team really uses it. The data with the answers is huge, and most of it is never collected. Hyperparam captures it into storage you own and reads millions of rows straight from your bucket, so you can see where AI earns its tokens and why it fails.
The data that would answer these questions, every prompt, every tool call, every session, is scattered across laptops and services and mostly thrown away. Hyperparam records the actual traffic, so the questions you can't answer today become queries.
For decades the data we stored was rows and columns you analyze by aggregation, and a whole industry grew up around it: Postgres, Snowflake, Tableau, dashboards. AI logs are not that. Open one and you find a couple of structured columns and then one enormous text column where every cell is a whole agent conversation, a reasoning trace, a source file, and there are millions of them.
SQL can count the rows. It cannot read them. Understanding AI logs needs infrastructure that both scales with the volume and reads the text the way a person would. That is what Hyperparam is built for.
Hyperparam searches your logs by keyword, by meaning, and with SQL, and can run a language model over the rows that match. It all runs in your browser, straight against the files in your bucket, reading only the bytes each query needs.
Surface a failure pattern in Hyperparam, edit the prompt or skill in your repo, and the next week's traces measure whether it worked.
Answering those questions takes two things most teams are missing: the raw logs, and a way to read them at scale. HypAware captures every model call as it happens, and Hyperparam reads straight from where they land, so you go from collecting nothing to querying millions of sessions with no pipeline to run in between.
Capture every call your AI makes.
A lightweight collector you roll out to engineering laptops, run alongside production services, or place in front of OpenAI, Anthropic, or your own gateway. Every model call it sees is written as a row into open table files in storage you control.
Explore millions of traces with nothing to run.
No cluster to stand up, no SQL endpoint, no ingestion pipeline. Point Hyperparam at your bucket and filter, search, and cluster millions of traces interactively, right in the browser. Your credentials and your data never leave it; only the byte ranges a query needs are pulled over HTTPS.
2.3M traces, loaded in under a minute — filtered, clustered, and annotated with AI assistance. No login required.