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Overview
Keep logs, metrics, and traces on object storage you own and search all of it in seconds. Use CtrlB directly or with your existing tools.
News
Your Observability Stack Was Built for Humans. AI Agents Don't Work That Way.
A developer asks an AI coding agent, "Why did checkout errors spike at 14:20?" The agent doesn't run one query and wait for an answer. It checks the checkout logs. Then traces. Then a downstream service. Then the deployment history. Then the database erro
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A faster alternative to DuckDB
DuckDB set the bar for querying Parquet on one machine. We put CtrlB next to it on ClickBench, same hardware, same file, same 43 queries. CtrlB is ahead on the hot run on both instances we tested, and the gap sits somewhere specific. Why we benchmarked ag
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Your LLM doesn't need to read the same log line 900,000 times
ctrlb-decompose separates what repeats in your logs from what actually changes. This is the math, the single-pass algorithm, and the type-aware encoding behind it. Problem: More logs in, worse answers out Past a certain point, pasting more logs into an LL
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CtrlB tops ClickBench: Analytical Search and Full-Text Search
Incident work is narrow and impatient. You look up one trace identifier, match one error substring, or isolate a single short-lived container session, and the answer has to come back in milliseconds. Dashboards and capacity reviews ask for the opposite: s
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One unified data lake vs. a fragmented stack
The Grafana LGTM stack (Loki, Mimir, Tempo) spreads every signal across separate backends you must wire together and tune. CtrlB takes the opposite bet: one pipeline, one query language, one open storage format. Here's a point-by-point comparison of why t
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