Rocketgraph – AI-Powered Log Compression & Debugging

An observability tool that uses ML to condense billions of log lines into tiny snapshots that LLMs can efficiently debug. Solves the problem of AI-generated code requiring different debugging approaches than human-written code.

Observability costs are blowing up as AI-generated code ships faster than teams can instrument it, and log volumes are scaling in ways that make token-limit-constrained LLM debugging genuinely painful — that tension is real and growing. The closest incumbent is Datadog, which dominates log management but treats compression and LLM-readiness as an afterthought rather than a core design principle, leaving real whitespace at the "pipe logs into an AI debugger" layer. The $10k–50k/mo band is plausible if the ICP is mid-size engineering teams already paying Datadog or Grafana and willing to pay a specialist tool on top, though that stacked-spend tolerance has limits. The biggest risk is that the hyperscalers — Datadog, New Relic, Grafana Cloud — ship "AI log summarization" as a checkbox feature within 12–18 months, commoditizing the core value prop before a standalone player can build sufficient switching costs.