Granica AI

Start with the question that actually decides this: is your pain in the data layer or in agent execution? They are different buyers and Granica sells to both. Pick Crunch when you run petabyte-scale tabular lakes on Iceberg, Delta Lake, or Hive and your engineers keep queuing compaction, vacuum, and dedup jobs by hand. The pitch is not exotic — the numbers are what matter, and Granica publishes them against a named competitor. A $200K per PB annualized ROI comes from the storage and compute line, so the more you store and scan, the more the math works. Four weeks from kickoff to verified savings is a short enough pilot that we'd treat it as a paid experiment rather than a platform migration. Pass on Crunch if your estate is mostly unstructured or your lake is small. Tabular data is the scope, and there is a size threshold below which the return does not show up. Plenty of teams also already have a competent platform group that automates this inside Spark — Granica has to beat the scripts they wrote, not a blank slate. Myelin is the one we would watch hardest. The claim that matters is 95.6× context resumed from cache instead of rebuilt, with 4.2 billion tokens of agent work daily. If your agents burn budget re-reading the same files and re-litigating the same constraints after every handoff, that is a real line item, and the fix is architectural rather than a prompt tweak. It is also early infrastructure, so we would benchmark it against your own agent traces before trusting a headline ratio. The perimeter argument is Granica's strongest non-numeric selling point. Data stays in your cloud, the models trained on it stay there too, and Granica says it keeps no copy. Authority does not fork from a person to an agent — humans and agents work the same data under the