Pebble Falcon

@alphabetatheta Hi Vishal! Great question,
Partially it depends who you are and what you need. For general cloud operations, a big part is we unify cloud, on-prem, and hybrid environments, doesn’t matter where your workloads run, we unify that data and optimize across all of it. Unlike a lot of tools that are locked into one or the other, Pebble brings it all together.

Our AI agents actually take action autonomously (with guardrails to prevent issues) and use a proactive, predictive approach. For example, if your workload dips at a certain time of day, we automatically down-throttle with a margin of safety and forecast usage so if demand spikes, we catch it and reprovision fast, no latency hits, SLAs intact. Got long-running batch jobs that aren’t urgent?

We’ll shift them to the best time zones and periods for lower power and carbon output, tuned for your priorities. Whether it’s cloud or local infra, Pebble adapts: maybe you care most about carbon in the cloud, or power cost on-prem, we flex with your focus.

If you’re an AI-driven org, we go even deeper. We’re building agent tools that directly optimize distributed PyTorch training, same philosophy, but now we’re adjusting power, compute, and timing across unstable, high-variance pipelines. So you get optimal training costs, minimized power draw, and reduced carbon, all without missing your delivery timeline. (I’m personally pretty partial to our PyTorch training optimizer, it’s killer.)

All of this while being totally secure, run in your cloud, no exfiltration of data, and you can enable either human-in-the-loop or full autonomous mode.

TLDR:

Pebble unifies cloud and on-prem optimization, takes secure autonomous actions (not just recommendations), and tunes for cost, carbon, or energy, across any environment, all with human or AI-in-the-loop.