The use case: how Trajectory deploys Daytona
Trajectory uses Daytona's sandbox infrastructure in its reinforcement learning post-training pipelines, provisioning large numbers of isolated, stateful sandboxes in parallel where AI agents train on replicas of customers' tools and systems. Snapshots and a warm pool are used to pre-configure environments, and lifecycle controls tear sandboxes down automatically.
More reported results
Figures published in Daytona’s case study. They are not independently measured by usedby.
700k sandboxes provisioned with Daytona
15+ hours of engineering time saved monthly
In the customer’s words
Daytona lets us spin up environments that used to take up to 30 minutes in under five seconds. And we can do that for thousands of sandboxes at once, which is what makes efficient RL post-training possible.Other AI tools used by Trajectory
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Explore Trajectory’s AI stack
Other companies using Daytona
Customers across industries, with public evidence.
AfterQueryConfirmed
AsteroidConfirmed
BenchFlowConfirmed
CapyConfirmed
HandshakeConfirmed
Laude InstituteConfirmedGo straight to the source
Based on Daytona’s published case study. We check the public evidence; the results are reported by the vendor.
daytona.io- Last checked
- Oct 27, 2026
Open source capture What the story claims, and what we checked
What we compared with the page.
- The figure: 99.6%Checked
Printed word for word on the page, near the name of Trajectory.
- The passage quoted aboveChecked
Copied word for word from the page, near the name of Trajectory.
- Trajectory uses DaytonaChecked
Confirmed line.
- The result itselfNot checked
We quote it; we did not measure it.
Combined with Daytona’s warm pool of snapshot-based sandboxes, startup times have dropped from 30 minutes to seconds. That speed enables Trajectory to run RL training loops at the pace their pipeline demands, accelerating model iteration and increasing training throughput.