CoreWeave Sandboxes: isolated execution for AI at scale
Agentic AI doesn't just produce outputs—it executes code. CoreWeave Sandboxes contains that execution at scale, without adding a separate stack to your infrastructure. See how.
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Hi my name is Deok
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I'm a product manager
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here at CoreWeave
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if you've been training agents
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running RL loops or model EVALs
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you've run into this challenge probably
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Code that came from the model
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has to run somewhere for you to verify it works well
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and you really don't want it touching your laptop
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or your training cluster that's what a sandbox is for
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an isolated environment spun up quickly
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that runs untrusted code human or model generated
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We're introducing CoreWeave Sandboxes
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there's basically two ways to use it
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You can use CoreWeave Managed Compute
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on a serverless option
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where you install the Weights and Biases
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SDK you log in and you can start running sandboxes
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there's no infra to set up and nothing to deploy
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There's also sandboxes for CKS and SUNK
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This infrastructure option is multi cluster by design
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it runs sandboxes on the clusters you already have
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on compute you're already paying for
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including idle CPU
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on the GPU nodes where you may be running SUNK
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which comes in very handy with capacity limitations
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Set up is easy for sandboxes through a simple CLI
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here you only need sandboxes
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admin IAM permissions
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and you create a profile that defines guardrails
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like networking policies for example
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to restrict ingress or internet egress
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you can also control namespace strategies
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and resource limits
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then you enable a runner on your cluster
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the runner is a CoreWeave managed workload
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which schedules each sandbox as a Kubernetes pod
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in your cluster
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Researchers in your org
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for example just do a pip install CW sandbox
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and they can immediately start running commands
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and code in sandboxes
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from their training scripts
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or whatever else they want to run it from
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from the SDK
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you can spin up thousands of sandboxes in parallel
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with a framework like veRL
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for example each rollout gets its own sandbox
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if you're running evals like SWE-bench
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which is a popular coding model benchmark
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you can spin up the hundreds of required containers
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and sandboxes to evaluate your model quickly
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So pick the path that
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best fits your needs
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and we can't wait to see
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what you build next
