Imported from jeremylongshore/tons-of-skills-marketplace (skills/.curated/coreweave-multi-env-setup/SKILL.md). Install upstream with npx skills add jeremylongshore/tons-of-skills-marketplace --skill coreweave-multi-env-setup. Copyright stays with the author (MIT).

CoreWeave Multi-Environment Setup

Community-contributed. Not affiliated with, endorsed by, or sponsored by CoreWeave, Inc. CoreWeave is a registered trademark of CoreWeave, Inc.

Overview

CoreWeave GPU cloud requires strict environment separation to control infrastructure costs and prevent resource contention. Each environment maps to an isolated Kubernetes namespace with its own GPU quota, scaling policy, and access controls. Development uses cheaper GPU tiers for iteration speed, staging mirrors production GPU types for accurate benchmarking, and production runs full-scale with no scale-to-zero to guarantee inference latency SLAs.

Environment Configuration

Prerequisites

• Separate, approved namespaces and service identities for development, staging, and production.
• Environment-specific secrets injected by a secrets manager, never committed .env files.
• A promotion owner, staging evaluation gate, and production rollback manifest.

Instructions

1. Define overlays that differ only in reviewed capacity, endpoint, and namespace values.
2. Validate required variables and secret references before applying an overlay.
3. Promote dev to staging, run the service evaluation, then use a controlled production rollout.
4. Roll back the production overlay on an SLO, quality, or security-gate failure; do not copy staging credentials into production.

const coreweaveConfig = (env: string) => ({
  development: {
    namespace: "app-dev", apiEndpoint: process.env.CW_API_ENDPOINT_DEV!,
    token: process.env.CW_TOKEN_DEV!, gpuType: "L40", scaleToZero: true, replicas: [0, 1],
  },
  staging: {
    namespace: "app-staging", apiEndpoint: process.env.CW_API_ENDPOINT_STG!,
    token: process.env.CW_TOKEN_STG!, gpuType: "A100_PCIE_40GB", scaleToZero: true, replicas: [0, 2],
  },
  production: {
    namespace: "app-prod", apiEndpoint: process.env.CW_API_ENDPOINT_PROD!,
    token: process.env.CW_TOKEN_PROD!, gpuType: "A100_PCIE_80GB", scaleToZero: false, replicas: [2, 10],
  },
}[env]);

Environment Files

# Per-env files: .env.development, .env.staging, .env.production
CW_API_ENDPOINT_{DEV|STG|PROD}=https://k8s.{ord1|ord1|las1}.coreweave.com
CW_TOKEN_{DEV|STG|PROD}=<service-account-token>
CW_NAMESPACE={app-dev|app-staging|app-prod}
CW_GPU_TYPE={L40|A100_PCIE_40GB|A100_PCIE_80GB}

Environment Validation

function validateCoreWeaveEnv(env: string): void {
  const required = ["CW_API_ENDPOINT", "CW_TOKEN", "CW_NAMESPACE", "CW_GPU_TYPE"];
  const suffix = { development: "_DEV", staging: "_STG", production: "_PROD" }[env];
  const missing = required
    .map((k) => (k.includes("NAMESPACE") ? k : `${k}${suffix}`))
    .filter((k) => !process.env[k]);
  if (missing.length) throw new Error(`Missing env vars for ${env}: ${missing.join(", ")}`);
}

Promotion Workflow

# 1. Validate model in dev namespace
kubectl -n app-dev get inferenceservice my-model -o jsonpath='{.status.conditions}'

# 2. Apply staging overlay with production GPU type
kustomize build k8s/overlays/staging | kubectl apply -f -

# 3. Run inference benchmarks against staging endpoint
curl -X POST https://staging.myapp.coreweave.cloud/v1/predict -d @test-payload.json

# 4. Promote to production (blue-green via namespace switch)
kustomize build k8s/overlays/prod | kubectl apply -f -
kubectl -n app-prod rollout status deployment/my-model

Environment Matrix

Error Handling

Output

• Environment-isolated manifests, identities, and quotas with a documented promotion path.
• A redacted validation and rollout receipt for each environment.
• A production rollback path that preserves the prior known-good revision.

Examples

Validate a staging overlay server-side before rollout, then wait for its deployment:

kustomize build k8s/overlays/staging | kubectl apply --dry-run=server -f -
kustomize build k8s/overlays/staging | kubectl apply -f -
kubectl -n app-staging rollout status deployment/my-model --timeout=10m

If the staging result fails its signed gate, stop promotion and restore the prior staging revision. Keep credentials out of terminal history, logs, and overlay files.

Resources

• CoreWeave CKS

Next Steps

See coreweave-deploy-integration.