Beam vs CoreWeave

Beam

Per-millisecond billing with cold starts and image pulls free, H100 PCIe at $3.50 and RTX 4090 at $0.69 an hour. No published request rate limits, 429 handling or SLA.

CoreWeave

Per-hour prices for 8-GPU H100, H200 and B200 nodes are public, the docs ship as Markdown with llms.txt and embedded OpenAPI specs, and IAM has read-only roles per service. An organisation must be approved by CoreWeave's sales team before any token exists, and no request rate limits were found in the reviewed documentation.

Beam

1. Check the response body for ok: false on gateway calls; a failure can arrive as HTTP 200
2. Don't pipe beam deploy --format json output into CI logs, since it contains the workspace token
3. Route anything over 180 seconds to a task queue and poll the task instead of holding the endpoint request
4. Set keep_warm_seconds deliberately; the 180-second endpoint default bills three minutes of GPU after every call
5. Pass gpu=["RTX4090", "A10G"] so a job still schedules when one type is out

CoreWeave

1. Create an API access token at console.coreweave.com/tokens with an expiry and send it as Authorization: Bearer to https://api.coreweave.com
2. Add GPUs by applying a NodePool resource (compute.coreweave.com/v1alpha1) to a CKS cluster with instanceType and targetNodes. instanceType can't be changed afterwards
3. Set targetNodes to 0 or delete the Node Pool when a job ends. Nodes are whole 8-GPU machines billed by the hour
4. Never pass dry_run to the MCP tool coreweave_kubectl_apply expecting a preview. The reference says the parameter is ignored and the manifest is applied
5. Use a separate Forge API key and https://api.inference.wandb.ai/v1 for Serverless Inference. A CoreWeave API access token doesn't work there