Cerebrium vs Thunder Compute

Cerebrium

Per-second GPU prices are public, a 94-operation OpenAPI spec covers the management API, and service account tokens expire and are limited to named projects. Deployed endpoints are callable without a token unless disable_auth = false is set, and no request rate limits, 429 handling or SLA were found in the reviewed documentation.

Thunder Compute

The hosted MCP server signs in with OAuth and separate read and write scopes, and the REST API has a public OpenAPI 3.1 document with keyless price and availability endpoints. No rate limits, SLA or API changelog were found, instance creation has no idempotency key, and instances cannot be stopped, only deleted.

Cerebrium

1. Set disable_auth = false in cerebrium.toml before deploying. The default leaves the endpoint callable by anyone with the URL
2. Authenticate headless with CEREBRIUM_SERVICE_ACCOUNT_TOKEN. cerebrium login opens a browser
3. Raise response_grace_period for long work. It defaults to 15 minutes and async runs stop at 12 hours
4. Send ?async=true to get a run_id with HTTP 202, and add webhookEndpoint because async calls return no result to the caller
5. Check the plan before choosing hardware. A100, H100, H200, B200 and RTX PRO 6000 need Standard, and protected compute bills at twice the listed rate

Thunder Compute

1. Call GET /v2/status or the get_availability tool before creating an instance. Availability can change before launch, and creation fails when a type is sold out.
2. List instances before retrying a failed create, because the call has no idempotency key.
3. Pass public_key on create. If omitted, the response carries a generated private key that is returned once.
4. To pause work, create a snapshot, delete the instance and later create a new instance from the snapshot. Snapshot storage keeps billing until deleted.
5. For headless use set TNR_API_TOKEN to a token from the console. The MCP server needs a browser sign-in on first connection.