CoreWeave Alternatives for Inference
Serving or batch-running a model for generation, where throughput per dollar decides the bill.
What inference actually needs
Why people look past CoreWeave for this
Little state to carry, so this is the job that moves most freely on price. The cost is the weight pull and the warm-up on the new box, not lost work. That is exactly the gap CoreWeave does not close: CoreWeave is built for organisations buying GPU capacity at fleet scale on contracts, with Kubernetes-native infrastructure and one of the largest H100/H200/GB200 estates anywhere. If you are one developer who wants a box that remembers your environment, it is the wrong shape and the published per-GPU rates show it.
To be fair, CoreWeave's real strength is real: Fleet scale and hardware breadth, including GB200 NVL72: capacity most brokers simply cannot source.
Live rates for the GPUs inference wants
Live per-GPU rates from Aquanode's marketplace. Refreshes hourly.
How you run it here today
Run it today with the seeded vllm-batch job recipe — a real, publicly-imaged container Aquanode can queue directly, not a template we're promising to build later.
Restoring an environment requires a snapshot that already exists. Stopping a deployment yourself captures it on the way out, so you can bring it back later on any provider. A provider-side termination is different: it is only recoverable if you had already switched automated snapshots on for that deployment, and it costs you the work since the last one. Automated snapshots are opt-in, nothing runs until you start it, and with none running there is nothing to restore.
More alternatives pages
Other workloads on CoreWeave
Inference alternatives to other clouds
