Spheron 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 Spheron 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 Spheron does not close: Spheron publishes aggressive rates on a decentralized supply network and is genuinely cheap on consumer-class GPUs. If your workload is a one-shot job the price is hard to argue with; if you are rebuilding the same environment every time you rent, the price is not what is costing you.
To be fair, Spheron's real strength is real: Published headline rates on consumer GPUs that are among the lowest we have verified first-party.
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 Spheron
Inference alternatives to other clouds
