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AI Reserve GPU Instances is a provider-neutral compute contract, not a wire-compatible RunPod API. The common single-container GPU workflow maps cleanly, but request fields, IDs, operation polling, and response models change.

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A common request

A secure, on-demand RunPod request conceptually like:
becomes this AI Reserve request:
The exact RunPod GPU SKU and data center are deliberately absent. AI Reserve selects eligible secure capacity and returns its own resource ID.

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Call-flow changes

The CLI’s --wait flag handles operation polling. Follow the quickstart for the complete command sequence.

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Field mapping

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What AI Reserve adds

  • placement and fallback across mapped provider capacity;
  • an optional non-spending quote with a complete price breakdown;
  • storage-aware placement and inline persistent-volume creation;
  • durable operations and idempotent mutations;
  • normalized capacity refusals with a reason and alternatives;
  • wallet, quota, and Compute-key policy at one API boundary.

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Provider-specific features not currently normalized

A migration needs redesign—or may not be possible yet—when it depends on:
  • Community Cloud or spot/interruptible bids;
  • CPU Pods;
  • exact GPU SKU, country, data center, or compliance placement;
  • minimum host RAM, vCPU, CUDA version, or bandwidth constraints;
  • RunPod template IDs, provider-managed Jupyter startup, or Pod locks;
  • global networking or private Pod DNS (Instant Clusters are normalized as multi-node clusters; cluster templates and rename are not);
  • local persistent Pod disks or high-performance network-volume tiers;
  • in-place Pod updates or a normalized restart endpoint;
  • provider runtime metrics or complete launch-configuration read-back.
The normalized API accepts a deliberately smaller set of portable controls. It does not currently offer a providerOptions.runpod escape hatch.

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Storage differences

Both products preserve an independent network volume after compute termination. AI Reserve additionally places new storage with the GPU and exposes its post-termination rate in the instance quote. It does not normalize RunPod’s exact data-center selection, volume tiers, or volume rename. See Storage and persistence for attachment, billing, teardown, and the transitional grow flow.

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Migration checklist

  • Map each required RunPod GPU SKU to an AI Reserve accelerator family.
  • Remove exact provider and data-center dependencies, or record them as blockers.
  • Replace create-response assumptions with operation polling.
  • Persist AI Reserve instance and operation IDs instead of RunPod IDs.
  • Add idempotency keys to every mutation.
  • Convert ports and decimal price fields to the normalized shapes.
  • Register SSH keys and registry credentials in AI Reserve.
  • Rework status comparisons and connection discovery.
  • Confirm storage teardown explicitly deletes volumes when intended.
  • Test one paid instance end to end before migrating automation.
For the provider-shaped compatibility API, see GPU Compute. New integrations should use GPU Instances.