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The missing operational layer for inference

Getting one model to answer one request is straightforward. Keeping a stable endpoint healthy while capacity is scarce, models take minutes to load, traffic changes, and revisions behave differently is the hard part. InferCrane gives teams one production inference platform for that operational gap. It builds or connects inference, preserves application identity, records what happened, and makes risky changes explicit. A durable control plane coordinates lifecycle work, while the request data plane keeps the application endpoint independent from PostgreSQL and provider APIs.

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Problems teams repeatedly encounter

These patterns appear throughout current vLLM and Kubernetes operator discussions: autoscaling needs multiple signals, cold-start lag dominates scale-out, and newly released models often require exact runtime and configuration combinations. InferCrane does not pretend those constraints disappear; it makes them observable and governable.

Signals behind these priorities

The product priorities are grounded in upstream behavior and recurring operator reports, including vLLM’s production metrics, Kubernetes autoscaling behavior, community reports about the number of moving parts in GPU autoscaling, cold-start lag and queue signals, and trial-and-error model/runtime combinations. Community discussions are product-discovery signals, not proof of InferCrane performance.

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What InferCrane replaces - and what it keeps

InferCrane replaces handwritten lifecycle glue, not the infrastructure ecosystem:

• SkyPilot or provider APIs continue provisioning infrastructure.
• vLLM, SGLang, or custom runtimes continue executing inference.
• vLLM Router continues distributing requests among standalone replicas.
• AIPerf continues generating benchmark load.
• Hugging Face Hub/Xet continues resolving and transferring model artifacts.
• OpenTelemetry conventions continue defining portable telemetry.

InferCrane owns the stable endpoint, desired and observed state, durable operations, revisions, rollout policy, evidence, explanations, and adapter qualification.

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Choose InferCrane when

• You want to operate open-weight or custom inference on infrastructure you control.
• You already run vLLM or an OpenAI-compatible gateway and need safer operations without migration.
• You need asynchronous deploy/update/delete operations that survive a disconnected CLI.
• You need evidence before promoting a runtime, model, GPU, or provider change.
• You want provider and runtime portability without adopting a universal lowest-common-denominator API.

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Do not choose InferCrane when

• You want supplier-owned GPU capacity with no infrastructure account. That is part of the planned InferCrane Cloud direction, not the current Community product.
• You need training, fine-tuning orchestration, a generic workflow engine, or an agent framework.
• You expect every model/runtime/provider combination to be supported without qualification.
• You require shared hosted sandbox multitenancy. InferCrane currently offers external sandbox composition and an optional private-tenant Brezel preview.
• You need a production claim for an adapter marked experimental or locally qualified only.