Compose specialist systems without blurring ownership

InferCrane integrates with established infrastructure and inference projects through narrow, replaceable contracts. A compatible license is necessary but not sufficient. Every integration also needs a pinned version, a security review, conformance tests, explicit mutation ownership, and real-system qualification before it can be advertised as supported.

System category	Integration model	Candidate upstreams	License boundary	InferCrane owns	Specialist system retains
LLM gateway	OpenAI-compatible target or replaceable gateway adapter	LiteLLM, Envoy AI Gateway	Review exact distributed paths; no fork is bundled	Endpoint identity, budgets, evidence, releases	Provider translation catalog and upstream credentials
Training	Signed checkpoint handoff; job adapter only after demand	Kubeflow Trainer, MLflow, SkyPilot jobs	External service and artifact terms remain operator-owned	Artifact lineage, qualification, deployment handoff	Training data, execution, storage, and scheduling
Vector database	Dependency identity and health evidence only	Qdrant, Milvus	External service; not redistributed	Endpoint linkage and evidence references	Vector storage, indexing, and retrieval
Agent framework	Stable endpoint, SDK examples, telemetry, and request identity	LangGraph, Google ADK	External application dependency	Inference identity and operational evidence	Planning loop, tools, and application state
Sandbox	External identity plus scoped, expiring inference access	E2B, Kubernetes Agent Sandbox	External runtime and credentials	Endpoint access policy and evidence references	Isolation, commands, files, snapshots, and networking
Inference engine	Runtime Contract adapter and conformance	Ollama, llama.cpp, TensorRT-LLM	Audit the exact engine and redistribution path	Capability truth, lifecycle intent, and evidence	Engine internals, kernels, decoding, and distributed KV
Kubernetes	Standard APIs against an operator-owned cluster	Kubernetes, KServe, Kind for tests	InferCrane does not distribute a cluster	Workload intent, manifests, lifecycle, and evidence	Cluster, networking, storage, devices, and scheduling
GPU scheduler	CRD or API selected by the cluster owner	Kueue, Volcano	External cluster component	Serving intent and scheduling evidence	Queueing, placement, and device allocation
Workflow engine	Events, webhooks, SDK steps, and bounded async inference	Argo Workflows, Temporal	External application service	Inference operations and inference idempotency	Generic DAG and workflow execution

Build, integrate, or reject

Build a capability only when it belongs to InferCrane’s durable inference contract and no specialist system already owns it. Integrate when a mature upstream project owns the concern. A fork is acceptable only when all of these conditions are true:

The license permits the exact distribution model.
An adapter cannot produce the required user outcome.
The divergence is deliberately small and bounded.
A named maintainer owns upstream security merges.
A removal or upstreaming plan exists.

No candidate listed above currently meets that fork threshold.

Qualification ladder

Each adapter advances independently:

REGISTERED → CONTRACT_TESTED → LOCAL_QUALIFIED → REAL_SYSTEM_QUALIFIED

Registration means a capability is discoverable. It does not prove credentials, networking, capacity, runtime behavior, performance, billing, cleanup, or recovery on a real system. The CLI, API, console, and documentation must display the achieved tier rather than infer support from the presence of an adapter.

Start here

Gateways and external systems

SDKs and automation

Providers

Runtimes and evidence tools

Integration ownership and licenses

Compose specialist systems without blurring ownership

Build, integrate, or reject

Qualification ladder

​Compose specialist systems without blurring ownership

​Build, integrate, or reject

​Qualification ladder

Compose specialist systems without blurring ownership

Build, integrate, or reject

Qualification ladder