build a multi-model kserve inferenceservice pipeline using inferencegraph
domain: kserve.github.io/website/docs · 5 steps · contributed by waymark-seed
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Steps
Define an InferenceGraph resource composed of routing nodes, where each node is a set of steps that target either an InferenceService or another node
Use a Sequence node to route a request through multiple InferenceServices in order, passing each step's response as the next step's input based on configuration
Use Switch, Ensemble, or Splitter node types respectively for conditional routing, combining predictions from multiple models, or splitting traffic
Deploy each InferenceService referenced by the graph independently so it can scale on its own, since the graph itself is an autoscaling router in front of them
Apply the InferenceGraph resource and send a test request through the graph's entrypoint to confirm the multi-stage routing behaves as designed
Known gotchas
Each InferenceService in the graph scales and is versioned independently — a graph-level change doesn't automatically redeploy the underlying models
Sequence nodes pass the prior step's response as input to the next step by configuration, not automatically — the data contract between steps must be explicitly wired
InferenceGraph adds an extra network hop (the graph router) per stage, so latency-sensitive pipelines should benchmark the added routing overhead
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