ome-projects/ome

[ENHANCEMENT] Integrate KEDA operator to enable advanced autoscaling in OME

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#155 aberto em 9 de jul. de 2025

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Description

What would you like to be added?

Support for integrating the KEDA operator to enable advanced, custom metrics-based autoscaling for OME-managed LLM workloads.

Why is this needed?

Current OME lacks native support for autoscaling based on custom or external metrics. Integrating KEDA will allow OME to:

  • Dynamically scale model-serving workloads in response to real-time demand.
  • Optimize GPU and compute resource costs by scaling pods up and down automatically.
  • Support a wider range of scaling triggers beyond standard CPU/memory metrics (e.g., Prometheus queries, external event sources).
  • Improve latency and reliability for LLM inference during traffic spikes. This enhancement would provide greater flexibility and operational efficiency for enterprise users deploying LLMs at scale.

Completion requirements

  • Design doc (if significant feature)
  • API change
  • Docs update
  • Tests

Can you help us implement this enhancement?

  • Yes, I can contribute
  • No, but I'm available for testing
  • No

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