---
title: "KServe | Skills We Assign For | Azendo"
description: "KServe for Kubernetes model serving — standard interface, scale to zero, plus the Azendo roles assigned for it."
url: "https://azendo.co/skills/kserve/"
---

[Skills](https://azendo.co/skills/) MLOps and model delivery 

# KServe.

KServe is a Kubernetes-native model serving platform providing a standard inference interface across frameworks, with autoscaling including scale-to-zero, canary rollout and explainability hooks.

## Where KServe fits on a long engagement.

A standard inference interface across frameworks is the organisational win. A PyTorch model and an XGBoost model presented identically means client code, monitoring and deployment tooling do not branch per framework.

Scale-to-zero matters most for the long tail. Many organisations have models used occasionally where a permanently running instance is disproportionate, and scaling to zero with a cold start on first request makes those economically viable — provided the latency is acceptable.

## What an assigned team does with KServe.

It assumes Kubernetes competence. For a team already running Kubernetes it fits naturally; for one that is not, adopting it means taking on the cluster as well as the serving platform.

That is a significant scope question rather than a tooling detail, and it is assessed at scoping alongside [devops as a service](https://azendo.co/services/cloud-and-devops/) capacity.

## What we use KServe for.

* One interface across frameworks Client and monitoring code that does not branch per model type.
* Occasional models made viable Scale-to-zero for the long tail, where a running instance is disproportionate.
* Canary rollout for models Traffic shifted gradually with automatic rollback on degradation.

## How KServe capacity is assigned.

Kubernetes-based serving is assigned across AI and platform capacity, with cluster competence treated as a prerequisite rather than an assumption.

## Service lines it sits in

* [MLOps engineering](https://azendo.co/services/mlops-engineering/)

Capacity is agreed as a committed monthly capacity across a discipline, not per skill.

## Related in mlops and model delivery

* [MLOps handoff — skill we assign for](https://azendo.co/skills/mlops-handoff/)
* [ONNX — skill we assign for](https://azendo.co/skills/onnx/)
* [TensorRT — skill we assign for](https://azendo.co/skills/tensorrt/)
* [Model registry — skill we assign for](https://azendo.co/skills/model-registry/)
* [Weights & Biases — skill we assign for](https://azendo.co/skills/weights-and-biases/)
* [Comet — skill we assign for](https://azendo.co/skills/comet/)
* [Neptune.ai — skill we assign for](https://azendo.co/skills/neptune-ai/)
* [DVC — skill we assign for](https://azendo.co/skills/dvc/)

## Tell us what your roadmap needs KServe for.

A service delivery manager replies with the disciplines we would assign, the monthly capacity and what the first month looks like.

[All skills we assign for](https://azendo.co/skills/)