Kserve

Overview

Kserve is a standardized platform for deploying machine learning models on Kubernetes, supporting both generative and predictive inference. It handles multi-framework serving, scaling, and resource management for distributed AI workloads.

Best for

Best for
Teams already using Kubernetes who need a scalable, multi-framework inference server

Use cases

• Deploy large language models in production on Kubernetes
• Run batch predictions with autoscaling and canary rollouts
• Serve models from TensorFlow, PyTorch, and other frameworks via a unified API

Notes

Kserve is a standardized platform for deploying machine learning models on Kubernetes, supporting both generative and predictive inference. It handles multi-framework serving, scaling, and resource management for distributed AI workloads.

5,534 stars on GitHub. Last updated 2026-06-01. Licensed Apache-2.0.

Use cases

• Deploy large language models in production on Kubernetes
• Run batch predictions with autoscaling and canary rollouts
• Serve models from TensorFlow, PyTorch, and other frameworks via a unified API

Pros

• Open source with strong community backing and 5500+ stars
• Supports autoscaling, canary deployments, and request routing
• Works on any Kubernetes cluster with minimal vendor lock-in

Cons

• Steep learning curve for operators unfamiliar with Kubernetes
• Complex configuration for advanced serving topologies
• Observability features require additional tooling like Prometheus

Indexed from awesome-llmops and enriched against its public facts.