KServe

KServe is a standardized inference platform for scalable, multi-framework AI deployment on Kubernetes.

KServe is a standardized distributed generative and predictive AI inference platform designed for scalable, multi-framework deployment on Kubernetes. It unifies generative and predictive AI workloads, supporting backends like vLLM and llm-d for large language models, as well as frameworks such as TensorFlow, PyTorch, scikit-learn, XGBoost, and ONNX. The platform provides features such as GPU acceleration, model caching, KV cache offloading, request-based autoscaling, canary rollouts, and model explainability. It is built as a Cloud Native Computing Foundation incubating project for organizations deploying AI at scale. Users can install KServe via standard Kubernetes, Knative, ModelMesh, or Kubeflow.

KServe at a glance

Best for: Software engineer
Runs on: Kubernetes, self-hosted, open source
Pricing: Free
Released: March 2019
Category: AI coding tool
Website: github.com/kserve/kserve

Key features

Optimized backends for vLLM and llm-d
OpenAI-compatible inference protocol
GPU acceleration and memory management
Request-based autoscaling and scale-to-zero
Multi-framework support for TensorFlow and PyTorch
Canary rollouts and inference pipelines
Built-in model explainability and drift detection

Use it for

Deploying large language models with vLLM
Running canary rollouts for machine learning models
Serving predictive models using TensorFlow and PyTorch
Scaling enterprise AI workloads on Kubernetes

Alternatives to KServe

LMDeploy — Accelerates inference with high-performance engines and quantization tools.
llm-d — Deploys production-grade LLM serving on existing hardware clusters.
Genkit — Provides unified APIs and local developer tools for AI application workflows.
GPUStack — Unifies AI model serving and GPU compute management across heterogeneous hardware.
fal — Access 1,000+ generative models and high-performance serverless GPUs.
Together AI — Fast serverless inference and GPU clusters with transparent token pricing.
Flyte — Resumes failed tasks automatically from the exact point of interruption.
Superlinked SIE — Consolidates model serving in one cluster to reduce per-token API costs.
Freebuff — Access multiple coding AI models for free without subscriptions.
MNN — It delivers high-performance inference and training with a lightweight footprint.
LMCache — It stores, compresses, searches, and reuses KV caches across clusters.
Cube Studio — An open source, end-to-end platform for MLOps and distributed model training.

All KServe alternatives