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A lightweight, multi-model inference server supporting standard runtimes and protocols for production model serving.
DESCRIPTION
MLServer is an open-source inference server from Seldon supporting multiple models and runtimes like SKLearn, XGBoost, LightGBM, and ONNX. It implements standardized inference protocols over REST and gRPC. The server is extensible via custom runtimes and integrates with Kubernetes platforms, including KServe and Seldon Core. It focuses on performance, observability, and compatibility. Multi-model serving reduces infrastructure overhead. It is suited for cloud-native, production-grade deployments.
ALTERNATIVE DEFINITIONS
MLServer Documentation
MLServer is a lightweight and fast inference server for serving machine learning models.
Stresses lightweight design and performance for model serving.
Seldon
An open-source inference server that supports multiple model runtimes and standard inference protocols.
Highlights multi-runtime support and standards compliance.
RELATED TERMS
KServe Seldon Core ONNX Runtime V2 Inference Protocol gRPC REST Model Serving Multi-model Serving
COMPETING TERMS
Triton Inference Server BentoML TorchServe
KNOWLEDGE AREAS
MLOps AI Infrastructure Platform & Tooling
TARGET AUDIENCE
MLOps Engineer AI Engineer Software Developer Cloud Architect
EXTERNAL SOURCES
- Seldon - Getting started with MLServer
- Seldon - Supported inference protocols
- MLServer - Built-in and custom runtimes
TOP QUESTIONS
Why choose MLServer for inference?
It’s lightweight, supports multiple runtimes, and integrates with Kubernetes platforms for production-ready deployments.
Category: Relevancy
How does MLServer support multiple models?
It can load and serve several models in one instance, reducing overhead and simplifying management.
Category: Application
What protocols does MLServer support?
It supports REST and gRPC, including standardized inference protocols for portability.
Category: Overview
What are common pitfalls with MLServer?
Not sizing resources for multi-model workloads can cause memory pressure; monitor and tune concurrency.
Category: Mistakes
How does MLServer compare to Triton and TorchServe?
MLServer emphasizes extensibility and standards; Triton focuses on GPU acceleration; TorchServe is PyTorch-centric.
Category: Alternatives
Can MLServer integrate with KServe or Seldon Core?
Yes, it is compatible and often used as a default server in those platforms.
Category: Application
How to add a custom runtime in MLServer?
Implement the runtime interface and package it so MLServer can load and serve your model type.
Category: Requirements
How to monitor MLServer in production?
Use built-in metrics, logs, and tracing with tools like Prometheus and OpenTelemetry.
Category: Best practices
What’s trending with MLServer?
Adoption in multi-model deployments and standardized protocols for interoperability across platforms.
Category: Topic