---
id: mlserver
version: "1.7.1"
license: Apache-2.0
license_treatment: permissive
maintenance: aging
---

# mlserver — MLServer

License: permissive · Maintenance: aging · Downloads: 166.1K/mo

## What it is and what it does

MLServer is a production-grade inference server that wraps machine learning models and exposes them via standardized REST and gRPC endpoints. It implements the KFServing V2 Dataplane protocol, allowing models to be served in a framework-agnostic way and integrated into Kubernetes-native platforms like Seldon Core and KServe. The server handles multi-model serving within a single process, supports adaptive batching to group requests on the fly, and can parallelize inference across worker pools for vertical scaling.

The package comes with 25 runtime dependencies covering FastAPI for HTTP handling, gRPC for RPC, OpenTelemetry for observability, Kafka for event streaming, and Pydantic for validation. It ships with pre-packaged runtimes for common frameworks (scikit-learn, XGBoost, LightGBM, HuggingFace, etc.), but you can also write custom runtimes. Installation is straightforward, though you must separately install framework-specific runtime packages to serve models from those frameworks.

Use it for:

- Serve multiple scikit-learn or XGBoost models from a single process with REST/gRPC endpoints.
- Deploy models to Kubernetes using Seldon Core or KServe with MLServer as the core Python backend.
- Implement adaptive batching to group inference requests and improve throughput for batch-friendly models.
- Monitor and trace inference requests using OpenTelemetry integration for observability.
- Build custom inference logic by writing a custom runtime and registering it with MLServer.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

MLServer is an open-source inference server that exposes machine learning models through REST and gRPC APIs compliant with the KFServing V2 Dataplane specification, supporting multi-model serving, adaptive batching, and parallel inference.

Yes, with conditions. MLServer is suitable for production model serving if you need KFServing V2 compliance, multi-model serving, or Kubernetes integration. However, the aging maintenance status warrants checking whether security patches and bug fixes align with your production requirements. Evaluate the 25 dependencies for your deployment footprint and confirm compatibility with Python 3.9–3.12. No known vulnerabilities are reported.

## Install

pip install mlserver
uv add mlserver
poetry add mlserver

## Installing mlserver

Before you install: Low install friction with a pure-Python wheel distribution. Maintenance status is aging—last release was 434 days ago—but the package remains in use. The 25 runtime dependencies are substantial (FastAPI, gRPC, OpenTelemetry, Kafka support) and should be reviewed for your deployment context.

License in practice: Licensed under Apache License 2.0 (permissive), allowing commercial use and modification. Note that optional inference runtimes and frameworks used alongside MLServer may carry different licenses; consult their documentation for legal terms.

Quickstart:

pip install mlserver

from mlserver import MLServer
from mlserver.settings import ServerSettings

settings = ServerSettings()
server = MLServer(settings=settings)
await server.start()

Requires Python 3.9–3.12 (3.13 unsupported). Optional inference runtimes (e.g., mlserver-sklearn) must be installed separately to serve specific model frameworks.

Verify before relying:

- Whether aging maintenance status affects production readiness or security patching cadence.
- Performance characteristics under high concurrency or large model counts in multi-model serving.
- Compatibility and integration testing with specific Kubernetes distributions in your environment.

## Package facts

- License: Apache-2.0 (permissive)
- Python support: capped_below_current
- Install friction: low
- Maintenance: aging
- Downloads: 166.1K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

ml model serving rest api, inference server grpc, kfserving v2 protocol, multi-model serving framework, machine learning model deployment, adaptive batching inference, kubernetes model serving, model-serving, inference-server, kubernetes

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