The Best Mmdeploy Alternatives in 2026

Package, configure, and iterate on a model with Truss at whatever level of control your model needs.

Merlin supports machine learning model management, deployment, and serving in Kubernetes environments.

Eurybia tracks model drift and validates data during machine learning model deployment.

Model deployment for AI applications is supported by nndeploy, a framework described as easy to use and high performance.

Cog packages machine learning models into containers to support deployment across environments.

ONNX-MLIR compiles ONNX models using MLIR compiler infrastructure for model deployment workflows.

DJL is a Java framework for running deep learning models across different engines.

Train and run models with OneFlow, a scalable deep learning framework for distributed machine learning workloads.

ORMB packages machine learning models as OCI artifacts, in a workflow described as Docker for ML models.

Machine learning operations and large language model operations are the focus of Starwhale’s platform.

Manifest connects AI agents and tools to multiple model providers through an LLM gateway.

ModelDB tracks machine learning model versions, metadata, and experiments.

FastChat offers an open platform for training, serving, and evaluating large language models, and hosts release repositories for Vicuna and Chatbot Arena.

Flexible, high-performance serving system for machine learning models.

Kitaru provides agent traces that teams can run and inspect during application workflows.

The Hydrosphere repository describes an MLOps platform for machine learning operations.

OpenAI compatible API for LLMs and embeddings (LLaMA, Vicuna, ChatGLM and many others).

File Parser optimised for LLM Ingestion with no loss. Parse PDFs, Docx, PPTx in a format that is ideal for LLMs.

dstack coordinates AI compute and supports training and inference across GPU clouds, Kubernetes, VMs, and bare metal.

MiroShark describes a swarm intelligence engine for simulating scenarios with stated time and cost targets.

Latitude provides observability for AI agents and helps teams trace failures and verify fixes.

Marquez collects, aggregates, and visualizes metadata about a data ecosystem.

Harbor sets up a preconfigured LLM stack with one command and includes hundreds of services to explore.

BLACKBOX AI serves selected open-weight models as dedicated deployments or through one inference endpoint.