Model Hubs & ServingStarwhale
MLOps/LLMOps platform for model building and evaluation
- Status
- UnmaintainedNo commits in 614 days.
- Last checked
- Aging · Sep 4, 2026
- License
- Open source
- Pricing
- Free tier · from $0
What it is
Starwhale is an MLOps/LLMOps platform designed to streamline the process of building, evaluating, and fine-tuning machine learning models. It provides a comprehensive solution for developers and data scientists to manage their ML workflows efficiently.
Starwhale offers a comprehensive, self-hosted MLOps solution tailored for model building and evaluation, providing flexibility without cloud dependency.
When it fits — and when it doesn’t
A good fit for
Teams needing a self-hosted MLOps solution for model building and evaluation
Data scientists looking to fine-tune machine learning models locally without cloud dependencies
Look elsewhere if
Projects requiring real-time streaming capabilities (batch-only architecture)
Budget-constrained projects that cannot afford the resources needed for self-hosting
Known weaknesses
Each one links to the public issue, changelog or docs page it came from, where we have one.
- 01
Limited integrations with other ML tools and platforms
Documentation shows limited connectors compared to competitors like Kubeflow or MLflow
- 02
Complex setup for self-hosted deployment
Requires manual configuration of multiple components and services
What it does well
Model building and evaluation
Fine-tuning capabilities
Self-hosted deployment options
Pricing
- Free tier
- YesOpen source — free to use
- Paid from
- $0
- Model
- Flat rate
- Enterprise plan
- No
Pricing not independently confirmed — check the vendor’s page.
Performance benchmarks
How Fast Is It?
Set up Starwhale
Setup steps, common gotchas and what to check after 30 days.