Model Hubs & Serving

Yatai

Model Deployment at Scale on Kubernetes 🦄️

Status
UnmaintainedGitHub repository has been archived.
Last checked
Checked · Oct 12, 2026
License
Open source
Pricing
Free tier · from $0
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What it is

Yatai is a model serving and deployment platform built for Kubernetes. It enables developers to deploy machine learning models at scale, ensuring high availability and scalability.

Yatai stands out as a Kubernetes-native platform specifically designed for deploying machine learning models, offering high availability and scalability without the need for external managed services.

When it fits — and when it doesn’t

A good fit for

  • Teams needing to deploy ML models at scale with Kubernetes

  • Organizations requiring high availability and scalability in their model deployment

  • Developers who prefer a self-hosted solution for model serving

Look elsewhere if

  • Projects that require real-time streaming capabilities (Yatai is batch-oriented)

  • Teams preferring cloud-managed services over self-hosting solutions

Known weaknesses

Each one links to the public issue, changelog or docs page it came from, where we have one.

  1. 01

    Complex setup process for Kubernetes integration

    Requires deep understanding of Kubernetes configurations to deploy effectively

What it does well

  • Kubernetes-native deployment of ML models

  • High availability and scalability

  • Integration with BentoML for model packaging

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 Yatai

Setup steps, common gotchas and what to check after 30 days.

Open the setup guide →
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