Yatai
Model Deployment at Scale on Kubernetes 🦄️
Pricing
Free tier
Flat rate
Adoption
↘CoolingLicense
Open Source
Data freshness
Aging · Jun 8, 2026Overview
What is Yatai?
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.
Key differentiator
“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.”
Capability profile
Capability Radar
Honest assessment
Strengths & Weaknesses
↑ Strengths
↓ Weaknesses
API requires Python-specific patterns, TypeScript SDK is community-maintained
v0.1 to v0.2 migration required rewriting chain definitions
Primary development and documentation focus on Python, with minimal support for other languages
Requires deep understanding of Kubernetes configurations to deploy effectively
Fit analysis
Who is it for?
✓ Best 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
✕ Not a fit for
Projects that require real-time streaming capabilities (Yatai is batch-oriented)
Teams preferring cloud-managed services over self-hosting solutions
Cost structure
Pricing
Free Tier
Available
Open source — free to use
Starts at
$0
Model
Flat rate
Enterprise
None
Performance benchmarks
How Fast Is It?
Ecosystem
Relationships
Alternatives
Works well with
Integrations
Next step
Get Started with Yatai
Step-by-step setup guide with code examples and common gotchas.