Technical Use Case
AI/ML Training & Inference
GPU clusters for training, optimized inference endpoints, and MLOps pipelines for production AI.
Cloud Native Architecture
Scalable & Resilient Design
GPU Instances
Model Registry
Inference API
MLflow
The Challenge
Modern applications require infrastructure that can handle rapid change, scaling demands, and complex dependencies. Traditional monolithic setups often lead to bottlenecks, making it difficult to innovate quickly or maintain reliability at scale.
The Solution
Lineserve provides a fully managed environment for AI/ML Training & Inference, removing the operational overhead so you can focus on code.
- Managed Control Plane
- Auto Scaling
- Integrated Security
- Global Reach
System Architecture
AI/ML Training & Inference Architecture
Access
DNS
Global
CDN
Edge
Ingress
Load Balancer
L7
Gateway
API
Compute
Cluster
Primary
Workers
Nodes
State
Database
Managed
Storage
Block
Implementation Guide
Detailed implementation steps are available in our full documentation.
Required Product Stack
| Product | Purpose | Requirement |
|---|---|---|
| Standard core infrastructure required (Compute, Networking, Storage). | ||
Estimated Starting Cost
Based on minimum production-ready configuration
$150/month
Quick Start
bash
$ lineserve init ai-ml
# Initializing ai-ml environment...
# Creating VPC... Done
# Provisioning resources... Done
# Creating VPC... Done
# Provisioning resources... Done
$ lineserve deploy --env production
✓ Deployment successful!