Production-Grade LLM Inference Platform: Practice of Kubernetes-Based Elastic Inference Architecture

This article introduces an open-source production-grade LLM inference platform built on Kubernetes, integrating components such as vLLM high-performance inference, LiteLLM unified routing, KEDA+Karpenter elastic scaling, and OpenCost cost monitoring. It aims to address core challenges in LLM production deployment, including high availability, elastic scaling, and cost control, providing enterprises with a complete LLM service solution.

With the widespread application of Large Language Models (LLMs) in production environments, enterprises face three core challenges: ensuring high service availability, achieving elastic scaling to handle traffic fluctuations, and controlling inference costs. Traditional deployment methods struggle to meet these needs, so a cloud-native solution is required to integrate industry-leading tools and technologies.

The platform adopts a layered cloud-native architecture, with the core component stack as follows:

Component	Technology Selection	Function Positioning
Inference Engine	vLLM (Cloud) / Ollama (Local)	High-performance model inference service
Routing Gateway	LiteLLM	Unified API interface, multi-backend management
Orchestration Platform	Kubernetes (kind local/GKE cloud)	Container orchestration and resource management
Auto-scaling	KEDA + Karpenter	Request-level and node-level elastic scaling
Observability	Prometheus + Grafana + Jaeger	Metric collection, visualization, trace tracking
Cost Management	OpenCost + Custom Cost Tracking	Cost monitoring and FinOps practices

Key component details:

• vLLM: Uses PagedAttention technology and continuous batching to maximize GPU utilization, supports quantization formats to reduce memory usage.
• LiteLLM: Provides OpenAI-compatible API, supports multi-backend switching and load balancing, enabling vendor decoupling.
• KEDA: Implements Pod-level scaling based on metrics like request queue and GPU utilization, supports zero scaling to save resources.
• Karpenter: Provisions GPU nodes in seconds, intelligently selects optimal instance types, reduces node fragmentation.
• OpenCost: Multi-dimensional cost analysis, supports cloud provider integration and optimization suggestions, facilitating FinOps practices.

The platform supports two deployment modes:

1. Local Development Mode (kind): Quickly set up a test environment via the make local command, suitable for feature development, CI/CD pipelines, and local demonstrations.
2. Cloud Production Mode (GKE): Deploy to Google Kubernetes Engine, use GKE Autopilot to simplify node management, obtain high-end GPUs like A100/H100 on demand, and integrate Cloud Monitoring for observability.

To ensure service stability and cost control, the following operation strategies are recommended:

• Model Deployment: Use multiple replicas to avoid single points of failure, canary releases to validate new models, and hierarchical caching for hot models.
• Resource Planning: Reserve GPU memory for KV Cache, configure CPU/memory ratios appropriately, and ensure high-bandwidth storage and network.
• Monitoring and Alerts: Focus on metrics such as latency (TTFT/TPOT), throughput, GPU utilization, request queue length, and cost per thousand requests.

The platform is suitable for multiple scenarios:

1. Enterprise Internal AI Assistant: Deploy private LLM services to support internal knowledge base Q&A, code assistance generation, and intelligent document processing.
2. AI SaaS Platform: Provide pay-as-you-go LLM API services for multi-tenants, enabling resource isolation and elastic scaling.
3. Model Evaluation Platform: Support parallel deployment of multiple models and A/B testing, quickly compare performance and collect user feedback.

Project Status: In active development phase, basic architecture setup, vLLM integration, LiteLLM routing, and other features have been completed; detailed architecture documentation, local deployment guide, and cost model documentation are to be improved.

Summary: This platform is not a pile of tools but a carefully designed complete solution, providing a validated reference architecture for LLM service infrastructure planning. Whether for local validation or enterprise-level production environments, value can be derived from it.

Project Link: https://github.com/devam1402/llm-inference-platform-k8s License: MIT