Ship an Agent: Deployment, Monitoring, and Cost Control
What this scenario solves
It works locally and fails in production: cost runs away, errors go unnoticed, and nothing is traceable.
Tool stack
Not the only solution, but a stack we have verified end to end. Each tool links to its full review, including who it is not for.
- 01vLLM開源社群（源於 UC Berkeley）
High-throughput LLM inference server — the default choice for self-hosted production
- 02OpenClaw開源社群
Open-source, skills-driven agent framework — extensively benchmarked on this site
- 03Claude CodeAnthropic
Terminal-based coding agent that can point at any OpenAI-compatible backend
What you end up with
A deployment with health checks, usage and cost monitoring, failure alerts, traceable logs, and a degradation strategy.
Full steps
- 01環境
- 02實錄
- 03最終架構狀態
Articles carrying the full content
- Building a Three-Layer Agent Framework from Scratch: Complete Deployment Record
A record of the complete process from a blank system to a fully running three-layer Agent on a Mac Studio M3 Ultra, including all pitfalls encountered, configuration details, and debugging tips.
2026-05-098 minRead - Complete Guide to vLLM Deployment and Performance Tuning
vLLM high-performance LLM inference engine deployment guide: installation, configuration, PagedAttention principles, performance benchmarking.
2026-05-107 minRead - LiteLLM Proxy Multi Model Routing in Practice
Use LiteLLM Proxy to centrally manage multiple LLM backends such as DeepSeek, ModelStudio, Ollama, and more: load balancing, cost tracking, fallback configuration.
2026-05-108 minRead
Adjacent scenarios
Other scenarios using
- vLLM in other scenarios
- OpenClaw in other scenarios
- Claude Code in other scenarios
Level: Advanced · Tracks: Developer Track · Last verified: 2026-09-29