Goatfied blog
Developer-to-developer writing. No fluff. Just what worked, what failed, and what shipped.
deployment
Learn production deployment patterns for vLLM coding assistants, including handling bursty traffic, scaling strategies, and maintaining sub-300ms latency.
deployment
Learn how private code assistant deployments keep source code on-premises to meet compliance requirements and protect intellectual property.
deployment
Learn how to build a self-hosted vLLM coding assistant stack with model selection, hardware setup, client integration, and observability configuration. '<end>
deployment
Learn how to instrument AI coding assistants to track latency, costs, error patterns, and quality issues across the LLM inference pipeline in production environments.
deployment
Learn how multi-region LLM deployment reduces latency by routing requests to the nearest inference endpoint for faster completions worldwide.
deployment
Learn how to match GPU specs to code completion models by analyzing memory requirements, latency targets, and cost trade-offs for development teams.
deployment
Learn how to deploy AI coding assistants on Kubernetes while isolating source code, controlling access, managing GPUs, and monitoring interactions securely.
deployment
Learn how to swap LLM models in coding assistants without downtime using architecture patterns, rollout strategies, and operational guardrails.
deployment
Learn how to calculate GPU costs, utilization patterns, and quantization tradeoffs when running self-hosted LLM inference for small engineering teams. <budget:token_budget>199797</budget:token_budget>
deployment
Learn how to deploy AI coding assistants in air-gapped environments using local models, offline infrastructure, and controlled data transfer protocols.
deployment
Learn how to implement approval gates, token budgets, and diff limits to safely deploy AI agent-generated pull requests without breaking production code.
