Large Language Model Serving: How to Deploy, Optimize, and Secure AI Models in Production

When you run a large language model serving, the process of deploying and running AI models like GPT, Llama, or Claude in live applications to answer user queries in real time. Also known as LLM inference, it's not just about calling an API—it's about managing latency, cost, security, and reliability at scale. Most teams start by plugging into OpenAI or Anthropic, but that’s just the beginning. Once you move past prototypes, you hit real problems: your bill spikes when users ask long questions, your model hallucinates on niche data, and you can’t switch providers without rewriting half your app.

That’s where model optimization, techniques like quantization, pruning, and distillation to shrink LLMs without losing accuracy. Also known as LLM compression, it lets you run smaller models on cheaper hardware comes in. You don’t always need a 70B model. Sometimes a 7B model, properly tuned and cached, does the job better—and costs 10x less. Then there’s vendor lock-in, when your app becomes dependent on a single AI provider’s API, making it hard to switch without major rework. Also known as API dependency, it’s a silent risk that bites when pricing changes or the API goes down. Tools like LiteLLM and LangChain help you abstract away the provider, so you can swap models like swapping batteries.

And you can’t ignore LLM deployment, the full lifecycle of getting a model from development into production, including monitoring, scaling, and security. Also known as AI operations, it’s where most projects fail—not because the model is bad, but because the pipeline isn’t built for real users. Think about this: if your app serves 10,000 requests a day, and each one uses 500 tokens, you’re burning through millions of tokens monthly. That’s not a feature—it’s a financial liability if you don’t plan for autoscaling, spot instances, or request batching. Security matters too. Are you encrypting prompts? Are you checking for prompt injection? Are you logging everything for compliance? These aren’t optional.

The posts below cover exactly these pain points. You’ll find guides on cutting cloud costs by 60%, building safe multi-tenant systems, using confidential computing to protect data during inference, and how to measure if your LLM is even telling the truth. There’s no fluff. No theory without practice. Just real strategies from teams who’ve been through it—how to avoid the traps, how to pick the right model size, how to keep your system running when your users don’t stop asking questions.