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AI Model Deployment: Strategies for Production LLM Serving

Deploying AI models to production requires infrastructure for serving, scaling, and monitoring. LLM deployment differs from traditional ML deployment due to high compute requirements, variable latency, and unique cost models.

Serving Options

Managed APIs (OpenAI, Anthropic, Google) provide the simplest deployment. No infrastructure management. Pay per token. Best for most applications. Limited customization and data control.

Self-hosted (vLLM, TGI, Triton) provide full control. Lower per-token cost at scale. Data stays within your infrastructure. Requires GPU infrastructure and operational expertise.

Hybrid: use managed APIs for production and self-hosted for high-volume or sensitive workloads. This balances cost, latency, and control.

Infrastructure

LLM serving requires GPU instances (A100, H100). Use autoscaling to handle traffic variability. Load balance across instances. Implement request queuing and retry logic. Monitor GPU utilization and memory.

Optimization Techniques

Continuous batching: combine multiple requests into a single batch for efficient GPU utilization. Speculative decoding: use a small model to generate tokens that a large model validates. KV-cache optimization: reuse cached attention computations across requests.

Prompt caching: store processed prompt outputs for identical or similar requests. Semantic caching: cache responses for semantically similar inputs. These techniques reduce both latency and cost.

Monitoring

Track latency (TTFT and TPOT), throughput (tokens/second), error rates, and cost per request. Monitor GPU memory, utilization, and temperature. Set up alerts for latency spikes and error rate increases.

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