Deploying LLMs in Production: Latency, Cost & Reliability Trade-offs

Deploying a large language model behind a chat interface for a demo is easy. Deploying one in a production system that processes 10,000 requests per hour with a p95 latency SLA, predictable costs, and 99.9% uptime is an entirely different engineering challenge.

The Latency Problem

LLM inference is slow by the standards of traditional APIs. GPT-4 Turbo averages 15-30 seconds for long generations under load. For user-facing features, this is generally unacceptable. The solutions range from architecture changes to model selection.

• Streaming: For any user-facing feature, stream tokens as they are generated — perceived latency drops dramatically.
• Smaller models: GPT-4o-mini, Llama 3 8B and Mistral 7B are 10-50x faster than GPT-4 for tasks that do not require frontier-model reasoning.
• Semantic caching: Return cached responses for functionally identical requests. 20-40% cache hit rates are common in focused use cases.
• Batching: For non-real-time pipelines, batching requests 5-10x and processing asynchronously is the highest-leverage optimisation.

The Cost Problem

• Model routing: A layer that sends simple queries to cheaper models and complex queries to frontier models can cut costs by 50-70%.
• Prompt compression: Long system prompts are expensive. Tools like LLMLingua compress prompts without degrading quality.
• Self-hosted inference: Running Llama 3 70B via vLLM has a break-even vs. API costs at roughly 2-5M tokens/day.

Reliability and Observability

LLMs are non-deterministic and will occasionally produce harmful, incorrect or off-topic outputs. Production deployments need output validation, fallback chains, circuit breakers, and comprehensive logging of every inference call.

“Every LLM that makes a decision in your product is a system you are responsible for. Build it with the same reliability engineering discipline you would apply to any other critical service.”

— Priya Sharma, CTO