Inference Latency

Inference Latency is the time between sending a prompt to an AI system and receiving its full response. Latency matters enormously for user experience — a chatbot that takes 30 seconds to respond feels broken, while one that responds in 800 milliseconds feels conversational. For LLMs, latency breaks into two components: time-to-first-token (TTFT) which measures how quickly the response starts appearing, and inter-token latency which measures how fast subsequent tokens stream. Typical TTFT for major hosted LLMs ranges from 200ms to 2 seconds depending on model size and load. Latency depends on model size, prompt length (longer prompts take longer to process), hardware, batching strategy, and network distance. Tools like LangSmith, Helicone, and Langfuse track latency across production. AI governance, AI compliance, and AI risk management programs incorporate latency monitoring into operational SLAs supporting responsible AI deployment across every customer-facing enterprise AI system.

Centralpoint Tracks Latency Across Every Model You Use: Centralpoint by Oxcyon meters response times alongside token consumption across OpenAI, Gemini, Llama, and embedded models. The platform keeps prompts and skills on-prem and deploys low-latency chatbots into any portal via a single line of JavaScript — letting you compare provider performance side by side.


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