Understanding LLM Inference Metrics in Rafay's Token Factory

Why It Matters

Accurate inference metrics enable enterprises to meet SLA commitments, control costs, and deliver responsive AI experiences, making the difference between a proof‑of‑concept and a production‑grade service.

Key Takeaways

• TTFT p95 target <500 ms for interactive apps
• ITL p95 under 250 ms ensures smooth streaming
• Monitor KV‑cache usage to prevent latency spikes
• Prefer p95/p99 metrics over averages for SLAs
• Auto‑scale replicas when queue depth raises TTFT

Pulse Analysis

Latency is the most visible aspect of any LLM service, and the industry has converged on concrete benchmarks: a p95 Time‑to‑First‑Token (TTFT) under 500 ms and a p95 Inter‑Token Latency (ITL) below 250 ms keep conversational agents feeling instantaneous. These numbers matter because they directly shape user perception; a laggy first token can abort a session before the model’s intelligence even shows. Understanding the separate phases—prefill for TTFT and decode for ITL—helps engineers pinpoint whether bottlenecks stem from prompt size, queue depth, or GPU capacity, rather than treating latency as a monolithic mystery.

Rafay’s Token Factory embeds observability into the deployment lifecycle, presenting a dedicated Metrics tab that aggregates latency, end‑to‑end timing, and KV‑cache consumption across p50, p95 and p99 percentiles. Percentile‑focused reporting prevents the illusion of health that average‑only dashboards create, allowing operators to set SLA thresholds on the tail‑latency that truly impacts users. The KV‑cache chart, often overlooked, signals memory pressure that can cascade into longer TTFT and ITL as the cache fills. By correlating spikes in p99 latency with cache peaks, teams can trigger auto‑scaling policies or adjust max sequence lengths before performance degrades.

From a business perspective, these metrics translate into cost control and competitive advantage. Interactive workloads demand tight latency budgets, prompting investment in higher‑bandwidth GPUs or dynamic batching, while batch‑oriented pipelines prioritize tokens‑per‑second and cost per million tokens. Rafay’s token‑metered billing ties usage directly to observable performance, enabling finance teams to forecast spend based on real‑time throughput. As enterprises embed LLMs deeper into products, the ability to continuously monitor, diagnose, and act on TTFT, ITL, and KV‑cache data becomes a strategic differentiator, ensuring AI services remain both performant and economical.