Monitoring Guide
AI Latency Monitoring Guide (2026) - Response Time Optimization
AI latency monitoring: time to first token (TTFT), total response time, throughput (requests/second). SLA targets: chat <2s TTFT, streaming <500ms TTFT, batch <30s. Metrics: P50, P95, P99 latency. Alert thresholds: P95 > SLA, P99 > 2x SLA.
Direct answer
AI latency monitoring: time to first token (TTFT), total response time, throughput (requests/second). SLA targets: chat <2s TTFT, streaming <500ms TTFT, batch <30s. Metrics: P50, P95, P99 latency. Alert thresholds: P95 > SLA, P99 > 2x SLA.
Fast path
- TTFT: measure time from request to first token output.
- Total response: measure complete response generation time.
- Throughput: track requests per second, concurrent sessions.
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Implementation Steps
- TTFT: measure time from request to first token output.
- Total response: measure complete response generation time.
- Throughput: track requests per second, concurrent sessions.
- SLA compliance: P95 latency within target, P99 < 2x target.
- Alerting: configure alerts for latency spikes, degradation.
Frequently Asked Questions
How to monitor AI latency?
Monitor AI latency: track TTFT (time to first token), total response time, throughput (requests/sec). Use metrics: P50 median, P95 SLA threshold, P99 tail latency. Alert when P95 exceeds SLA. Optimize: caching, smaller models for simple tasks, parallel requests.
What is acceptable AI response latency?
Acceptable AI latency: interactive chat <2s TTFT, streaming <500ms TTFT, batch processing varies (30s acceptable). P95 should meet SLA, P99 tail latency tracked separately. Users tolerate longer for complex tasks. Monitor degradation patterns.
Related Guides
Use these adjacent playbooks to keep the same workflow connected across discovery, conversion, and execution.
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