Latency
The time delay between receiving user input and producing a response, a critical performance metric for conversational AI systems.
Overview
Latency measures how quickly your AI system responds to user inputs. In conversational AI, response time directly impacts user experience—users expect near-instantaneous responses in chat interfaces. High latency can lead to user frustration, abandonment, and perception of system unreliability.
Why Latency Matters
Natural conversation flow depends on quick responses—delays break the conversational rhythm. Users expect chat interfaces to feel real-time and immediate. Slow responses drive abandonment as users lose patience and leave. Interestingly, faster responses are often perceived as indicating higher intelligence or better quality, regardless of actual content.
High latency reduces engagement and conversion rates. It limits how many users you can serve concurrently, affecting scalability. Longer response times consume more compute resources, increasing costs. For enterprise deployments, latency determines whether you can meet service-level agreement commitments.
Testing Latency
Basic response time measurement involves tracking how long each request takes from input to output. Record timestamps at the start and end of processing to calculate total latency. Latency metrics should capture not just averages but the full distribution of response times. Load testing reveals how latency changes under realistic concurrent user scenarios, helping identify bottlenecks before they affect production users.
Latency Targets
Appropriate latency targets vary by use case. Chat interfaces should aim for under 1000ms ideally, with 2000ms being the acceptable upper limit before users become noticeably frustrated. Voice assistants require even faster responses—under 500ms ideally and under 1000ms at most—because delays in voice feel more jarring than in text. Batch processing can tolerate up to 10 seconds or more depending on complexity, since users don't expect immediate results. Background tasks that aren't user-facing can have variable latency requirements based on business needs rather than user experience.
When monitoring latency, don't rely solely on averages—track percentiles to understand the full distribution. The p50 or median represents the typical user experience, showing what half your users encounter. The p95 indicates what 95% of users experience, revealing problems that affect a significant minority. The p99 represents the worst case for most users, catching issues that still affect enough people to matter. Even p999 or extreme outliers remain important since they indicate severe problems that some users will encounter.
Factors Affecting Latency
Model characteristics significantly impact latency. Larger models with more parameters are slower to run but may provide better results. Sequence length affects processing time—longer outputs take more time to generate token by token. Temperature settings may influence latency as higher temperatures sometimes require more computation. Different LLM providers offer different speed-quality tradeoffs, with some optimized for latency and others for capability.
System architecture introduces additional latency beyond model inference. Network round-trip time to external APIs adds delay. Pre-processing and post-processing steps around the core model call accumulate. Database calls to fetch context or user data take time. External API calls for RAG retrieval or tool usage can dominate total latency. Concurrency and load on shared resources affect response times under realistic conditions.
Latency Testing Patterns
Baseline performance testing establishes expected latency under ideal conditions with minimal load. This provides a reference point for detecting performance regressions. Regression testing catches when changes increase latency unacceptably, triggering alerts before degraded performance reaches production. Real-user monitoring in production reveals actual latency experienced by users under real-world conditions with varying network quality, geographic distribution, and usage patterns.
Optimizing Latency
Model selection offers the biggest latency improvements by choosing faster models when appropriate. Balance speed against capability—you might not need your most powerful model for every query. Streaming responses reduces perceived latency dramatically by showing partial results as they're generated rather than waiting for completion. Caching frequently requested information eliminates redundant computation, particularly valuable for common queries or stable content.
Best Practices
For monitoring and measurement, track percentiles rather than relying on averages that hide problems affecting significant user segments. Set alerts on p95 and p99 thresholds to catch performance degradation early. Monitor trends over time to detect gradual degradation before it becomes severe. Segment latency by scenario since different use cases have different requirements and bottlenecks.
For comprehensive testing, include latency checks in your CI/CD pipeline with performance regression tests that fail if latency increases beyond acceptable thresholds. Test under realistic load that simulates actual concurrent user patterns. Vary input sizes by testing with both short and long prompts since latency characteristics differ. Conduct geographic testing because latency varies significantly by physical distance to API endpoints and regional infrastructure quality.
For optimization, choose appropriate models by matching capability to task requirements rather than always using the largest model. Optimize prompts to be concise since shorter prompts process faster. Use streaming to reduce perceived latency even when total processing time remains the same. Cache aggressively for repeated queries or stable information. Implement async processing for non-critical operations that don't require immediate responses, allowing critical paths to remain fast.