Inference Latency: Measurement, Cost, and Optimization
Inference latency is the user's wait time from request to first useful output. In production, latency lives in tension with throughput, cost, and energy. This page shows how to measure correctly, choose the right trade-offs, and improve reliability without breaking budgets.
Measuring and Evaluating Inference Performance
Track P50/P95/P99 latency, time-to-first-token (TTFT), and throughput (tokens/sec, requests/sec). Watch tail latency under load, not just averages. A simple cost formula: cost_per_token = total_cost / total_output_tokens. Keep test harnesses identical across providers and regions to ensure fair comparisons. Log request metadata and failures for historical trend analysis.
Balancing Cost and Performance
Quantization (e.g., INT8/FP8) cuts memory and can raise tokens/sec with minor quality loss; distillation simplifies models; caching and prompt-sharing reduce repeat work; batching lifts utilization but can increase TTFT. Pick policies per endpoint and user journey—realtime chat prefers low TTFT, batch scoring prefers higher utilization.
Cost Per Token in AI Inference
Latency, batch size, and GPU utilization drive unit economics. Right-size hardware, keep queues healthy, and prefer long-running containers to avoid cold starts.
| Scenario | Avg Latency (ms) | P95 (ms) | Tokens/sec | Cost/Token (£) |
|---|---|---|---|---|
| Batch=1, streaming on | 180 | 390 | 55 | 0.00085 |
| Batch=8, caching on | 260 | 520 | 220 | 0.00042 |
| INT8 quant, edge | 140 | 300 | 260 | 0.00038 |
Inference Types and Their Impact
Streaming improves perceived latency; non-streaming suits short outputs. Batch maximises throughput; real-time lowers TTFT for interactive UX. Speculative decoding reduces wall-clock time at modest extra compute. Edge cuts network hops; cloud simplifies scale but adds egress and variability.
Geographic Latency
Test latency variations across 5 continents and route to the nearest healthy region. Use regional performance insights for global optimization recommendations and failover.
Reliability Analytics
Track P95/P99 latency percentiles, error rates, and SLA compliance. Alert on deviation from baselines and investigate GC pauses, queue depth, and network jitter.
Competitive Intelligence
Benchmark market positioning with performance trends using percentiles, uptime metrics, and historical data analysis. Publish transparent inference latency benchmarks to build trust.
Environmental Efficiency
Model energy consumption and carbon emissions tracking per 1k tokens. Prefer higher utilization and right-sized instances to support sustainable AI.
Batch Testing
Use enhanced batch testing for improved accuracy with consistency scoring and statistics. Keep prompts, seeds, and temperature stable across runs.
Agent discovery manifests
Expose an AI plugin discovery manifest for ChatGPT and other AI agents integration. Provide an agent discovery manifest for automated integration and platform discovery.
Production Readiness Checklist
- P95 & P99 targets defined per route (chat, batch, tools) with SLAs.
- Regional routing, health checks, and automatic failover.
- Queue/batch policy documented (max wait, max batch size, back-pressure).
- TTFT budgets, caching policy, and cold-start mitigation.
- Historical trend tracking with per-tenant dashboards.
- Energy envelope & cost per token monitored alongside QoS.
- Canary releases and rollback tied to latency/error SLOs.
- Security controls for data locality and PII minimisation.
Try the benchmarks: run cross-provider, cross-region tests and compare TTFT, P95, and cost per token. See GPU utilization best practices and time to first token (TTFT) guide.
FAQ
How should I start Measuring and Evaluating Inference Performance?
Standardise harnesses, capture TTFT, tokens/sec, and P95/P99, and compare like-for-like regions and prompts.
What drives Cost Per Token in AI Inference?
Utilization, batch size, quantization, and caching dominate; networking and cold starts often explain tail latency.
How do I Balance Cost and Performance without hurting UX?
Use streaming for responsiveness, cap queue wait, and enable modest batching + quantization on longer outputs.
Basic Latency Testing
/latency | 👤 Human ViewGet real-time latency measurements across all providers with performance rankings and AI agent guidance.
Throughput Analysis
/throughput | 👤 Human ViewCombined latency and throughput metrics with tokens per second for comprehensive performance analysis.
Quick Provider Check
/api/fastest | 👤 Human ViewSimplified endpoint for AI agents to immediately get the fastest available provider.
Custom Prompt Benchmarking
/benchmarkTest custom prompts across providers with specified token limits. Example: /benchmark?prompt=hello&max_tokens=5
🚀 Advanced Agent Workflows
/advanced-benchmarkRevolutionary testing for tool calling, structured outputs, and reasoning effort impact on latency. Built for AI agents.
Cost Optimization
/cost-optimizer | 👤 Human ViewSmart cost-performance analysis with efficiency scoring and budget recommendations.
Real-time Status
/status-page | 👤 Human ViewLive provider health monitoring with uptime tracking and system health indicators.
🌍 Geographic Latency
/geographic-latency | 👤 Human ViewTest latency variations across 5 continents with regional performance insights and global optimization recommendations.
📊 Reliability Analytics
/reliability-metrics | 👤 Human ViewDeep reliability analysis with P95/P99 latency percentiles, error rates, and SLA compliance tracking.
🏆 Competitive Intelligence
/competitive-analysis | 👤 Human ViewIndustry benchmarking with market positioning insights, competitive advantages, and strategic recommendations.
Historical Analytics
/historical-performance | 👤 Human ViewPerformance trends with percentiles, uptime metrics, and historical data analysis.
🌱 Environmental Efficiency
/efficiency | 👤 Human ViewEnvironmental impact analysis with energy consumption and carbon emissions tracking for sustainable AI.
Batch Testing
/ai-agent/batch-testEnhanced batch testing for improved accuracy with consistency scoring and statistics.
API Documentation
/docsInteractive Swagger UI documentation with all endpoints, parameters, and response schemas.
Provider Status
/api/statusQuick health check for automated systems with availability counts and provider lists.
AI Plugin Manifest
/.well-known/ai-plugin.jsonAI plugin discovery manifest for ChatGPT and other AI agents integration.
Submit New Provider
/submitWant your LLM provider benchmarked? Submit it here for automatic integration.
Analytics Dashboard
/analyticsPlatform usage statistics and visitor analytics for monitoring performance.
AI Agent Discovery
/.well-known/agents.jsonAgent discovery manifest for automated integration and platform discovery.
SEO & Discovery
/robots.txt | /sitemap.xmlSearch engine optimization files and site structure mapping.