AI Latency & Throughput Calculator
Size your infrastructure for any AI workload. Estimate latency, throughput, and cloud costs before you build.
Frequently Asked Questions
What does requests per second (RPS) mean for LLM APIs?▼
RPS measures how many inference requests your system sends to the model per second. For a chatbot serving 1,000 users simultaneously, each sending a message every 20 seconds, that's 50 RPS. Throughput directly affects infrastructure cost, queue depth, and perceived latency.
What is the difference between p50, p95, and p99 latency?▼
Percentile latencies tell you how fast your slowest requests are. p50 (median) means 50% of requests complete in that time or less. p95 is more important for user experience — 95% of requests complete in that time or less. p99 captures near-worst-case scenarios. Always design to p95 for interactive AI applications.
When should I self-host instead of using the OpenAI or Anthropic API?▼
Self-hosting makes financial sense when your token volume exceeds roughly $5,000–$10,000/month on managed APIs. It also makes sense when you need data privacy, custom fine-tuning, predictable latency, or freedom from rate limits. Below that threshold, managed APIs offer lower operational overhead and faster iteration.
How accurate are these infrastructure estimates?▼
These are directional estimates based on published GPU throughput benchmarks and public cloud pricing (May 2026). Real-world performance varies based on batch size, prompt length, system prompt reuse, KV-cache hit rate, and network conditions. Use these numbers for planning and order-of-magnitude decisions, not SLA commitments. Load-test your specific workload before production.