⚡ LLM Cost & Latency Optimization
Learn LLM Cost & Latency Optimization on AI4AI — short, hands-on lessons with live AI runs, at three reading levels (beginner to expert). Free to start.
What you'll learn
- Where Your Money Goes: Tokens, Context, and LLM Economics (7 min) — ⚡ LLM APIs charge by the token — a subword unit averaging roughly 0.75 words in English (4 characters). Every API call …
- Picking the Right Model: Capability vs Cost vs Latency (7 min) — Every major AI provider offers a tiered model lineup — for example, GPT-4o mini vs GPT-4o, Claude Haiku vs Claude Sonne…
- Prompt Caching: Pay Once for Repeated Context (7 min) — Prompt caching lets you designate a static portion of your prompt — system instructions, a large document, few-shot exa…
- Model Routing & Cascades: Cheap First, Escalate When Needed (8 min) — ⚡ Model routing sends each incoming request to the most cost-effective model that can handle it reliably. A cascade is …
- Batching, Streaming & Concurrency: Squeezing Out Throughput (8 min) — Three levers control how much useful work your LLM system does per second and per dollar. **Batching** groups multiple …
- Measure First, Cut Second: Reducing LLM Cost Without Hurting Quality (8 min) — ⚡ LLM cost is driven by three levers: token count (input + output), model tier (e.g., GPT-4o vs. GPT-4o-mini), and call…
Lessons
Where Your Money Goes: Tokens, Context, and LLM Economics
⚡ LLM APIs charge by the token — a subword unit averaging roughly 0.75 words in English (4 characters). Every API call has two token counts: input tokens (your prompt plus any conversation history) and output tokens (the model's response). Providers price these separately; outpu…
Picking the Right Model: Capability vs Cost vs Latency
Every major AI provider offers a tiered model lineup — for example, GPT-4o mini vs GPT-4o, Claude Haiku vs Claude Sonnet vs Claude Opus, or Gemini Flash vs Gemini Pro. These tiers differ on three axes that trade off against each other: capability (reasoning depth, instruction-fo…
Prompt Caching: Pay Once for Repeated Context
Prompt caching lets you designate a static portion of your prompt — system instructions, a large document, few-shot examples — as a reusable prefix. The model processes and stores that prefix on the first call. Subsequent calls that share the identical prefix skip reprocessing i…
Model Routing & Cascades: Cheap First, Escalate When Needed
⚡ Model routing sends each incoming request to the most cost-effective model that can handle it reliably. A cascade is a specific routing pattern: try the cheapest model first; if it fails a quality gate, retry with a more capable (and more expensive) model. In practice, a quali…
Batching, Streaming & Concurrency: Squeezing Out Throughput
Three levers control how much useful work your LLM system does per second and per dollar. **Batching** groups multiple independent requests into a single API call (or a single forward pass on a self-hosted model). OpenAI's Batch API, for example, accepts up to 50,000 requests in…
Measure First, Cut Second: Reducing LLM Cost Without Hurting Quality
⚡ LLM cost is driven by three levers: token count (input + output), model tier (e.g., GPT-4o vs. GPT-4o-mini), and call frequency. Cutting any lever saves money — but each change risks degrading output quality. The professional approach is to measure quality before and after eve…