Cost Optimization
Practical ways to lower the bill — token spend, model selection, and the hidden costs of running LLMs in production.
Anthropic Prompt Caching: How It Works + When to Use It
How Anthropic prompt caching works, what it costs to write and read the cache, and the conditions under which it cuts input token spend by up to 90%.
How to Cut LLM API Costs by 50% (4 Proven Methods)
Four proven techniques to reduce LLM API token spend in production — system prompt optimization, output controls, model routing, and prompt caching — without degrading output quality.
Hidden LLM Costs in Production and How to Monitor Them
The expensive parts of a production LLM application are rarely the obvious ones. Four hidden cost drivers — and the monitoring setup that catches them before they hit the invoice.
LiteLLM Router Setup: Fallback, Cost Routing & Model Pools
A step-by-step walkthrough of LiteLLM's Router class — defining model pools, configuring multi-provider fallbacks, enabling cost-based routing, and adding task-specific pools for math, code, and creative tasks.
LLM Inference Optimization: 5 Cost Patterns to Fix
Enterprise LLM costs don't grow linearly with usage — five organizational and architectural patterns compound on each other to multiply spend. Here's what they are and how to fix them.
LLM Routing: What It Is and How to Cut Costs With It
Does this request actually need your most expensive model? Semantic routing answers that question automatically — before the expensive model ever sees it.
Multi-Tier LLM Cache: Semantic, Prefix & Inference Layers
How to stack semantic, prefix, and inference-layer caches into a single pipeline that maximises hit rate while controlling cost and staleness.
LLM Cache Pre-Warming for Off-Peak Customer Service Bots
A case study on warming LLM caches with predictable queries overnight so support bots hit cache on the first message of the day instead of paying full inference cost.
Prompt Caching Break-Even: How Many Reads to Save Money?
The exact formula for calculating your prompt caching break-even point — factoring in write premium, read discount, TTL, and request volume — so you know whether caching is worth it before you turn it on.
RouteLLM vs vLLM Semantic Router: Which Should You Use?
RouteLLM, semantic-router, and vLLM each solve a different layer of the routing problem. Here's what each tool actually does, where they overlap, and how to choose.
Run LLMs Locally vs OpenAI API: Real Cost Comparison
Every team scaling an LLM product eventually runs this comparison. Most get it wrong because they only count compute. Here's the full cost stack — and the exact token volume where the math flips.