Cost Optimization
Practical ways to lower the bill — token spend, model selection, and the hidden costs of running LLMs in production.
RouteLLM vs vLLM Semantic Router: Which One Actually Cuts Costs?
RouteLLM and vLLM Semantic Router both reduce LLM costs - but they solve fundamentally different problems. Here's the benchmark data, the architecture breakdown, and the exact decision framework to pick the right one.
Run LLMs Locally vs OpenAI API: Real Cost Comparison
At 50M tokens/day, OpenAI costs $126,000/year. We model the full 36-month TCO across three usage tiers - hardware, electricity, ops labor - so you know exactly when self-hosting wins.
Prompt Caching Break-Even: How Many Reads to Save Money?
Prompt caching advertises a 90% discount on cache hits - but the write premium means a low cache hit rate costs you more than no caching at all. Here's the exact break-even math and the architecture decisions that determine whether you capture the savings.
Multi-Tier LLM Cache: Semantic, Prefix & Inference Layers
A multi-tier LLM cache stacks semantic, prefix, and inference caching layers to slash costs by up to 80% and cut latency by 78%. Here's exactly how each layer works and when to use them.
LLM Cache Pre-Warming for Off-Peak Customer Service Bots
Your customer service bot is recomputing the same system prompt thousands of times a day. LLM cache pre-warming stops that waste - and the benchmarks are staggering: 57x faster TTFT, 2x throughput, up to 90% cost reduction.
LLM Routing: What It Is and How to Cut Costs With It
LLM routing directs each query to the right model instead of defaulting to the most expensive one. Done right, it cuts inference costs by 40–85% while retaining 95%+ of output quality.
LLM Inference Optimization: 5 Cost Patterns to Fix
Your LLM inference bill is probably 3–5x higher than it needs to be. This guide breaks down the 5 structural cost patterns most engineering teams miss - and gives you the exact fixes, with real benchmarks, to close the gap fast.
LiteLLM Router Setup: Fallback, Cost Routing & Model Pools
A practical, code-first guide to setting up the LiteLLM Router in production - covering model pools, all six routing strategies, three fallback types, cost-based routing, and Redis-backed reliability.
Hidden LLM Costs in Production and How to Monitor Them
The number on the provider's pricing page is the floor, not the estimate. Retries, embeddings, guardrails, and observability overhead routinely double or triple your raw token bill - and only 22% of teams are tracking it.
How to Cut LLM API Costs by 50% (4 Proven Methods)
Most teams overpay for LLM APIs by 3–5x. Four proven methods - prompt caching, model routing, prompt compression, and async batching - can slash that bill by 50% or more without touching output quality.
Anthropic Prompt Caching: How It Works + When to Use It
Anthropic prompt caching lets you reuse a stable prefix of your Claude prompts instead of reprocessing them from scratch - slashing costs by up to 90% and cutting latency by more than 2x.