Engineering
Implementation guides, cost breakdowns, and technical deep-dives on AI infrastructure optimization.
7 posts
Semantic Caching for LLM APIs: Complete Implementation Guide (Save 40-70%)
Exact-match caching misses 95% of duplicate queries. Semantic caching catches them. Here's how to implement it and what hit rates to expect in production.
What is the Model Tax? The Hidden Cost Every AI Team Pays
The Model Tax is the invisible cost of sending every LLM request to GPT-4o. 80% of your prompts don't need a premium model. Here's what it's costing you — and how to eliminate it.
How to Reduce OpenAI API Costs by 60-80% with Model Routing (Step-by-Step)
A practical tutorial showing how to implement model routing that sends simple prompts to cheap models and complex ones to GPT-4o. Before/after cost data included.
GPT-4 vs Cheaper Models: When to Use Each (And How to Automate It)
GPT-4 is 83x more expensive than Llama 3.1 8B. For most tasks, the cheaper model is good enough. Here's a framework for deciding — and automating the decision.
How to Reduce OpenAI API Costs: A Complete Guide for 2025
Most teams overpay for AI by 70–97%. This guide covers every technique to cut your OpenAI API bill without sacrificing output quality.
LiteLLM Alternatives for Production AI Gateways in 2026
LiteLLM got you started — but production demands more. We break down the best LiteLLM alternatives for teams that have outgrown the open-source proxy and need reliability, cost control, and observability at scale.
The Hidden "Model Tax": How Model Cascading Cuts Your LLM Bill by 80%
Every prompt you send to GPT-4o that could have been handled by a $0.06/M token model is a "model tax" you are silently paying. Here is how model cascading eliminates it — with real benchmarks.