Operating AI without waste.
Practical writing on cutting LLM token costs, context pruning, model routing, and operating LLM gateways without waste.
How to reduce your LLM token costs
Six concrete techniques to cut LLM token spend — context pruning, prompt caching, model routing, and more — with the tradeoffs of each.
Read articleContext pruning for LLMs: techniques and tradeoffs
What context pruning actually means for LLM applications — the main techniques, when each helps, and how to avoid degrading quality.
Read articleHosted LLM gateway security: a vendor evaluation checklist
How to evaluate a hosted LLM gateway so your prompts, cache, and model keys stay inside your boundary — data residency, retention, BYOK, audit trails, eval fallbacks, and compliance.
Read articleLLM gateway vs API proxy: architecture, savings, and when to use each
An API proxy forwards requests; an LLM gateway transforms them. Learn the architectural difference, where the savings come from, and how to implement a gateway safely.
Read articleModel routing for LLMs: how to cut cost without cutting quality
A practical guide to model routing: classify requests, set quality floors, build fallback paths, and measure cost, latency, and quality together.
Read articleWant this running on your infrastructure?
Singular applies context pruning and eval-guarded fallback for you, through a single API base URL.