Fat Context of RAG Drives Inference Cost Sky-high. Here’s How to Save Big on API Calls.
Last Updated on August 29, 2025 by Editorial Team
Author(s): Thuwarakesh Murallie
Originally published on Towards AI.
Try this before you dump your RAG prototype.
What prevents you from deploying that RAG app you developed?
The article discusses how to mitigate high inference costs in Retrieval-Augmented Generation (RAG) applications by employing context compression techniques. It emphasizes that developers do not need to abandon their RAG projects due to API costs, as there are effective strategies to maintain response quality while significantly reducing input tokens. Specifically, it introduces two compression methods from recent research: extractive and abstractive compression, which aim to selectively condense context without losing essential information, ultimately leading to cost savings and improved performance.
Read the full blog for free on Medium.
Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.
Published via Towards AI
Towards AI Academy
We Build Enterprise-Grade AI. We'll Teach You to Master It Too.
15 engineers. 100,000+ students. Towards AI Academy teaches what actually survives production.
Start free — no commitment:
→ 6-Day Agentic AI Engineering Email Guide — one practical lesson per day
→ Agents Architecture Cheatsheet — 3 years of architecture decisions in 6 pages
Our courses:
→ AI Engineering Certification — 90+ lessons from project selection to deployed product. The most comprehensive practical LLM course out there.
→ Agent Engineering Course — Hands on with production agent architectures, memory, routing, and eval frameworks — built from real enterprise engagements.
→ AI for Work — Understand, evaluate, and apply AI for complex work tasks.
Note: Article content contains the views of the contributing authors and not Towards AI.
Related posts
Recent Posts
