# Prefix-Aware Routing and Caching Reduce Redundant LLM Inference Costs

DevFeed: [Prefix-Aware Routing and Caching Reduce Redundant LLM Inference Costs](<https://devfeed.tech/articles/the-inference-tax-how-prefix-aware-routing-eliminates-the-hidden-cost-of-llms-at-scale-19936.md>)

Original publisher: [Read original article](<https://www.digitalocean.com/blog/reduce-llm-inference-costs-prefix-caching>)

Author: Simon Mo, CEO of Inferact

Published: 2026-06-01T19:30:00Z

Content type: article

Language: en

Sources: [DigitalOcean](<https://devfeed.tech/sources/digitalocean.md>)

Topics: [Inference](<https://devfeed.tech/topics/inference.md>), [Caching](<https://devfeed.tech/topics/caching.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [vllm](<https://devfeed.tech/topics/vllm.md>), [networking](<https://devfeed.tech/topics/networking.md>), [Digital Ocean](<https://devfeed.tech/topics/digital-ocean.md>), [Serverless](<https://devfeed.tech/topics/serverless.md>)

Tags: [ai-ml](<https://devfeed.tech/tags/ai-ml.md>), [caching](<https://devfeed.tech/tags/caching.md>), [digitalocean](<https://devfeed.tech/tags/digitalocean.md>), [engineering](<https://devfeed.tech/tags/engineering.md>), [gateway](<https://devfeed.tech/tags/gateway.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [gpu-optimization](<https://devfeed.tech/tags/gpu-optimization.md>), [inference](<https://devfeed.tech/tags/inference.md>), [llms](<https://devfeed.tech/tags/llms.md>), [serverless](<https://devfeed.tech/tags/serverless.md>), [vllm](<https://devfeed.tech/tags/vllm.md>)

## AI overview

This article explains how repeated prompt prefixes create avoidable compute costs during LLM inference. It describes vLLM prefix caching and prefix-aware routing in DigitalOcean's inference gateway as ways to reduce redundant prefill work and GPU compute waste, including a claimed reduction of up to 4x on the same hardware.

## Source excerpt

Introduction Inference demand is growing fast, and it's only accelerating. By 2030, inference is expected to account for the majority of AI compute globally. But scaling inference isn't just a hardware problem. Most teams discover too late that a significant portion of their compute spend is avoidable, primarily because their systems are silently repeating work they have already done, recomputing the same prompt prefixes and system instructions over and over again. We've seen this from two vantage points. From the infrastructure layer, the cost curve becomes visible at scale with clusters that look busy but aren't efficiently utilized. From the engine layer, the picture is just as clear. Without the right caching and scheduling primitives, even a well-optimized model wastes cycles on redundant computation. The root cause is the same regardless of where you're standing. The system lacks the memory and coordination to recognize when it's already done the hard part. Fixing this requires work at every layer of the stack. DigitalOcean has invested in GPU optimization across multiple fronts, from vLLM parallelism and quantization tuning to hardware-level kernel work. But one technique has had an outsized impact on cost efficiency at scale: prefix-aware routing and caching. In this post, we walk through how vLLM enables advanced prefix caching, how DigitalOcean's inference gateway uses prefix awareness to make smarter routing decisions, and how we plan to make this available to everyone on Serverless Inference in the coming weeks. The Cost Cliff and the Hidden Culprit Inference now accounts for roughly 70% of total AI compute costs. For most teams, a significant share of that is avoidable. It's not due to hardware limits. Instead, it's because the system keeps recomputing work it has already done, also known as redundant prefill. Every LLM inference request has two distinct computational phases. The first phase is prefill, where the model processes the entire input sequenc