{"schema_version":"onlylabs.public_signal.v1","title":"DigitalOcean (GradientAI) Writing: The Inference Tax: How Prefix-Aware Routing Eliminates the Hidden Cost of LLMs at Scale","description":"DigitalOcean (GradientAI) writing signal with public source context, captured evidence pages, related signals, and category-scoped analysis context.","url":"https://onlylabs.fyi/signals/d88fd1d6-c8ff-4326-8a86-94bf45b96f3c","json_url":"https://onlylabs.fyi/signals/d88fd1d6-c8ff-4326-8a86-94bf45b96f3c/signal.json","generated_at":"2026-06-07T21:14:00.850777+00:00","org":{"slug":"digitalocean","name":"DigitalOcean (GradientAI)","category":"neocloud","category_label":"Neocloud","dossier_url":"https://onlylabs.fyi/labs/digitalocean","dossier_json_url":"https://onlylabs.fyi/labs/digitalocean/dossier.json"},"related_urls":{"signal":"https://onlylabs.fyi/signals/d88fd1d6-c8ff-4326-8a86-94bf45b96f3c","signal_json":"https://onlylabs.fyi/signals/d88fd1d6-c8ff-4326-8a86-94bf45b96f3c/signal.json","source":"https://www.digitalocean.com/blog/reduce-llm-inference-costs-prefix-caching","lab_dossier":"https://onlylabs.fyi/labs/digitalocean","lab_dossier_json":"https://onlylabs.fyi/labs/digitalocean/dossier.json","analysis":"https://onlylabs.fyi/analysis/digitalocean","analysis_json":"https://onlylabs.fyi/analysis/digitalocean/analysis.json","analysis_evidence_json":"https://onlylabs.fyi/analysis/digitalocean/evidence.json","category":"https://onlylabs.fyi/neoclouds","category_json":"https://onlylabs.fyi/neoclouds.json","category_feed":"https://onlylabs.fyi/neoclouds/feed.xml","category_signals_json":"https://onlylabs.fyi/signals.json?category=neocloud","topic":"https://onlylabs.fyi/topics/talking","topic_signals_json":"https://onlylabs.fyi/topics/talking/signals.json?category=neocloud","topic_feed":"https://onlylabs.fyi/topics/talking/feed.xml?category=neocloud","data_business":null},"answer_pack":{"answer":"DigitalOcean (GradientAI) published The Inference Tax: How Prefix-Aware Routing Eliminates the Hidden Cost of LLMs at Scale. 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Sitemap . Dark mode is coming soon. Engineering The Inference Tax: How Prefix-Aware Routing Eliminates the Hidden Cost of LLMs at Scale By Piyush Srivastava and Simon Mo, CEO of Inferact Updated: June 2, 2026 13 min read <- Back to blog home 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..."},"evidence_pages":[{"url":"https://www.digitalocean.com/blog/reduce-llm-inference-costs-prefix-caching","final_url":"https://www.digitalocean.com/blog/reduce-llm-inference-costs-prefix-caching","title":"The Inference Tax: How Prefix-Aware Routing Eliminates the Hidden Cost of LLMs at Scale","http_status":200,"content_type":"text/html; charset=utf-8","capture_method":"plain","fetched_at":"2026-06-07T21:14:00.850777+00:00","bytes":294874,"raw_path":"a5d7a20ccc09ad8cf92476a2651c78ebf327d65d6aef0d28e271a325988ea7cf.html","content_hash":"8988646268ea9b55a43e3b04ea8fd0b1132270d2b145992553c4909900d3de64","excerpt_chars":1200,"truncated":true,"excerpt":"The Inference Tax: How Prefix-Aware Routing Eliminates the Hidden Cost of LLMs at Scale | DigitalOcean © 2026 DigitalOcean, LLC. Sitemap . Dark mode is coming soon. Engineering The Inference Tax: How Prefix-Aware Routing Eliminates the Hidden Cost of LLMs at Scale By Piyush Srivastava and Simon Mo, CEO of Inferact Updated: June 2, 2026 13 min read <- Back to blog home 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. 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