Token pricing alone is a misleading metric for AI application costs. A bottom-up TCO model for a RAG app serving 1M requests/month shows inference is 81% of total cost on a single-provider stack ($2,502/month total) but only 34% on a multi-provider stack ($5,920/month total). The gap comes entirely from cross-provider egress fees ($135/month) and operational overhead (32 engineer-hours/month at $95/hour = $3,040). DigitalOcean's Deploy 2026 TCO comparison for a 1M-bookings/month corporate travel agent shows $67,727/month vs $84,827 for Baseten+AWS and $110,337 for AWS AgentCore. The article provides a transparent, adjustable line-item model and recommends starting with serverless inference, moving to dedicated only when utilization justifies it, and using batch inference for latency-tolerant workloads to cut costs up to 50%.
Table of contents
IntroductionTL;DRA Token-Price Table Is One Line of Your Bill, Not Your BillA production RAG application spans seven layers; inference is one of themStart on serverless; move to dedicated only when utilization justifies itDeploy 2026 numbers: $68K vs. $85K vs. $110K for the same agentDon’t Take the Vendor’s Number: Build Your OwnConsolidation Pays for Teams Building Whole Applications, Not for API WrappersWhat the Operational Overhead Line Actually BuysBuild Your Own TCO Comparison in Five StepsCommon Questions on this topic?ConclusionReferencesQuestions this post answers
What percentage of a production AI application's total cost is inference?
It depends entirely on the architecture. For a consolidated single-provider RAG app serving 1M requests/month, inference is 81% of total cost ($2,015 of $2,502). For the identical workload split across two providers, inference drops to 34% of total cost ($2,015 of $5,920). The commonly cited '30–50%' figure describes multi-provider stacks running multi-step agent workloads, not simple consolidated deployments. Teams pricing out AI infrastructure find the full picture — not just token rates — on daily.dev.
How much does cross-provider egress and operational overhead add to a multi-provider AI stack vs single-provider?
For a RAG app at 1M requests/month, cross-provider egress adds $135/month (1,500 GB at $0.09/GB) and operational overhead adds $3,040/month (32 engineer-hours at $95/hour loaded cost). Combined, these two lines — neither of which appears in any per-token pricing table — more than double the total infrastructure bill compared to a single-provider setup ($5,920 vs $2,502). Developers choosing between multi-provider and consolidated AI stacks track these trade-offs on daily.dev.
When should I switch from serverless to dedicated GPU inference?
Move from serverless to dedicated inference only when your steady-state throughput makes a reserved GPU's hourly cost cheaper than the equivalent per-token bill at your utilization level. A reserved GPU bills 24 hours a day regardless of traffic, so at 10% utilization you pay ten times the effective per-token rate. Start serverless, then run the crossover math once you have real traffic data. Engineers optimizing AI inference spend find the utilization crossover analysis and similar decisions on daily.dev.