AC
Agent Cost ComparatorScenario model, not a live quote

Transparent agent economics

Compare the whole agent cost, not just tokens.

Model cash, failure recovery, developer time, capacity, and policy uncertainty across five deployment paths. Every number below is your scenario input.

Lowest modeled TCORaw API
Monthly successful runs0
Best cost / success$0.00
Capacity statusSet workload

Workload model

Describe one repeatable task

All values editable

Comparison lanes

Make unlike billing models comparable

Monthly TCO

Cash plus estimated labor

Formula audit

See every multiplier

Sensitivity

Low, base, and high utilization

Capacity planner

Will the lane keep up?

Decision weights

What matters besides price?

Operational checks

Turn unknowns into explicit work

Synthetic code-review example

Four model calls, eight tool steps, one successful review

This worked scenario demonstrates the calculation only. It does not reproduce or validate any provider claim from the source post.

Caveated recommendation

Best fit for this scenario

A scenario cost comparison needs explicit workload and labor assumptions

Read the explanation

The saved comparison page starts with forty runs per day and twenty two billing days, giving eight hundred eighty planned runs per month. At one pixel per ten runs the monthly bar measures eighty eight. Its displayed workload inputs also include four model calls and eight tool calls per run, yielding three thousand five hundred twenty model-call slots and seven thousand forty tool-call slots before any retries. At one pixel per fifty call slots those bars measure seventy point four and one hundred forty point eight. These are products of visible scenario inputs, not actual execution counts, current provider limits or validated billing. A retry-rate field must be interpreted through its real implementation before estimating completed runs. The visible recovery inputs assign nine developer minutes per failed run and ninety currency units per hour. Nine divided by sixty times ninety gives thirteen point five assigned labor units per failure. Ten such failures would contribute one hundred thirty five, and one hundred would contribute one thousand three hundred fifty. At one pixel per ten currency units those comparison bars measure thirteen point five and one hundred thirty five. These examples demonstrate dimensional arithmetic rather than the missing calculator implementation or actual labor records. Failure probability, retries, caching, provider prices and automation terms all remain separate assumptions; no quoted platform price or recommended purchase is established here. The capacity inputs show six concurrent runs and six minutes per run. Under the simplifying assumption of continuously occupied independent slots, six divided by six gives one completed run per minute, or sixty per hour. At two pixels per completion count, the per-minute and per-hour bars measure two and one hundred twenty. Queue delay, actual request admission and failure recovery can reduce that ideal rate. The saved HTML references application code and styles that are absent locally, leaving lane tables and calculated output uninitialized. Native numeric inputs can still change, but export and print buttons lack their application handlers. This local video describes visible assumptions without claiming a computed total cost, operational capacity benchmark or working public calculator.

Agent Economics: Modeling TCO Across Infrastructure Paths

Why does total cost of ownership for an autonomous agent often diverge significantly from simple per-token API pricing?

Comparing LLM agent backends solely on input and output token rates overlooks failure recovery, tool orchestration, and human engineering intervention. In autonomous agent loops, a single task run often triggers multiple model round-trips and tool executions. When steps fail or require retries, human engineering triage labor rapidly dwarfs the underlying compute bill. This comparator models full lifecycle economics across five deployment architectures—raw API calling, coding-agent CLI tooling, flat subscriptions, managed agents, and self-hosted instances—combining direct cash fees with configurable labor remediation costs.

All pricing figures, prompt cache discounts, and developer recovery hours are interactive scenario parameters rather than live vendor quotes or contracted service-level guarantees. Self-hosted and subscription costs do not dynamically reflect cluster autoscaling, cold starts, unannounced provider price changes, or variable model evaluation accuracy.

Try a worked example

Under the default workload of 40 runs per day across 22 monthly billing days (880 total runs), Raw API costs approximately $153 in model token fees. However, with a 12% retry rate and 9 minutes of developer triage per failure at $90/hour, developer remediation adds over $3,300 in monthly labor, driving total TCO to roughly $3,462. When you click the 'High' button in the Sensitivity section, the scenario engine applies a 175% utilization multiplier (1.75× baseline runs), scaling Raw API cash costs to approximately $267 and labor triage to $5,790, highlighting how failure rates compound total operating expenses as volume grows.

Token Pricing vs. Total Cost of Ownership

Evaluations that measure only raw token pricing assume 100% deterministic first-pass execution. Autonomous agents execute iterative planning loops where multiple LLM calls and external environment tool steps occur per task. Retries, context window expansion, and failure triage mean engineering time spent reviewing agent breakages frequently represents the dominant operational cost driver.

Architectural Tradeoffs Across Deployment Lanes

Raw APIs offer minimal fixed overhead but leave retry logic, error handling, and prompt caching implementation to the developer. Managed agent platforms and subscription CLIs trade higher base cash fees or per-seat licenses for integrated scaffolding, retry strategies, and bounded operational labor. Self-hosting requires dedicated GPU provisioning and maintenance, amortizing best only under high, sustained utilization.

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