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AI Agent ROI Calculator

Build vs buy cost, token economics, and 3-year TCO for production AI agents.

Estimate the real cost of building AI agents in-house: engineering headcount, timeline delay, maintenance, governance, and model usage. Then compare that cost against a platform approach built for regulated workflows.

Start calculating Read Build vs Buy

Default scenario

$6.8M estimated 3-year internal TCO
78% estimated token-cost reduction with MightyBot
What does an AI agent ROI calculator measure?

An AI agent ROI calculator estimates internal build cost, model and token spend, engineering headcount, implementation timeline, maintenance, and governance costs, then compares the total cost of ownership against buying an agent platform.

Calculator

Run your build-vs-buy scenario

No sign-up required. Results update as you change assumptions.

Workflow volume
Cases, documents, reviews, or decisions per month.
Minutes of human review, validation, and handoff.
Used to estimate the manual-work baseline in U.S. dollars.
Internal build team
Senior engineers, architects, data/ML, and platform support.
Salary, benefits, taxes, recruiting, equipment, and management load in U.S. dollars.
Months before the first regulated workflow is production-ready.
FTE for evals, monitoring, integrations, model upgrades, and support.
Architecture and token usage
Common prototype pattern with repeated context replay, retries, and validation loops.
Architecture default. Includes extraction, retrieval, policy checks, validation, and exception handling.
Architecture default. Includes prompt, context, retrieved evidence, tool results, and output.
Blended input/output estimate.
Operating cost (optional)
Data prep, security review, observability, compliance, and integrations.
Cloud, monitoring, eval tooling, queues, storage, and support systems in U.S. dollars.

Methodology

What the model includes

The calculator separates prototype cost from production cost. It includes engineering capacity, setup work, ongoing platform operations, model usage, retry behavior, and the cost of running a less efficient agent architecture at production volume.

Build cost Engineering FTE x loaded cost x timeline, plus setup, integrations, security, compliance, and tooling.
Token cost Monthly volume x model-backed steps x tokens per step x model cost x architecture and retry multipliers.
Platform path Implementation estimate, annual platform estimate, and MightyBot's baked-in token-efficiency planning assumption.

Architecture assumptions

The dropdown changes the economics

Agent architecture is not cosmetic. This dropdown models the current or in-house solution you are comparing against MightyBot. Sequential agents, RPA plus LLM steps, custom multi-agent systems, and deterministic workflows have different token, retry, observability, and maintenance profiles.

ReAct / sequential prompt-chain agent Common prototype pattern with repeated context replay, retries, and validation loops.
RPA plus LLM workflow Automation workflow with LLM steps layered onto recipes, bots, or task flows.
Custom multi-agent framework Engineer-built orchestration with agent roles, tool use, memory, and observability.
Deterministic workflow plus LLM calls Stronger internal-build option with explicit logic and model calls where needed.

Citations

Cost evidence behind the assumptions

The calculator defaults are planning assumptions, not a claim that every workflow behaves exactly this way. They are grounded in published agent architecture research, production framework documentation, and independent AI cost analysis. Replace these defaults with your trace data when you have it.

Architecture Calculator default Why the cost profile changes
ReAct / sequential prompt-chain agent 8 model steps, 6,500 tokens per step, 1.35 retry factor ReAct-style systems interleave reasoning, tool actions, observations, and follow-up reasoning. That loop tends to replay instructions, context, and observations across multiple model calls. ReAct paper · LangChain agent docs
RPA plus LLM workflow 6 model steps, 4,500 tokens per step, 1.25 retry factor Workflow systems usually have more fixed control flow than open-ended agents, but LLM steps still add extraction, classification, review, and exception loops. This is modeled below ReAct and multi-agent systems, but above deterministic compiled workflows. UiPath agents and workflows
Custom multi-agent framework 10 model steps, 8,500 tokens per step, 1.45 retry factor Multi-agent systems coordinate separate agent roles, tools, messages, and handoffs. That can improve capability, but it usually increases prompt volume, state carried forward, observability work, and validation paths. AutoGen framework docs · Dynamic reasoning cost study
Deterministic workflow plus LLM calls 5 model steps, 4,000 tokens per step, 1.15 retry factor Planning, DAG, and compiled execution patterns reduce repeated model calls and repeated context replay. ReWOO reports 5x token efficiency on HotpotQA, and LLMCompiler reports up to 6.7x cost savings versus ReAct in benchmarked function-calling tasks. ReWOO paper · LLMCompiler paper
Deloitte: token economics affect TCO Deloitte argues that AI economics increasingly need token-level TCO planning, FinOps discipline, and infrastructure strategy because usage can scale faster than unit prices fall. Read Deloitte analysis
HPCA 2026: dynamic reasoning increases cost variance A system-level study of AI agents found that multi-step reasoning introduces material resource usage, latency variance, and infrastructure-cost tradeoffs. Read the study
AgentDiet: trajectories create token waste AgentDiet found that reducing redundant agent trajectory context can cut input tokens by 39.9%-59.7% and total computational cost by 21.1%-35.9% in evaluated coding-agent tasks. Read the paper
AIIA and ClearML: hidden GenAI costs are under-modeled Their enterprise survey found many teams underestimated total ownership cost, including usage growth, infrastructure, API instability, and operational burden. Read the survey report

FAQ

AI Agent ROI Calculator FAQ

How do you calculate AI agent ROI?

AI agent ROI compares the expected savings and operational value from automation against the full cost of implementation and operation. For production agent systems, that should include engineering headcount, implementation timeline, model and token usage, cloud infrastructure, evaluation, observability, security, compliance, integrations, and maintenance.

How much does it cost to build an AI agent platform in-house?

The cost depends on team size, timeline, architecture, and workflow complexity. A regulated production build often requires 5 to 8 senior engineers for 12 to 18 months, plus ongoing maintenance, evaluation, monitoring, security, integration, and model-upgrade work.

Why are production AI agents more expensive than prototypes?

A prototype can be a prompt chain. Production needs policy control, document processing, source evidence, retries, exception routing, audit trails, access control, regression testing, observability, and model-upgrade governance. Those layers usually cost more than the initial demo.

How do token costs affect AI agent TCO?

Agent systems can call models many times for one workflow. Sequential architectures may replay context, tool definitions, documents, and validation steps repeatedly. That means token cost scales with workflow volume, retry rate, context size, and architecture choice.

When should a company buy instead of build AI agents?

Buying is usually stronger when the workflow is regulated, document-heavy, policy-bound, high volume, and not itself the company's core agent infrastructure IP. Building can make sense when the platform is core product IP and the organization is ready to own every production layer indefinitely.