Free AI agent cost, ROI and readiness estimator

How it works

1. Pick your Category, then Workflow: the business area (Finance, ITSM, HR…) and the specific process to cost.
2. Choose the Path closest to your use case: a clean run, an exception, a rush.
3. Select the Model your agent will use. Live list prices are preloaded.
4. Your estimate shows automatically: the per-outcome cost, the step-by-step breakdown and the heat-mapped diagram. Press ▶ Watch the cost build any time to replay it as an animation and see where the cost lands.
5. Open Assumptions and tune them to your environment. Every number updates live.

What this is. A pre-deployment, usage-based cost model for agentic AI. It estimates the token cost of a single agent-produced outcome (one processed invoice, one triaged ticket, one reconciled account) and projects that unit cost to production volume, so finance, platform and engineering leaders can quantify usage-based spend before committing to a build or a vendor.

Purpose

Agentic AI is priced by consumption (tokens), not by seat, so spend scales with usage and cannot be forecast from a published list price alone. A single autonomous outcome is rarely one model call: the agent plans, invokes tools, retrieves context, and re-reads an expanding working context on every step. As a result an agentic workflow typically costs between 10 and 1,000 times a single chat completion. This tool makes that consumption explicit and converts it into figures a budget owner can defend, using transparent assumptions you control rather than a vendor headline rate.

Intended use

• Budgeting and forecasting. Convert a per-outcome unit cost into a monthly and annual run-rate at your projected volume, for budget submissions and capacity planning.
• Unit economics and the business case. Establish a defensible cost per transaction and test payback against the manual or legacy baseline the automation is intended to replace.
• Model and vendor selection. Compare cost per outcome across frontier models at current list prices, with a Custom rate for negotiated, committed-use or self-hosted pricing.
• Cost governance and sensitivity. Isolate the dominant cost drivers (planning loops, tool-result size, retrieval, sub-agents, context window) and quantify the effect of levers such as prompt caching and model tiering before they reach the invoice.
• Capacity and risk planning. Stress-test exception and peak-volume paths to size headroom and avoid consumption overruns after launch.

Where this fits in FinOps for AI

This estimator supports the FinOps Inform stage: planning, forecasting, budgeting and unit cost for agentic AI spend, before any tokens are billed. The assumption levers map directly to the two FinOps optimization paths. Usage optimization: planning loops, tool-result size, memory retrieval, sub-agents and the carried context set how many tokens each outcome consumes. Rate optimization: model choice, prompt caching, and a Custom rate for negotiated, committed-use or self-hosted pricing set what each token costs. Because it works from estimates rather than metered usage, it complements but does not replace a FinOps or observability platform reading your actual spend, which is what drives the Optimize and Govern phases once the workload is live.

AI spend is now a managed FinOps Scope (FinOps for AI, added to the 2025 FinOps Framework). In FinOps for AI the token is the atomic unit of cost, but cost per outcome is the truer business unit: optimizing tokens alone can raise cost per outcome through retries, errors and incomplete tasks, so the report keeps cost per outcome as the headline and shows cost per 1,000 tokens as the supporting metric. Beyond model choice, model routing or tiering (sending each request to the cheapest model that is good enough, which the Compare tab helps you find) is a core AI rate-optimization lever. Token cost is also only one part of AI total cost of ownership, which adds retrieval and vector databases, training and fine-tuning, GPU and inference infrastructure, orchestration, evaluation and guardrails, and human oversight.

Choosing a model that fits

Cost is only one axis: the goal is the cheapest model that clears your accuracy bar, not the cheapest model outright. To support that, each process shows a suggested tier in the Compare tab and beside the Model selector, derived from its stakes (how costly an error is, inferred from the category) and its complexity (planning, branching and tool use, inferred from the workflow graph). Higher stakes or complexity raise the suggestion. Models below it are not hidden — they are flagged “validate” so you test them deliberately rather than by accident.

Tiers group the models by general capability class (an editorial judgment, reviewed Jul 2026):

• Frontier — deepest reasoning, highest accuracy, highest cost: Claude Opus 4.8, GPT-5.5, GPT-5.4, Grok 4.3, GPT-4.1, Gemini 2.5 Pro, Mistral Large 3.
• Balanced — strong general-purpose at lower cost: Claude Sonnet 5, Claude Sonnet 4.6, GPT-4o, DeepSeek V4, Llama 4 Maverick.
• Efficient — fast and inexpensive, best for simpler high-volume tasks: Claude Haiku 4.5, Claude Fable 5, GPT-4.1 mini, GPT-4o mini, Gemini 2.5 Flash.

Treat the suggestion as a starting point, not a verdict. Model capabilities change frequently and real fit depends on your prompts, data and quality bar, so confirm any choice with your own evaluation on representative cases before relying on it. A Custom rate has no tier; validate it yourself.

Reasoning-class models. Some models (for example Grok 4.3) always run internal reasoning and bill those thinking tokens as output. The list price can look cheap while the tokens consumed per call run higher than a non-reasoning model. Represent this by raising Output / call (and, for heavy deliberation, Planning loops) when you price a reasoning model, and validate against your own traces.

About the example workflows

The workflows here are generic, representative examples of common business processes, not models of any specific organization. Your own process will differ in its steps, decision points and the balance of automated and human work. Select the workflow closest to your use case as a starting point, then tune the assumptions on the right (planning loops, tool calls, retrieval, sub-agents and volume) so the estimate reflects how your process actually runs.

What drives the cost

• Steps are typed by who does the work: A AI agent and H+A human-assisted-by-AI steps make model calls (tool-using steps make two); H human-only and R robot/system steps use no AI and cost no tokens.
• The big driver is context × calls: the running context grows as the agent works, and every call re-reads it. Planning loops, tool results, memory retrieval and sub-agents multiply this, which is why agentic workloads can cost 100–1000× a single chat turn.
• After a run, the diagram heat-maps each step with a cost bar scaled and coloured by spend, so you can see at a glance where the cost concentrates.
• In FinOps-for-AI terms these are the five consumption drivers of token economics: system-prompt overhead, context and memory, model selection, output length, and retry/orchestration overhead. They map one-to-one onto the assumptions on the right. What is AI tokenomics?

What the assumptions mean

• Base context: instructions/tools carried into every call; prompt caching re-uses it at ~10%.
• Prompt / Output: tokens added and generated per call.
• Context window cap: the model’s max input; running context is capped here.
• Planning loops: reflections/retries per step; each loop adds calls.
• Tool result & Memory retrieval: tokens tools return / RAG pulls in, added to context.
• Sub-agents: parallel agents on one outcome; multiplies calls.
• First-pass success: share of outcomes done right with no retry; cost per successful outcome = clean cost ÷ this rate, so a weaker model that retries can cost more. FinOps for AI calls this idea token yield: the share of consumption that ends in a usable result. Defaults by model tier — set yours from your own evals.
• Users × Runs/user: scales one-outcome cost to monthly & yearly totals.

Reading the numbers

• Cost / outcome: one full run. vs. one chat turn: the agentic multiplier.
• Cost / month and the report's scale table apply users × runs/user.
• Prompt caching cuts the repeated system/base context by 90%, often the single biggest lever.

Evidence and sources

• About 80% of companies use gen AI, yet a similar share report no material earnings impact, and fewer than 10% of use cases pass the pilot stage. (McKinsey, Seizing the Agentic AI Advantage, 2025)
• At scale, gen AI running costs can exceed the initial build investment, so running cost should be estimated before committing. (McKinsey, 2025)
• Nearly 3 in 4 organizations expect to use agentic AI within two years, but most cannot yet predict which use cases will yield the highest return. (Deloitte, State of AI in the Enterprise, 2026)
• Buyers increasingly expect each dollar of AI spend to fuel measurable outcomes. (PwC, 2026 AI Business Predictions)

Important

Token cost is one component of total AI cost. Full TCO also includes data integration, vector databases, governance, security, monitoring, evaluation, change management and human oversight. Figures here are modeled estimates, not a vendor quote.

Disclaimer

All figures are illustrative estimates generated from the assumptions you enter. They are not a quote, forecast, guarantee, or financial advice, and actual costs will differ. These are pre-deployment estimates, not metered actuals: validate them against your real usage and pair this tool with a FinOps or observability platform that reads actual spend for the Optimize and Govern phases. The estimates are provided “as is” without warranty of any kind; the provider of this tool accepts no liability for any decision or loss arising from reliance on them. Verify all figures independently before acting.

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