TOOLRELIEF DECISION INTELLIGENCE

Calculator AI Tools
Software Intelligence
Decision
Use AI Subscription Waste Calculator & AI Spend Evidence Map to test the scenario described on this page from the inputs and assumptions that apply to your situation before taking the related action.
Evidence Basis
A modeled assessment based on the inputs, formulas, assumptions, thresholds, and decision signals documented by this tool.
Best Used For
Testing a scenario or estimating its magnitude before making a higher-cost or harder-to-reverse decision.
Decision Boundary
Calculator outputs are decision-support estimates based on the stated inputs and assumptions. They do not guarantee financial, operational, ranking, legal, or performance outcomes.
ToolRelief AI Spend Forensics

AI Subscription Waste Calculator & AI Spend Evidence Map

Map subscriptions, seats, AI add-ons, embedded capability and usage-based spend. Separate observed cost from review signals, then model changes without invented waste percentages.

Transparent mathNo waste score. Every review signal is traceable to entered evidence or a named external source.
01 — Build the spend map

Enter the economics you actually know.

One row can represent a subscription, paid seat pool, AI add-on, embedded capability, API workload or contracted AI cost. The tool does not infer your invoice from a market average.

Reporting currency: portfolio totals are only combined when each row can be expressed in the same reporting currency. If a row uses another currency, you supply the conversion rate; ToolRelief does not silently invent FX.

Add an AI cost line

Minimum credible inputs first; conditional fields appear only when relevant.

Use your invoice or actual recurring amount.
Used only to model a monthly run-rate.
1 native unit = X reporting units.
Use a consistent observation period.
If supplied, ToolRelief derives observed cost per unit. It does not infer business value.
Workflows / capabilities served
Substitution / change constraints (optional)
Fixed monthly equivalentDerived
—
Recurring fixed rows only; variable AI usage excluded.
Variable monthly run-rateModeled
—
Normalized from the observation periods you entered.
Review queueInterpretation
0 lines
No review-associated value yet.
Evidence gapsDerived
0
Rows missing owner, usage evidence or required FX conversion.

AI spend ledger

A local working register built only from the cost lines you enter.

02 — Evidence map

See concentration without calling it duplication.

The coverage map shows where multiple entered products touch the same workflow and where an included capability sits beside paid AI. It is a review map, not a substitute recommendation engine.

Key boundary: capability overlap is not functional equivalence. Data controls, integrations, output quality, workflow dependency and commercial terms can all make two apparently similar products non-substitutable.

Capability coverage map

Paid coverage is blue; included/embedded coverage is teal.

Derived from your rows

Review queue

Transparent triggers; no hidden weighting.

Interpretation
03 — Scenario workbench

Model a change. Do not pretend it already happened.

Choose an action for a specific cost line. Target economics are user-controlled scenarios; the resulting difference is not guaranteed savings.

Variable/API rows use a monthly planning run-rate. Fixed subscriptions use a monthly-equivalent recurring value. The summary keeps those concepts visible rather than presenting a fictional invoice total.
04 — Reference intelligence

Real source context, kept separate from your company data.

These references explain why the tool supports multiple AI billing models. Published prices are contextual evidence only and are never substituted for your invoice.

Source review: September 19, 2026. Vendor pricing and plan structures change. Use the linked primary source to verify any current commercial term before acting.
Primary vendor

OpenAI Business

External

OpenAI publishes Standard and Premium Business seat types with different annual/monthly prices, demonstrating that one workspace can contain multiple seat economics.

Reviewed: 2026-09-19Context: US business list pricingBoundary: Your invoice and negotiated terms take precedence.
Open primary source ↗
Primary vendor

GitHub Copilot

External

GitHub documents organization plans that combine per-seat pricing with pooled AI credits and usage beyond the included pool, a concrete hybrid seat + usage model.

Reviewed: 2026-09-19Population: organizations / enterprisesBoundary: Plan rules can change; verify current docs.
Open primary source ↗
Primary vendor

Google Workspace + Gemini

External

Google Workspace pricing describes Gemini AI capability inside Workspace plans, illustrating why an AI capability can exist without a separately identifiable incremental AI subscription.

Reviewed: 2026-09-19Context: Workspace plan structureBoundary: Included functionality varies by plan and date.
Open primary source ↗
Primary vendor

Microsoft 365 Copilot

External

Microsoft publishes standalone Copilot Business pricing as well as Microsoft 365 bundles with Copilot, supporting the distinction between add-on AI and bundled AI economics.

Reviewed: 2026-09-19Context: US business offeringsBoundary: Promotions, taxes and regional pricing can differ.
Open primary source ↗
Primary vendor

Anthropic Claude API

External

Anthropic documents model-specific input/output token pricing, caching and batch economics. This supports treating API spend as usage economics rather than a conventional seat subscription.

Reviewed: 2026-09-19Context: Claude API pricingBoundary: Model and regional pricing can change.
Open primary source ↗
Open standard

FOCUS Specification 1.4

External

FOCUS provides structured examples for seat licenses, SaaS subscriptions, spend commitments and API credit packs, reinforcing the need to keep commitment structure separate from simple monthly spend.

Reviewed: 2026-09-19Population: technology cost and usage dataBoundary: FOCUS examples illustrate data modeling; they do not classify your contract for you.
Open standard ↗
Institutional methodology

FinOps Unit Economics

External

FinOps Foundation documents unit-economics metrics such as cost per API call = total API costs ÷ API calls, while stressing normalization and context across technology categories.

Reviewed: 2026-09-19Use: variable AI cost normalizationBoundary: Unit cost is not itself proof of business value.
Open methodology ↗
Open-source cross-check

LiteLLM model cost map

External

The open-source LiteLLM project maintains a machine-readable model pricing/context map. ToolRelief treats it as a useful coverage and cross-check resource, not as primary vendor truth.

Reviewed: 2026-09-19Type: community-maintained registryBoundary: Validate important prices against provider documentation.
Open registry ↗
05 — Methodology

Every number has a category and a boundary.

The tool intentionally separates user-provided facts, derived normalization, modeled scenarios, external source context and ToolRelief interpretation.

Derived

Fixed monthly equivalent

monthly amount = amount ÷ billing interval

Annual values divide by 12; quarterly by 3; weekly uses 52 ÷ 12. This is normalization, not a claim about cash timing.

Modeled

Variable monthly run-rate

observed spend × 30.4375 ÷ observed days

A planning run-rate only. It can change materially with model mix, workloads, caching, region and usage.

Derived

Seat capacity exposure

(paid seats − active seats) × derived unit seat cost

Only calculated for seat-priced rows with enough evidence. Exposure does not equal immediately realizable savings.

Derived

Observed unit cost

observed variable spend ÷ observed units

Shown only when you provide the denominator. It describes cost intensity, not business outcome or ROI.

!

Decision boundary

ToolRelief can normalize entered spend, expose evidence gaps, show workflow concentration, calculate seat-capacity exposure where mathematically valid and model explicit changes. It cannot prove that low usage means low value, that overlapping capability is substitutable, that a contract can be reduced immediately, or that a modeled difference will become realized savings.

06 — Portable decision output

Turn the page into a working artifact.

Export the portfolio register or carry a concise decision brief into a budget, procurement or stack-review conversation.

AI spend review brief

Live summary of the entered portfolio; no external market average is mixed into these values.

Cost lines0
Review candidates0
Overlap workflows0
Fixed monthly eq.—
Variable run-rate—
Modeled difference—

Export

CSV preserves row-level evidence. The copied/printed brief separates current data from modeled changes.

07 — Contextual next decision

Follow the evidence, not a link dump.

Routes below update with the portfolio signals you enter and are capped at four.

AI Spend Forensics is a ToolRelief decision-support instrument. Published price references are contextual and time-stamped; your own invoices, contracts, usage records and organizational requirements remain the controlling evidence for company-specific decisions.