👤 Builders and Small Teams

Know what your AI costs before it scales

For developers, founders and small AI teams. Price each step of a workflow, try cheaper routes and set limits before the bill grows.

Is this you?

You are building your first or second production AI workflow
You want cost per outcome before choosing a model
You put tools on a card, not through procurement

Priced on governed scope, not on consumption. You pay for the workloads AICost governs. No token markup, no share of your AI bill and no share of savings, so our price never grows because your usage does.

Plans

AICost Free

Individuals evaluating AICost

Decision engines on the web
MCP key with a monthly decision allowance
One saved workload
Cost, TCO and optimization output

Explore free

AICost Team

$299/mo

Small AI and platform teams

3 active workloads
MCP and API
All decision engines
Workflow economics and TCO
Optimization recommendations
Basic governance envelopes and policy output
History and evidence
5 users

Start Team

An illustrative workload, priced by AICost engines

What AICost changes in one AI workflow

Each step below is priced by its own AICost engine at the volume shown. The levers and the governance envelope are what AICost adds. Numbers refresh with model prices.

AI SDR for outbound prospecting and inbound lead qualification

3,000 qualified lead a month · 7 steps

RevOps loads a target list or segment (lapsed customers, past attendees, cold accounts), then Agent researches each account and contact, then Agent writes and sends personalized email sequences, or chats with website visitors, then Agent handles replies and books meetings on rep calendars.

Research, write and send, per lead $1,788 $536/mo ↓ 34% loop cap↓ 4% prompt caching↓ 6% context compaction↓ 6% model routing↓ 20% shorter prompts Agent Loop

Shorter prompts per email /mo Token Reduction

Cheap model for drafts, strong model for replies /mo Model Router

CRM, email and calendar connections $1,472/mo Tool Integrations

Brand and claims guardrails $7/mo ✓ Guardrail screening Safety Screening

Rep approves drafts before send $1,875 $1,000/mo ↓ 47% better grounding Human Review

Cost per booked meeting /mo AI ROI Quick Check

Without AICost

$5,142/mo

$1.71 per qualified lead

Spend shows up on next month's invoice
No per-run or daily ceiling on agents
Controls written down, not enforced
No evidence trail for audits

With AICost

$3,016/mo

$1.01 per qualified lead 41% lower cost

Monthly budget$600

Daily cap$26

Per-run ceiling$2.38

Turn cap per run16

Guardrail screening
Human review
Cost firewall at the gateway
Data-class model allowlist

Where the savings come from

📚 Research, write and send, per lead$1,788 → $536/mo Loop cap using the AICost Agent Loop engine −$601 (34%)
A ceiling on turns and tokens per agent run, so a stuck agent cannot run up the bill.; Prompt caching using the AICost Prompt Caching engine −$75 (4%)
Repeated instructions and documents are cached, so you pay less for the same prompt text. *; Context compaction using the AICost Context Trim engine −$107 (6%)
Long histories are trimmed each turn, so every call carries less text. *; Model routing using the AICost Model Router engine −$109 (6%)
Routine steps run on a cheaper model; critical steps stay on the primary model. *; Shorter prompts using the AICost Token Reduction engine −$359 (20%)
Trimmed prompts and tighter outputs cut tokens on every call.

🤖 Rep approves drafts before send$1,875 → $1,000/mo Better grounding using the AICost Human Review engine −$875 (47%)
Answers come only from your approved sources, so fewer cases go to a person. *

What it means against the AICost price: Monthly savings on this one workload cover the AICost Team fee 7.1 times.

Illustrative: one workload at the volume shown, priced from current public model prices. Your numbers come from your own workload. Open this workflow in the live demo →

Questions

Is Free really usable?

Yes. The engines and MCP key work on Free. Team adds saved workloads, policies, history and more users.

Can we upgrade later?

Yes, at any time. Saved work moves with you.

What counts as a workload?

One AI application or business process with its own outcome and governance boundary. Steps inside it, such as models, agents and tools, are not counted separately.

Do you charge a percentage of AI spend?

No. AICost is priced on the workloads it governs. There is no token markup and no share of savings.

Other paths

Talk to AICost

Bring one workload. We price it step by step and show what changes with AICost.

Book a free consult See the live demo

Vendor / Model	Field	Why it’s inferred
Anthropic — Claude Sonnet 4.6	`cachedInput`	Derived at 10% of input rate — Anthropic publishes 90% cache-hit discount on this tier.
Anthropic — Claude Sonnet 4.5	`cachedInput`	Derived at 10% of input rate; same 90% cache-hit convention as Sonnet 4.6.
Anthropic — Claude Sonnet 4.5	`batchInput`	Derived at 50% of standard input — Anthropic documents uniform 50% Batch discount.
Anthropic — Claude Sonnet 4.5	`batchOutput`	Derived at 50% of standard output — Anthropic documents uniform 50% Batch discount.
Anthropic — Claude Haiku 4.5	`cachedInput`	Derived at 10% of input rate — Anthropic 90% cache-hit discount convention.
OpenAI — GPT-5.4 Mini	`cachedInput`	Derived at 10% of input — OpenAI documents automatic 90% discount on cache hits across GPT-5.x tier.
OpenAI — GPT-5.4 Nano	`cachedInput`	Derived at 10% of input — OpenAI 90% cache-hit convention.
OpenAI — GPT-5.4 Nano	`batchInput`	Derived at 50% of input — OpenAI Batch API uniform 50% discount.
OpenAI — GPT-5.4 Nano	`batchOutput`	Derived at 50% of output — OpenAI Batch API uniform 50% discount.
OpenAI — GPT-5.4 Pro	`cachedInput`	Derived at 10% of input — OpenAI 90% cache-hit convention.
OpenAI — GPT-5.4 Pro	`batchInput`	Derived at 50% of input — OpenAI Batch API uniform 50% discount.
OpenAI — GPT-5.4 Pro	`batchOutput`	Derived at 50% of output — OpenAI Batch API uniform 50% discount.
OpenAI — GPT-5.2	`cachedInput`	Derived at 10% of input; no residency uplift.
OpenAI — GPT-5.2	`batchInput`	Derived at 50% of input.
OpenAI — GPT-5.2	`batchOutput`	Derived at 50% of output.
OpenAI — GPT-5	`cachedInput`	Derived at 10% of input.
OpenAI — GPT-5	`batchInput`	Derived at 50% of input.
OpenAI — GPT-5	`batchOutput`	Derived at 50% of output.
OpenAI — GPT-5.5 Pro	`cachedInput`	Derived at 10% of input — OpenAI does not publish a cached rate for *-pro models; using the family convention.
OpenAI — GPT-5.5 Pro	`batchInput`	Derived at 50% of input.
OpenAI — GPT-5.5 Pro	`batchOutput`	Derived at 50% of output.
OpenAI — GPT-5.2 Pro	`cachedInput`	Derived at 10% of input — pro-tier convention.
OpenAI — GPT-5.2 Pro	`batchInput`	Derived at 50% of input.
OpenAI — GPT-5.2 Pro	`batchOutput`	Derived at 50% of output.
OpenAI — GPT-5.1	`batchInput`	Derived at 50% of input.
OpenAI — GPT-5.1	`batchOutput`	Derived at 50% of output.
OpenAI — GPT-5 Pro	`batchInput`	Derived at 50% of input.
OpenAI — GPT-5 Pro	`batchOutput`	Derived at 50% of output.
OpenAI — GPT-5 Nano	`cachedInput`	Derived at 10% of input.
OpenAI — GPT-5 Nano	`batchInput`	Derived at 50% of input.
OpenAI — GPT-5 Nano	`batchOutput`	Derived at 50% of output.
Google — Gemini 3 Flash	`cachedInput`	Derived at 10% of input — Google caching discount convention ~90%.
Google — Gemini 3.1 Flash-Lite	`cachedInput`	Derived at 10% of input — Google caching convention.
Google — Gemini 3.1 Flash-Lite	`batchInput`	Derived at 50% of input — Google Batch API uniform 50% discount.
Google — Gemini 3.1 Flash-Lite	`batchOutput`	Derived at 50% of output — Google Batch API uniform 50% discount.
Google — Gemini 2.5 Pro	`cachedInput`	Derived at 10% of input.
Google — Gemini 2.5 Flash	`cachedInput`	Derived at 10% of input.
Google — Gemini 2.5 Flash-Lite	`cachedInput`	Derived at 10% of input — Google caching convention.
Google — Gemini 2.5 Flash-Lite	`batchInput`	Derived at 50% of input — Google Batch API uniform 50% discount.
Google — Gemini 2.5 Flash-Lite	`batchOutput`	Derived at 50% of output — Google Batch API uniform 50% discount.
xAI — Grok 4 (legacy)	`cachedInput`	Extrapolated at 25% of base.

Vendor / Model

Field

Why it’s inferred

Anthropic — Claude Sonnet 4.6

cachedInput

Derived at 10% of input rate — Anthropic publishes 90% cache-hit discount on this tier.

Anthropic — Claude Sonnet 4.5

cachedInput

Derived at 10% of input rate; same 90% cache-hit convention as Sonnet 4.6.

Anthropic — Claude Sonnet 4.5

batchInput

Derived at 50% of standard input — Anthropic documents uniform 50% Batch discount.

Anthropic — Claude Sonnet 4.5

batchOutput

Derived at 50% of standard output — Anthropic documents uniform 50% Batch discount.

Anthropic — Claude Haiku 4.5

cachedInput

Derived at 10% of input rate — Anthropic 90% cache-hit discount convention.

OpenAI — GPT-5.4 Mini

cachedInput

Derived at 10% of input — OpenAI documents automatic 90% discount on cache hits across GPT-5.x tier.

OpenAI — GPT-5.4 Nano

cachedInput

Derived at 10% of input — OpenAI 90% cache-hit convention.

OpenAI — GPT-5.4 Nano

batchInput

Derived at 50% of input — OpenAI Batch API uniform 50% discount.

OpenAI — GPT-5.4 Nano

batchOutput

Derived at 50% of output — OpenAI Batch API uniform 50% discount.

OpenAI — GPT-5.4 Pro

cachedInput

Derived at 10% of input — OpenAI 90% cache-hit convention.

OpenAI — GPT-5.4 Pro

batchInput

Derived at 50% of input — OpenAI Batch API uniform 50% discount.

OpenAI — GPT-5.4 Pro

batchOutput

Derived at 50% of output — OpenAI Batch API uniform 50% discount.

OpenAI — GPT-5.2

cachedInput

Derived at 10% of input; no residency uplift.

OpenAI — GPT-5.2

batchInput

Derived at 50% of input.

OpenAI — GPT-5.2

batchOutput

Derived at 50% of output.

OpenAI — GPT-5

cachedInput

Derived at 10% of input.

OpenAI — GPT-5

batchInput

Derived at 50% of input.

OpenAI — GPT-5

batchOutput

Derived at 50% of output.

OpenAI — GPT-5.5 Pro

cachedInput

Derived at 10% of input — OpenAI does not publish a cached rate for *-pro models; using the family convention.

OpenAI — GPT-5.5 Pro

batchInput

Derived at 50% of input.

OpenAI — GPT-5.5 Pro

batchOutput

Derived at 50% of output.

OpenAI — GPT-5.2 Pro

cachedInput

Derived at 10% of input — pro-tier convention.

OpenAI — GPT-5.2 Pro

batchInput

Derived at 50% of input.

OpenAI — GPT-5.2 Pro

batchOutput

Derived at 50% of output.

OpenAI — GPT-5.1

batchInput

Derived at 50% of input.

OpenAI — GPT-5.1

batchOutput

Derived at 50% of output.

OpenAI — GPT-5 Pro

batchInput

Derived at 50% of input.

OpenAI — GPT-5 Pro

batchOutput

Derived at 50% of output.

OpenAI — GPT-5 Nano

cachedInput

Derived at 10% of input.

OpenAI — GPT-5 Nano

batchInput

Derived at 50% of input.

OpenAI — GPT-5 Nano

batchOutput

Derived at 50% of output.

Google — Gemini 3 Flash

cachedInput

Derived at 10% of input — Google caching discount convention ~90%.

Google — Gemini 3.1 Flash-Lite

cachedInput

Derived at 10% of input — Google caching convention.

Google — Gemini 3.1 Flash-Lite

batchInput

Derived at 50% of input — Google Batch API uniform 50% discount.

Google — Gemini 3.1 Flash-Lite

batchOutput

Derived at 50% of output — Google Batch API uniform 50% discount.

Google — Gemini 2.5 Pro

cachedInput

Derived at 10% of input.

Google — Gemini 2.5 Flash

cachedInput

Derived at 10% of input.

Google — Gemini 2.5 Flash-Lite

cachedInput

Derived at 10% of input — Google caching convention.

Google — Gemini 2.5 Flash-Lite

batchInput

Derived at 50% of input — Google Batch API uniform 50% discount.

Google — Gemini 2.5 Flash-Lite

batchOutput

Derived at 50% of output — Google Batch API uniform 50% discount.

xAI — Grok 4 (legacy)

cachedInput

Extrapolated at 25% of base.

Know what your AI costs before it scales

Is this you?

Plans

AICost Free

AICost Team

What AICost changes in one AI workflow

AI SDR for outbound prospecting and inbound lead qualification

Questions

Other paths

AI-Native Companies

Enterprises

System Integrators and Consultants

MSPs

Talk to AICost

Methodology

Primary sources

Inferred values (marked with * in calculator tables)

Immediate steps you can take