A firewall blocks the bad traffic and lets the good traffic through. AICost CostWall does that for AI spend.
Your teams keep shipping. The waste does not get through. Budgets, daily caps, spike detection and a kill switch, compiled into the gateway you already run. Governance, control and security sit on the money, and the AI innovation carries on.
AICost CostProof turns modeled AI savings into concrete evidence, so your next AI plan is built on what actually happened rather than what someone hoped.
Every way in, in order
Most teams stop at the first card. Each rung credits into the next: the $39 Blueprint comes off a Session, the Session fee comes off any plan. You never pay twice for the same ground.
Everything you need to see it, cut it and plan it.
No signup for the calculators.
Run the AICost decision engines on your own AI workload.
Two steps. Then decide.
mcp.aicost.ai to Claude, ChatGPT or CursorYour workload, priced and planned. Automated.
Credits in full toward a Session.
CostWall stops it. CostProof proves it.
Founding price. Locked for as long as you stay.
The cloud bill under the model bill, too.
Everything in Control, plus:
Agents that will not sit still. An AI bill nobody can attribute. A key that could end a quarter. AICost CostWall and AICost CostProof live in AICost Control and AICost Scale.
Talk to us
Three things AICost does not sell through a checkout page. Each one starts with a conversation about your AI estate rather than a plan picker.
$999/mo · 5 tenants · $99 each additional
from $2,500/mo
from $5,000 one-off
The line
Five AICost product lines are free to run on your own AI workloads. Two products are paid, because they hold your gateway in production and persist the evidence. All seven work across the same six AI cost pillars.
Model your AI costs for nothing. Pay when you want AICost to hold the brake in production and show the receipts afterwards.
| Family | Free | Control | Scale |
|---|---|---|---|
| AICost ClaritySo you understand why your bill is what it is. Bill diagnose, attribution, unit economics | ✓ | ✓ | ✓ |
| AICost OptimizeSo you pay less, measurably. Routing, caching, batching, agent loops, RAG | ✓ | ✓ | ✓ |
| AICost ForecastSo you know what you will pay before you ship. TCO, ROI, scale projection | ✓ | ✓ | ✓ |
| AICost RiskSo you do not pay later for what goes wrong. Hallucination liability, compliance exposure, PII leakage | ✓ | ✓ | ✓ |
| AICost LeverageSo you negotiate like the Fortune 500 does. Commitment discounts across Anthropic, OpenAI, Bedrock, Azure OpenAI, Vertex | ✓ | ✓ | ✓ |
| AICost CostWallBlocks the waste, lets the work through. Budgets, caps, anomaly, kill switch, enforced in your gateway | — | ✓ | ✓ |
| AICost CostProofProve it. Persistent ledger, modeled vs actual, realized savings | — | ✓ | ✓ |
| Cloud billing ingestAWS CUR, Azure Cost Management, GCP BigQuery | — | — | ✓ |
| WorkloadsConcurrent workloads under management | 1 | 5 | Unlimited |
There is no catch, and AICost is not going to insult you by saying “free forever”. Nothing is.
Here is what is actually true. The AI landscape moves every week. Teams are running AI workloads nobody has had to manage before: MLOps pipelines feeding models that retrain themselves, RAG over a corpus that changes daily, agent loops that call themselves and bill you for the privilege. AICost does not know that its AI cost decision engines work on your specific AI workload until you run them on it. So AICost would much rather you found that out for nothing.
MIT’s Media Lab studied 300 public enterprise AI deployments and found that 95% of GenAI pilots delivered no measurable P&L impact. The cause was not model quality. It was the learning gap: generic tools that never adapt to a real workflow. Tools brought in from outside succeeded about twice as often as internal builds. The GenAI Divide: State of AI in Business 2025. MIT Media Lab, NANDA initiative, July 2025.
AICost is not interested in being one of that 95%. So the order matters: your AI workload gets to production first. Once the AICost decision engines have it live and under control, you will want AICost CostWall holding your gateway and AICost CostProof persisting the evidence. That is when AICost makes money. If the free AI cost tools solve it and you never pay AICost a cent, that is still a good outcome. You won.
The real AICost price for the first customers. $99/mo instead of $199, $499/mo instead of $799. It is locked for as long as you stay subscribed. Not a trial, and not a discount that expires. When founding closes, new customers pay standard and you do not.
Buy an AICost Blueprint, the $39 comes off a Solution Session. Buy a Session, the fee comes back as a one-use code toward any AICost plan. AICost emails it to you and you enter it at checkout. Each rung credits into the next, so you never pay twice for the same ground.
Actually the opposite. AICost wants your teams as productive as they can possibly be: the right model for the right capability, without paying a surcharge for the privilege.
Runaway AI spend is not only a finance problem. It is the thing that gets an AI project cancelled before it ever reaches production. Every dollar burned on a retry loop nobody bounded, or on a frontier model doing work a small one does just as well, is a dollar not spent on the part of your AI that is actually innovative. Teams that lose control of the AI bill do not just overspend. They fail to make it to market.
There is a plainer reason to trust this. AICost does not earn a cent until your AI workload is live and under control. AICost CostWall and AICost CostProof only sell to teams who got to production. Your project shipping is the AICost business model, so AICost is aligned with you rather than with your invoice.
Mechanically: AICost CostWall caps what a workload can burn in a day, catches a spike the moment it starts, and gives you a kill switch. It does not queue your requests, sit in your hot path, or ask an engineer for permission. Under the cap nobody notices it is there. Over the cap you find out in seconds rather than on the invoice.
No. The AICost router takes a quality floor and a latency ceiling that you set. A cheaper model that fails your floor is never selected. AICost simply does not claim the saving. Nothing is auto-applied either: AICost emits a policy, a human reviews it, your gateway runs it. AICost never touches your AI traffic.
No. The AICost decision engines model from facts you supply. No cloud credentials, no API keys, no customer data. On AICost Scale you can send a billing export if you want reconciliation. That is a file you choose to send, not access you grant.
One AI workload with its own budget: a support bot, an agent pipeline, a summarization endpoint. Not one model, and not one API key. It is the unit you would want AICost CostWall to cap independently.
Any time, from your account. No call, no retention script. Your AICost CostProof history stays exportable for 30 days after.
All prices USD. How the integration works · For MSPs · Try 84 calculators free · [email protected]
Last-verified date is the most recent successful daily snapshot
(aicost_pricing_snapshots) or, when no snapshot exists yet,
the latest successful crawler run (aicost_crawler_runs).
10 of 10
vendors are currently verified. Aggregator services (TokenCost, AI Pricing Guru, etc.)
are not listed.
Derived from industry conventions, not directly published by the vendor. Typical conventions: cached input = 10% of base (90% off), Batch API = 50% of base (50% off).
| 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. |
| Google — Gemini 2.0 Flash | cachedInput |
Derived at 25% of input per Google 2.0 family caching rates. |
| Google — Gemini 2.0 Flash | batchInput |
Derived at 50% of input — Google Batch API uniform 50% discount. |
| Google — Gemini 2.0 Flash | batchOutput |
Derived at 50% of output — Google Batch API uniform 50% discount. |
| Google — Gemini 2.0 Flash-Lite | cachedInput |
Derived at 10% of input — Google caching convention. |
| Google — Gemini 2.0 Flash-Lite | batchInput |
Derived at 50% of input — Google Batch API uniform 50% discount. |
| Google — Gemini 2.0 Flash-Lite | batchOutput |
Derived at 50% of output — Google Batch API uniform 50% discount. |
| xAI — Grok 4 (legacy) | cachedInput |
Extrapolated at 25% of base. |
Pricing is cross-verified against the
LiteLLM community registry
when available. Daily snapshots are kept in aicost_pricing_snapshots;
every change is logged to aicost_price_changelog with old & new
values for full audit trail. Read the full methodology →