Your AI bill: measured, reduced, controlled.
OpenAI and Anthropic invoices land at month-end — no breakdown, no control. Cloudios meters every call at its real cost, spots when a leaner model is enough, when the cache saves you from paying twice, when flat-rate capacity beats pay-as-you-go — and blocks any budget overrun before the money leaves. Teams that apply these levers typically cut their AI bill by 30–60%.
Three steps, zero code rewrite.
The Cloudios meter slips between your applications and the AI providers — your tools, your keys and your code stay the same.
One line of configuration — 5 minutes
Your developer changes one line of configuration to route your AI calls through the Cloudios meter — reversible anytime, no code rewrite. Your OpenAI and Anthropic keys stay yours (encrypted, never re-displayed). Show this page to your developer: the exact line is in the fold-out below.
For your developer — the line to change
# OpenAI SDK — the one line that changes base_url = "https://trycloudios.com/api/ai-proxy/v1" # before: https://api.openai.com/v1 api_key = CLOUDIOS_KEY # key created in the dashboard # Anthropic SDK / Claude agents ANTHROPIC_BASE_URL = "https://trycloudios.com/api/ai-proxy"
Finally see who spends what
Every call is metered at its real cost, attributed to the team, project or agent that caused it, and checked against the provider’s real invoice — with the carbon of each call next to the euros.
Reduce, then lock
The savings show up priced in € on your real traffic: a leaner model at verified quality, answers served from cache, flat-rate capacity when it beats pay-as-you-go. Then you set blocking budgets — alerts first, hard refusal after — so it never drifts again.
The levers that cut the bill — and the lock that holds it.
Everything below is in the product today — not a roadmap.
The same answers, cheaper
Cloudios spots when a leaner model delivers answers of equivalent quality — verified on your calls, never assumed — and recommends it or routes automatically. Opt-in, you keep the veto; the price gap reaches 60–75% on the calls concerned.
Never pay twice for the same answer
Repeated requests are served from cache instead of going back to the provider, and cached tokens are tracked — savings shown in proven € on your traffic, not estimates.
Flat-rate or pay-as-you-go? The math is done
Like electricity, AI is paid per use or as reserved capacity. From your real traffic, Cloudios computes the point where reserved capacity (Azure PTU, Bedrock) becomes cheaper — never inventing a price that isn’t public.
Spend is refused before it leaves
When a project or agent exceeds its blocking budget, the call is refused before it reaches the provider (402 response, fail-closed) — even mid-way through a streaming response. The money never moves.
Every spend has an owner
One Cloudios key per team, project or agent: every call is attributed to whoever caused it — budgets, alerts and internal billing follow automatically.
Carbon next to euros, on every call
gCO₂e next to € on every call, per model and per region, plus a standardised carbon score (SCI for AI, Green Software Foundation) — no other FinOps platform exposes this today.
Gateway + reconciled invoice + outcome.
Portkey and LiteLLM are excellent gateways. Cloudios is one too — wired into the finance layer: real invoice, chargeback, outcome, carbon.
| Cloudios | Portkey | LiteLLM | |
|---|---|---|---|
| LLM proxy: caps, quotas, routing | Yes | Yes | Yes |
| Chargeback reconciled to the provider invoice | Built-in | — | — |
| Cost per business outcome | Built-in | — | — |
| Carbon per inference (SCI for AI) | Built-in | — | — |
| Compliance attestation on hash-chained audit | Built-in | — | — |
| Cloud FinOps on the same platform (9 clouds) | Yes | — | — |
Comparison is indicative, based on publicly available information. A “—” means we could not verify the capability. Trademarks belong to their owners.
The four objections, head-on.
Where does the “30–60%” come from?
From the levers themselves, not an invented case study. The published price gap between a frontier model and a leaner one reaches 60–75% on calls where verified quality is equivalent; an answer served from cache costs nothing at the provider; reserved capacity beats pay-as-you-go past a traffic threshold we compute on your data. How much of your bill each lever covers depends on your traffic — which is exactly what the “measure” phase establishes, before changing anything.
How much latency does the proxy add?
One extra HTTP hop and a budget check before the forward — streaming is then relayed as-is, byte for byte. On an LLM call, inference time dominates by far. We don’t publish an invented latency figure: measure on your own traffic — the proxy is enabled per key, team by team.
What if Cloudios goes down?
Your keys stay yours (BYOK): in an incident, your developer puts the original configuration line back and your calls resume immediately, straight to the provider, without depending on us. Our component status is public at /status — the same health checks as our internal monitoring.
Is this one more lock-in?
No, by construction: native OpenAI and Anthropic formats (no code rewrite), your keys belong to you, and leaving = putting one configuration line back. Your usage data exports, and GDPR-grade erasure is built in.
How much lower could your AI bill be?
One line of configuration and the meter runs: who spends what, where the savings are, and budgets that block the drift. The first euro saved is worth every demo.
One line of configuration to change · Your API keys stay yours · No cloud account required