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Giotto Alto and Solo: API Pricing and Private Deployment Explained
The Swiss lab is offering reasoning models through cloud access and customer-controlled infrastructure, with separate API billing options to understand.
Giotto announced Alto and Solo on October 27, a reasoning-model family offered through European cloud services and private deployments. Alto targets demanding workloads, while Solo is designed for a single GPU. The practical question for buyers is which deployment and billing arrangement fits their workload, not simply where the company is based.
What Giotto Alto and Solo offer
The Lausanne company's launch announcement says Alto runs on eight B300 GPUs and Solo on one GPU. These are vendor deployment claims, not performance results from independent testing.
Giotto also announced free shared-cloud access, anonymous chat and mobile apps. Private infrastructure is a different commercial arrangement: the release describes monthly dedicated-cloud rentals and annual or multiyear on-premises licences priced by GPU count and type.
A single-GPU deployment could be useful for a team that wants to keep a narrowly defined workflow inside its own environment. But a GPU count alone does not establish affordability or speed. The hardware specification, concurrent users and context length still determine whether it is a practical fit.
Giotto Alto API token pricing
Giotto's public API page currently displays these euro prices for Alto:
| Token category | Price per million tokens |
|---|---|
| Input | €2.10 |
| Output | €10.50 |
| Cache hit | €0.21 |
| Cache write | €2.10 |
At those displayed rates, one million ordinary input tokens plus 100,000 output tokens would cost €3.15, before any other applicable charges. That is arithmetic for a defined usage mix, not a prediction of the cost of an agent or document workflow.
Repeated inputs and long generated answers produce different bills. The cache-hit price makes reuse relevant, while the output rate means a long reasoning response should not be budgeted as if all tokens cost the input rate.
Flat subscription and by-token access are separate
The launch release also describes a flat-fee API option with one concurrent request and token packages for greater concurrency. Reading that alongside the pricing table can look inconsistent until the product documentation is considered.
Giotto's API setup guide, updated October 28, distinguishes a flat subscription from a separate By-token API product. It describes separate credentials and €10 credit packs. The public token table therefore should not be treated as the price of the flat subscription, whose fee is not stated on that page.
The guide documents a Python client with a Responses interface and medium or high reasoning effort. It also distinguishes orchestrator-event streaming from token-by-token text streaming. Developers building a live interface should check that difference before assuming a familiar streaming experience.
Private deployment is not an open-model licence
The deployment offering includes managed European hosting, installation on private GPUs and preconfigured hardware. Its private-deployment and hardware links lead to sales enquiries, so the site should not be read as an unrestricted public weights download.
There is a licensing distinction too. Giotto's individual EULA describes a proprietary model and limits that licence to non-commercial testing and evaluation. Business buyers should confirm the agreement attached to their selected product rather than assume free chat grants production rights.
Control over where a model runs and freedom to modify or redistribute it are different things. A private deployment can address an infrastructure requirement without removing reliance on a vendor's software, updates or contract.
For an enterprise evaluation, the useful next step is a small, representative workload with agreed hardware, latency and licensing requirements. Giotto's deployment choices make it a candidate for that discussion; they do not replace the discussion with a sovereignty label.
I'm Paras. I work at the intersection of AI, engineering and distribution.
I help technical teams explain complex AI products and deployment tradeoffs in language developers and decision-makers can use.