OUTSCALE blog

Sovereign AI and sovereign cloud: how can public-sector projects be accelerated?

OUTSCALE — Tue, 29 Sep 2026 12:56:55 +0000

At OUTSCALE EXPERIENCES 2026, Mireille Mege, Public Sector Customer Success Manager at OUTSCALE (Dassault Systèmes), and Clara Pascolo, Chief Operating Officer at SMLB Next, explained how Nuage Public simplifies public-sector access to sovereign cloud, drawing on its partnership with OUTSCALE and the generative AI bundles from Mistral available on the OUTSCALE Marketplace.

For public administrations and parapublic organizations, public cloud provides a practical entry point to secure, compliant and scalable environments capable of adapting to business needs.

The success of these projects relies on a structured approach: identifying a relevant initial use case, ensuring the protection of sensitive data, selecting the appropriate level of support and anticipating industrialization from the earliest stages.

Nuage Public as a starting point

The UGAP Nuage Public marketplace provides access to cloud services for more than 26,000 public- and parapublic-sector beneficiaries. It enables organizations to define their needs, specify their technical and regulatory requirements, and then be directed to cloud providers capable of meeting them.

This approach facilitates the launch of artificial-intelligence projects, often in the form of a proof of concept. It responds to two main trends. Some organizations want to implement the Cloud au Centre doctrine promoted by DINUM. Others want to use AI in a more secure and controlled environment.

The Nuage Public marketplace combines several factors that are important for public procurement: negotiated prices, a simplified process, guidance toward relevant offerings and shorter deployment times. In some cases, a project can start in less than a month, while the purchase order can be processed very quickly when the requirements are clearly defined.

Public-sector priorities

Organizations launching a sovereign-AI project generally have three main expectations.

Controlling data location

The first question concerns where data is located and how it is processed. Users want to prevent sensitive data from being transferred to environments they do not control. This concern applies in particular to administrative data, health information, identity documents, financial data and defense-related information.

Sovereignty therefore becomes a central decision criterion. Data must be processed and stored in an environment that meets the organization’s requirements, with particular attention paid to access, permissions and the platform’s operating conditions.

Reducing shadow-AI risks

Employees may be tempted to use AI services directly available online, particularly to query language models with professional information. This practice creates a risk of losing control over the data entered and where it is sent.

A sovereign-AI environment provides users with a controlled framework. The organization can define authorized services, manage access and encourage the use of models hosted in an environment that meets its security requirements.

Preparing for industrial deployment

A project often begins with a limited use case. Once the initial results are positive, other needs emerge. The organization may then want to analyze more documents, automate additional steps or use several models.

The selected architecture must therefore be able to evolve. A scalable offering facilitates the transition from proof of concept to production and then to multiple use cases. This industrialization capability must be considered from the outset to avoid having to redesign the entire technical environment later.

Three AI project offerings

Public-sector organizations can choose between several approaches. The right option depends on the desired level of control, the number of models required, the available resources and the expected level of support.

Consuming compute resources

The first use case consists of providing the resources required to run language models. The organization consumes compute including CPU, RAM and GPU and then uses the models at its disposal.

This approach offers significant technical freedom. It is suitable for teams capable of deploying and administering their own environment. However, they must manage several components themselves, including resource configuration, Nvidia drivers and the components required to run the models.

Deploying a Mistral bundle

The second use case is based on Mistral bundles. This offering provides a ready-to-use solution, with a Mistral model deployed in a tenant dedicated to the customer. Deployment takes place through the Marketplace and can be completed quickly after the order has been placed.

The customer receives a directly accessible endpoint to begin its work. Mistral models are supplied with their licenses, and the environment is dedicated to the organization. Data therefore remains within the designated environment in France, in accordance with the characteristics of the offering.

The bundles offer unlimited token consumption. However, capacity remains determined by the available hardware resources, particularly the number of GPUs associated with the model and the use case. Depending on requirements, one or two GPUs may be offered.

This option is especially suitable for organizations that want to use a language model without managing the entire technical stack themselves. It reduces initial complexity and accelerates access to an operational solution.

Building a Mistral AI Studio platform

The Mistral AI Studio platform is designed for projects requiring multiple models and several usage scenarios. It may be appropriate when an administration wants to analyze documents, perform optical character recognition, verify information or cross-check a form against an identity document.

In this configuration, the offering is built together with the customer. Use cases are analyzed in advance to select the appropriate models. It is generally advisable to begin with two or three clearly defined use cases and then design a coherent solution around those needs.

This approach creates a customized and scalable environment. Mistral also supports the customer in tuning the solution and adapting it to the identified use cases. Additional support may be provided for Kubernetes-related operations, including model updates and platform upgrades.

Toward hybrid model consumption

Organizations may need several model families depending on their use cases. A Mistral model may be suitable for one scenario, while a Llama model or another open-source model may be more effective for another task.

This is leading toward hybrid model consumption. The stated objective for the first half of 2027 is to offer an AI Factory capable of adapting GPU consumption to the intensity of the use cases and the models being used.

Such an approach should make it possible to allocate resources according to actual needs. It opens the way to more flexible use of AI infrastructure, with greater adaptability across models, workloads and expected performance levels.

Moving from proof of concept to production

Choose a manageable first use case

The choice of the first use case influences the rest of the project. An organization benefits from starting with a clearly defined need that is useful enough to produce measurable results and simple enough to be mastered quickly.

This initial stage helps identify technical constraints, security requirements, support needs and monitoring arrangements. It also provides valuable feedback before AI is extended to more complex processes.

The approach can follow three priorities:

select a concrete and relatively simple use case;
measure results and resource consumption;
prepare for expansion to other use cases.

Organize security workshops

Security workshops play an important role in projects involving several models or sensitive data. They help define authorized users, roles, access rights and the conditions governing use of the platform.

This preparation should take place before operational deployment. It helps establish a trusted framework and align the technical environment with the organization’s internal security requirements.

Security also concerns project governance. Teams must know what data may be processed, by which users and under what conditions. These rules facilitate adoption and limit uncontrolled use.

Plan for operational support

The required level of support depends on the selected offering and the skills available internally. An organization consuming compute resources directly must have the capabilities needed to administer its environment. A turnkey solution reduces this burden. A multi-model platform requires closer collaboration around use cases, models and their evolution.

Support may also cover Kubernetes operations, model updates, version upgrades and platform optimization. This becomes particularly important when the solution moves from experimentation to production.

Managing costs and environmental impact

Controlling consumption is a major issue in public procurement. The available budget must be considered alongside the resources used, including GPUs, tokens and other cloud components.

Monthly monitoring makes it possible to identify resources that have been ordered but are seldom used, and then adjust the environment. This consumption review also helps prepare for project expansion using actual data rather than initial estimates.

FinOps provides a framework for financial management. It makes it possible to compare deployed resources with observed usage and arbitrate between performance, sovereignty, cost, support and scalability.

Green IT complements this analysis. A carbon calculator can be used to track changes in the footprint associated with resource consumption. It may reveal overconsumption caused by virtual machines left running during inactive periods or by resources being deployed without a corresponding operational need.

Financial and environmental consumption monitoring should begin in the early stages of the project. It promotes more precise resource usage and supports decisions related to scaling.

Balancing sovereignty, performance and scalability

The choice of an AI environment cannot be based on a single criterion. Organizations must consider data location, security, expected compliance, performance, cost, support quality and scalability at the same time.

Sovereignty is particularly important when data is sensitive. In this context, an environment located in France and designed to meet security requirements provides an appropriate framework for public- and parapublic-sector projects.

Performance must then be assessed according to the use case. A model intended for document analysis does not necessarily require the same resources as an environment running multiple models and several simultaneous processes.

Finally, scalability must be incorporated into the decision. A solution suitable for a proof of concept may become insufficient as the number of users, documents or use cases increases. The initial choice should therefore preserve the possibility of gradual industrialization.

Conclusion

Sovereign AI on public cloud provides public-sector organizations with a practical framework for experimenting with, securing and industrializing their use of artificial intelligence. The Nuage Public marketplace simplifies access to cloud providers, cloud offerings and conditions suited to public procurement.

The most effective approach is to start with a manageable use case, secure access and data, monitor consumption and then prepare for expansion to multiple models and scenarios. Compute-resource offerings, Mistral bundles and the Mistral AI Studio platform make it possible to adapt the level of freedom and support to the project’s objectives.

Discover the solutions available on the OUTSCALE Marketplace

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Open source, digital commons and sovereignty: levers for the public sector

OUTSCALE — Fri, 18 Sep 2026 14:50:11 +0000

At OUTSCALE EXPERIENCES 2026, Julie Ripa, Co-Director of La Suite numérique at the Interministerial Digital Directorate (DINUM), Florian Caringi, Deputy Head of Data & Open Source, President of OW2 and Vice President of Tosit within the BPCE Group, and Léo Unbekandt, Co-Founder and Chief Technology Officer of Scalingo, discussed the role of open source and digital commons in digital sovereignty, particularly in the public sector.

Open source and digital commons are playing an increasingly important role in public-sector digital sovereignty strategies. In particular, they enable public administrations to better control their technological dependencies, facilitate reversibility and pool their digital investments. Their effectiveness nevertheless depends on specific conditions: open governance, an active community, available skills and sustainable funding.

Understanding open source and digital commons

Open source as a collaboration model

Open source refers to code distributed under a license that authorizes its use and, subject to the applicable conditions, its modification and redistribution. This approach is based on several core principles: transparency, collaboration and the ability to develop and innovate collectively.

In an open-source environment, value therefore lies not only in the code. It also depends on the communities that maintain it, the organizations that develop it and the stakeholders capable of integrating it into operational environments.

Open source thus provides a shared technological foundation. It enables several companies, public administrations and contributors to work on common building blocks sometimes through cooperation, and sometimes through competition between service providers.

Digital commons rely on governance

A digital common is a digital resource collectively managed by a group that defines its own rules. This resource may take different forms, including data, code, specifications or content.

Wikipedia and Linux illustrate this approach. However, publishing software under an open-source license is not enough to create a digital common. The decisive factor is the community and governance organized around the resource.

A project becomes a common when several stakeholders can contribute to its maintenance, participate in decision-making and ensure its continuity. Governance must therefore define how contributions are made, how changes are approved and how the project is funded.

Digital sovereignty as strategic autonomy

Controlling technological dependencies

Digital sovereignty does not mean that an organization must produce all the hardware and software components it uses on its own. Such an approach would be difficult to achieve in practice, since no organization controls the entire digital value chain, from hardware to software.

Rather, digital sovereignty is about having the ability to control and make informed choices. A sovereign organization must be able to:

identify its technological and organizational dependencies;
control its data and choose its tools;
change providers when necessary;
limit its dependence on a single supplier;
maintain the ability to respond to risks associated with its suppliers and technologies.

This approach can be described as strategic autonomy. It involves choosing one’s dependencies, measuring their impact and building resilience solutions suited to the organization’s activities.

Open source as a resilience lever

Open source contributes to this autonomy by reducing the risk of dependence on a single stakeholder. An open technology can be maintained, integrated or adapted by multiple actors, provided that a sufficiently robust ecosystem exists around the project.

This plurality facilitates reversibility. An organization can rely on different integrators, service providers or internal teams to operate the same technology. It therefore has greater room for maneuver in the event of price changes, license modifications, product discontinuation or strategic disagreement with its provider.

Reversibility also depends on architectural choices. Open APIs, open software and widely adopted technologies make it easier to move a solution to another environment.

Public-sector strategies

The “public money, public code” principle

In the public sector, opening up code reflects a desire to reuse collective investments. The “public money, public code” principle holds that code funded by taxpayers should be reusable by other public administrations, companies or citizens.

This approach pursues several objectives. It improves the transparency of public action, facilitates code review and creates opportunities for cooperation between public administrations. It also promotes innovation by enabling different stakeholders to reuse and adapt existing developments.

La Suite numérique illustrates this approach. This suite of collaborative tools for public-sector employees is developed as open source and operated by DINUM. It can be hosted on sovereign clouds, including OUTSCALE’s SecNumCloud environment or ministerial infrastructures.

Pooling development at European level

Opening up code facilitates collaboration between public administrations in different countries. Tools developed to meet a public-sector need can be reused in other national contexts and then enhanced through new contributions.

This cooperation makes it possible to bring together resources around shared challenges, pool development efforts and reduce the costs associated with creating similar solutions. It also contributes to the emergence of European alternatives to major technology platforms.

However, publishing the code is only a first step. For a public-sector solution to become a sustainable digital common, it must attract private companies, other public administrations and contributors from several countries.

Building a sustainable community

Open and inclusive governance

The long-term viability of a digital common depends on the diversity and commitment of its ecosystem. A solution that remains entirely dependent on a single public administration or company retains a structural weakness, even when its code is open.

Governance must therefore organize the participation of different stakeholders. It should establish rules for reviewing contributions, prioritizing developments, resolving disagreements and maintaining essential components.

Foundations and associations can play an important role in this organization. A foundation with open governance can bring together software publishers, integrators, users and public-sector stakeholders around common rules. An association can also facilitate the sharing of best practices, co-development and joint funding.

Cooperation and competition within the same ecosystem

A digital common can bring together competing companies that contribute to the same technological building block. This logic of coopetition enables each stakeholder to develop its own services while improving a shared software infrastructure.

PostgreSQL is an example of a technology around which several companies can offer services, integrations or support. Competition between these stakeholders helps strengthen the underlying technology and increases the range of options available to users.

The community then extends beyond the project’s initial offering. It transforms open-source software into a widely adopted, maintained and usable resource for multiple organizations.

Assessing open-source risks

Distinguishing open source, source-available and open core

The term “open source” can cover different realities. Some software publishers offer an open version of their software while reserving essential features for proprietary modules. This model, often called open core, can limit the user’s actual freedom.

A thorough assessment should therefore examine:

the license applicable to the code;
the features available in the open version;
the terms governing commercial use;
the project’s governance;
the diversity of its contributors;
its maintenance and support arrangements;
the community’s ability to continue the project.

Software may be publicly accessible while remaining highly dependent on a single company. The project’s long-term viability must therefore be assessed alongside its license and technical characteristics.

Monitoring project vitality

Opening up the code does not guarantee software continuity. A project may be technically promising while depending on a very small team or a limited number of contributors. If the company maintaining it changes its strategy, abandons the product or changes its license, users may face significant operational risk.

Organizations must therefore assess the vitality of communities, the frequency of contributions, the diversity of maintainers and the existence of stakeholders capable of ensuring the project’s continuity. This approach is part of the broader management of software supply-chain risks.

Understanding licenses and forks

A license applies to code at a given point in time. If the project owner subsequently changes the license, code already released under the previous license remains subject to the rights defined at that time, subject to the specific terms of the license concerned.

A fork consists of creating a new branch of a project from an existing version and continuing its development along a separate path. This possibility enables a community to keep developing software when its governance, license or strategic direction no longer meets its needs.

A fork does not eliminate legal constraints. The new project inherits the obligations of the license governing the reused code. Some licenses are compatible with one another, while others impose specific restrictions on redistribution, commercial use or research.

Companies can rely on an Open Source Program Office to manage these issues over time. This function helps structure license analysis, contribution management and compliance with open-source usage requirements.

Security and maintenance of critical components

Automated analysis is changing the scale of cybersecurity

Artificial-intelligence tools capable of analyzing code can help detect vulnerabilities in open-source projects. They also lower the barrier to entry for developers who want to contribute to existing projects.

However, this development increases the volume of potential issues that must be addressed. Projects may receive more reports, fixes and contributions, with varying levels of quality. Teams must therefore strengthen their review, assessment and prioritization processes.

For operators of large infrastructures, the growing number of vulnerabilities requires shorter response times. Security becomes an organizational capability based on monitoring, coordination and the availability of teams able to intervene quickly.

Funding essential components

A large share of proprietary software and digital services relies on open-source building blocks. The robustness of these components is therefore a collective concern for public administrations, companies and users.

Organizations that use these projects must contribute to their long-term sustainability. This contribution can take several forms:

direct funding for foundations and projects;
code contributions and vulnerability fixes;
provision of expertise;
participation in governance;
support for training and talent development.

European funding mechanisms dedicated to maintaining digital commons can reinforce this dynamic. They help support sovereign and open-source projects that provide collective value but have limited resources.

Creating value with open source

Value also lies in operation

Open source can support viable business models. Commercial value often comes from the services surrounding the software: integration, hosting, operations, support, maintenance, security and scaling.

A company can therefore use open-source technology while providing specialized operational expertise. It helps customers deploy the software, keep it running in production and adapt it to their specific requirements.

This approach is particularly relevant for managed services. Customers benefit from open technology and professional support to operate it over the long term.

Developing European alternatives

Open source can also support the creation of European technology companies. Platforms such as Scalingo rely on open-source components and provide environments that enable customers to test, deploy and operate these technologies.

The use of open software and open APIs strengthens the reversibility of the services offered. Customers can retain the ability to change providers and reduce their exposure to proprietary interfaces.

This proposition addresses the growing expectations of public- and private-sector organizations seeking to limit their dependence on major technology platforms. It combines open technology with operational, support and security expertise.

Three priorities for organizations

To turn open source into a lever for digital sovereignty, organizations can focus their efforts on three priorities:

map technological dependencies, licenses and critical stakeholders;
contribute to the projects used by the organization through code, funding or expertise;
participate in governance structures capable of ensuring the continuity and evolution of digital commons.

This approach must involve IT, legal, procurement, security and business teams. Digital sovereignty is an organization-wide strategy combining architecture, governance, skills and funding.

Conclusion

Open source and digital commons are essential levers of strategic autonomy. They facilitate reversibility, resource sharing, transparency and supplier diversification, while encouraging the emergence of European technology ecosystems.

Their effectiveness nevertheless depends on organizations moving beyond the simple consumption of open software. Sustainable digital sovereignty requires active communities, inclusive governance, local expertise and regular funding. Open source is not free: it relies on contributions, shared responsibilities and investments capable of ensuring the continuity of critical digital building blocks.

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My Carbon Footprint: managing the carbon footprint of Cloud usage with OUTSCALE

OUTSCALE — Thu, 17 Sep 2026 13:30:04 +0000

Mastering the carbon footprint of Cloud has become a central challenge for organizations deploying applications, virtual machines, GPU services, and large-scale storage capacities. Available since November 2025, OUTSCALE’s My Carbon Footprint provides structured visibility into the estimated carbon impact of Cloud usage within a sovereign and secure environment. This tool helps companies understand their emissions, meet certain reporting requirements, and steer their decisions toward more resource-efficient Cloud practices.

The digital carbon footprint and the rise of data centers

Digital technologies now account for a significant share of the carbon footprint, both in France and globally. This footprint is growing due to the widespread adoption of SaaS applications, generative AI, high-performance computing, and the continuous increase in data volumes.

Data centers play an increasingly important role in this equation. Their share of digital impact has risen sharply since 2022. This trend is driven by intensifying computing and storage needs, as well as the development of particularly demanding use cases, notably GPU infrastructures dedicated to language models and AI workloads.

Forecasts point to a very significant increase in data center electricity consumption by 2030. AI usage contributes to this trend, even as precise assessments of its environmental impact continue to evolve. Organizations must therefore integrate energy efficiency, resource sizing, and infrastructure choices from the outset of their digital projects.

Public Cloud as a lever for environmental efficiency

Using public Cloud infrastructure is a first lever for controlling environmental impact. Resource pooling enables higher server density, more efficient equipment utilization, and continuous optimization of workloads.

This efficiency also relies on Cloud-specific mechanisms such as dynamic capacity adjustment, automatic scaling, and reduction of unused resources. A properly sized infrastructure limits overcapacity and promotes consumption better aligned with applications’ actual needs.

The location of data centers also plays a decisive role. In France, the electricity mix has a particularly favorable carbon intensity, around 40 grams of CO₂ per kWh. Deploying Cloud workloads in a region with largely decarbonized electricity directly improves the estimated carbon footprint of digital usage.

Data center efficiency is also measured through operational indicators such as PUE (Power Usage Effectiveness) for energy efficiency and WUE (Water Usage Effectiveness) for water efficiency. Public Cloud infrastructures can achieve better results thanks to their level of industrialization, equipment pooling, and adapted cooling technologies.

Data centers designed for a more sustainable cloud infrastructure

The sustainability of a Cloud service depends as much on the technology provided as on data center operators’ practices. The infrastructures used by OUTSCALE in France rely on ISO 50001 certification for energy management and ISO 14001 for environmental management. They are also aligned with the European Code of Conduct for Data Centers.

Renewable electricity supply via guarantees of origin is an important element of this approach. Cooling systems also contribute to sites’ environmental performance. Technologies used include free cooling, free chilling, hybrid solutions with adiabatic cooling, and, in some rooms, direct liquid cooling.

Reducing environmental footprint also involves careful lifecycle management of equipment. Extending usage duration, reusing, and recycling are major focus areas. This issue is particularly sensitive for GPUs, whose replacement cycle can be faster than CPUs due to evolving AI-related needs.

Choosing the right GPU model must therefore respond to a clearly identified business need. Sizing adapted to language models, processing volumes, and performance objectives helps limit unused resources and control associated impacts.

My Carbon Footprint: measuring the carbon impact of Cloud usage

My Carbon Footprint addresses a concrete need for visibility into the carbon footprint of Cloud resources used on the OUTSCALE infrastructure. The tool provides data to track the estimated impact of usage, feed reporting efforts, and support operational decision-making.

Three use cases particularly structure the value of this feature:

Track the estimated carbon footprint of Cloud resources used on the OUTSCALE infrastructure.
Have useful data to meet requirements such as CSRD or greenhouse gas inventories.
Understand the main impact drivers related to virtual machines, GPUs, and storage.

The tool is accessible from the OUTSCALE Cockpit, under My Data then My Carbon Footprint. API access is also available, facilitating the integration of carbon data into internal dashboards, CSR initiatives, or reporting processes.

Users can select their main Cloud account or sub-Cloud accounts to analyze different environments. The available history covers up to 36 months, starting from January 2024. Results can be displayed in tonnes of CO₂ equivalent or kilograms of CO₂ equivalent, depending on the required level of granularity.

Detailed breakdown by service and emission source

My Carbon Footprint provides a breakdown of the carbon footprint by service type. This analysis distinguishes virtual machines, GPUs, and storage, making it easier to identify the biggest contributors. Interactive visualizations provide access to absolute values and ratios associated with each resource category.

The tool also offers a view by emission source. This view covers several components of the environmental footprint:

Electricity consumed by the infrastructure.
Hardware mobilized.
Network.
Hosting and maintenance of data center equipment.

Data can be displayed as charts or tables. This flexibility improves information accessibility for technical teams, CSR managers, finance departments, and compliance teams.

A monthly breakdown by service also makes it possible to observe changes over time. The projection presented is based on the last 24 months, providing a useful basis for tracking trends, analyzing consumption variations, and identifying periods requiring resource adjustments.

A documented and traceable carbon methodology

Measuring the digital carbon footprint relies on methodologies that continue to evolve. Several frameworks coexist, employing different scopes, assumptions, and calculation factors. Interpreting results therefore requires particular attention to the method used.

My Carbon Footprint is built on three guiding principles: pragmatism, transparency, and scalability. Pragmatism aims to provide actionable data to manage Cloud usage and guide decisions. Transparency involves clarifying methodological choices, elements taken into account, and calculation scope limits. Scalability allows the method and proposed features to be progressively enriched.

The first version of My Carbon Footprint covers the European region. The ambition is to gradually extend the scope to other regions, integrate new services, and refine data granularity. This trajectory supports increasingly precise environmental management of Cloud infrastructures.

Toward responsible cloud management

Reducing the Cloud carbon footprint relies on a combination of technological, operational, and organizational choices. Using efficient infrastructure, selecting committed data centers, optimizing resources, and regularly measuring impacts are complementary practices.

My Carbon Footprint transforms environmental data into a concrete management tool. By visualizing the estimated impact of virtual machines, GPUs, and storage, organizations have a stronger basis for adjusting their architectures, limiting over-sizing, and embedding their Cloud usage in a responsible digital approach.

Learn more about Carbon Footprint

Carbon Footprint: A Service enabling our clients to measure and manage the carbon footprint of their Cloud usage

OUTSCALE unveils “Carbon Footprint,” a sovereign service that enables CIOs to measure and manage the carbon footprint of their Cloud usage

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SecNumCloud, the qualification now mandatory for the state’s sensitive data: the complete guide to the legal framework and scope of application

OUTSCALE — Thu, 17 Sep 2026 08:43:14 +0000

Since 22 August 2026, the rules have changed for French government bodies that host their most sensitive data in the Cloud. An order published in the Official Journal approves the SecNumCloud framework as the reference framework for meeting the security and protection requirements set out in the new regulatory framework.

This legal shift, in preparation for several years, raises a very concrete question for government bodies, their operators, and the companies that work with them: who is affected, from when, and for exactly which data?

This guide reviews the legal framework, the scope of the obligation, how SecNumCloud compares with other security frameworks, and the outlook for this digital sovereignty doctrine.

What is SecNumCloud?

SecNumCloud is the qualification framework for Cloud service providers, developed and issued by ANSSI (Agence nationale de la sécurité des systèmes d’information — France’s national cybersecurity agency). Created in 2016, it has gone through several revisions, with version 3.2 now the framework in force.

One important point of vocabulary to clarify from the outset: SecNumCloud is a qualification, not a certification in the sense of ISO 27001 or HDS. An ANSSI qualification involves an assessment carried out by an accredited audit provider, on the basis of which the agency itself grants the recognition: a process that is more demanding and more tightly controlled than a certification issued by a standard accredited body.

In concrete terms, SecNumCloud attests to a high level of technical and organizational security, but above all, it guarantees immunity from foreign extraterritorial legislation, first and foremost the US Cloud Act and FISA. It is this dimension of legal independence that sets SecNumCloud apart.

The legal framework that made SecNumCloud mandatory

Since when has SecNumCloud really been mandatory, rather than recommended? Are there any exemptions? Could an equivalent European certification be enough?

These are the questions that come up most often on this topic. The answer lies in a precise timeline, built in three stages.

The obligation did not appear out of nowhere: it is the culmination of a trajectory that began in 2021, with the government’s “Cloud at the center” (“Cloud au centre”) doctrine, which already recommended that government bodies use qualified offerings for certain sensitive data. Three texts then built, step by step, the current legal framework:

The SREN law (2024), in Article 31, establishes the principle of an obligation to use SecNumCloud-qualified providers to host the State’s sensitive data.
Decree No. 2026-272 of 14 April 2026 sets out the terms of application: who is affected, what falls under the notion of sensitive data, and the applicable exemption regime.
The Order of 12 August 2026, published in the Official Journal on 14 August, formally approves the SecNumCloud 3.2 framework as the technical framework for implementation. It is this text that gives binding force to what had, until then, remained a strongly recommended best practice.

The decree provides for an exemption regime for certain projects already under way. This is not a commercial loophole: the exemption must be justified, closely regulated, made public under the prescribed conditions, and approved at the required level. It does not allow sovereignty and security requirements to be permanently set aside.

This timeline is worth understanding as a whole: it shows a digital sovereignty doctrine built progressively, with each text refining and reinforcing the one before it.

Who is affected by the SecNumCloud obligation and what sensitive data is covered?

The scope of the obligation is precise. Those affected include, in particular:

central government administrations and relocated national services;
their State operators;
certain public interest groupings (groupements d’intérêt public, or GIP) falling within the scope defined by the texts.

These entities are subject to the obligation when they use a private provider to host sensitive data as defined by the decree: that is, data whose breach would harm public order, public safety, or public health.

Are local authorities affected by the SecNumCloud obligation?

Local authorities (collectivités territoriales) are not directly covered by the current scope of Article 31 of the SREN law.

They may nonetheless choose to use SecNumCloud-qualified offerings to meet their own security, sovereignty, and data protection requirements.

Since the regulatory framework around the sovereignty of digital procurement is likely to evolve, this remains a topic for local authorities and their IT departments (DSI) to watch.

SecNumCloud compared with other frameworks

SecNumCloud is not in competition with ISO 27001 or HDS: these certifications serve different purposes, and an organization can very well hold several qualifications or certifications at once.

ISO 27001 is an international standard for information security management, with no dimension of legal sovereignty.
HDS (Hébergeur de Données de Santé, or Health Data Hosting) is a sector-specific certification, dedicated to health data.
SecNumCloud is a qualification designed specifically for Cloud services, combining strict requirements on security, operations, and sovereignty.

At the European level, the EUCS (European Cybersecurity Certification Scheme for Cloud Services), a harmonized Cloud certification scheme, has been under discussion for several years to provide an EU-wide equivalent. It remains under negotiation, however, and is not intended to replace SecNumCloud in the short term.

What this actually changes for public-sector players and their providers

One point deserves clarification: the SecNumCloud obligation does not mean that a bidder for a public contract (a web development agency or a business software vendor, for example) must necessarily be qualified from end to end itself.

However, as soon as a project touches the State’s sensitive data, the requirement applies to one specific, non-negotiable link: hosting. The successful bidder will need to have that data hosted on Cloud infrastructure that itself holds the SecNumCloud qualification, or an equivalent recognized by ANSSI at the European level. It is this piece of infrastructure that constitutes the unavoidable compliance standard.

Conclusion

The Order of 12 August 2026 closes a legal process launched with the SREN law of 2024: SecNumCloud is no longer a recommended best practice — it is now a legal condition for hosting the State’s most sensitive data. The scope remains targeted for now, but the direction is clear.

Today, around ten offerings are SecNumCloud-qualified in France, including OUTSCALE’s public Cloud offering.

A pioneer, OUTSCALE was the first public Cloud provider to obtain the SecNumCloud qualification, in 2019, and also holds HDS certification, enabling it to meet the needs of organizations with specific requirements for hosting health data.

Discover OUTSCALE’s sovereign Cloud infrastructure.

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OUTSCALE accelerates sovereign AI with NVIDIA HGX H200 GPUs in its SecNumCloud infrastructure

OUTSCALE — Mon, 14 Sep 2026 08:30:53 +0000

OUTSCALE announces the availability of NVIDIA HGX H200 GPUs within its SecNumCloud-qualified cloud infrastructure, operated in France, to accelerate sovereign AI and high-performance computing projects in a trusted environment.

Discover NVIDIA HGX H200 GPUs available at OUTSCALE.

Designed for the most demanding uses in generative AI and high-performance computing (HPC), the Nvidia HGX H200 architecture is built on multi-GPU configurations (8x H200) optimized for large language models and memory-intensive workloads.

The HGX H200 platform delivers up to 1.1 TB of aggregate HBM3e memory across its 8 GPUs, with each GPU offering up to 4.8 TB/s of memory bandwidth delivering higher performance for both training and extended-context inference.

With these new capabilities, OUTSCALE enables public and private organizations to accelerate the deployment of their AI projects, streamline the transition from experimentation to production, and maintain control over their strategic data.

Highly regulated sectors such as the public sector, healthcare, finance, and defense can therefore deploy their AI projects with a high level of security, compliance, and sovereignty.

To discover how these new capabilities can accelerate your artificial intelligence projects within a sovereign and secure environment, discover NVIDIA HGX H200.

L’article OUTSCALE accelerates sovereign AI with NVIDIA HGX H200 GPUs in its SecNumCloud infrastructure est apparu en premier sur OUTSCALE blog.

Running an organization in real time: What it demands from Cloud infrastructure

OUTSCALE — Fri, 28 Aug 2026 15:15:59 +0000

A customs tariff decision made thousands of miles away can, within a few weeks, upend an entire organization’s financial balance. Faced with this kind of shock, an organization’s ability to recalculate in real time the impact of a scenario on its value chain (suppliers, costs, lead times, margins) becomes a decisive competitive advantage. This is precisely what Virtual Twins of organizations are addressing today, an approach presented by Morgan Zimmermann, CEO of 3DEXPERIENCE, Dassault Systèmes, at OUTSCALE EXPERIENCES 2026: simulate before deciding, rather than endure. But this agility cannot be decreed by software alone, it depends on a Cloud infrastructure capable of absorbing it.

Simulating before deciding

The principle behind a Virtual Twin of an organization is to continuously represent how a company actually operates: its flows, dependencies, and constraints, so that scenarios can be tested before they are executed. Faced with a sudden rise in customs tariffs representing several billion euros in potential impact, an organization equipped with such a system can query its Virtual Twin, obtain a quantified assessment of several supply chain reorganization scenarios within moments, and make an informed decision. This kind of steering relies on specialized agents, capable of representing a situation, understanding it, engaging with teams, reasoning through alternatives, and triggering concrete actions.

Agility that comes with a technical price

This level of responsiveness places strong demands on the infrastructure that supports it. First, it requires sufficient compute elasticity to absorb occasional, unpredictable simulation spikes without excessive latency: a Virtual Twin that takes several hours to respond loses some of its value. Second, it requires strict protection of data and intellectual property: an organization’s Virtual Twin aggregates strategic information on suppliers, costs, and internal processes, often shared with an ecosystem of partners. Rigorous isolation therefore becomes a condition of trust, not an option. Morgan Zimmermann himself raises this requirement for pooled yet controlled compute power at the close of his demonstration, pointing to OUTSCALE’s ability to pool costly compute resources to make them economically efficient at the point of use: a principle also found in the dynamic GPU resource allocation implemented within OUTSCALE’s AI Factories.

OUTSCALE, the foundation that makes this possible

It is precisely on these requirements that OUTSCALE builds its response. Philippe Miltin, CEO of OUTSCALE, sums up the criteria for this sovereignty: recognized certifications and qualifications, including SecNumCloud qualification, reversibility and interoperability of environments without dependency on a single vendor, and long-term control over the technology investments that underpin it. Arnaud Bertrand, CTO of OUTSCALE and Senior Vice President of OUTSCALE, Dassault Systèmes, details the technical translation of this within the AI Factories: better, pooled use of GPU resources, and physical and logical segregation guaranteeing each organization the isolation of its workloads, even when several partners are working on the same scenario.

Running an organization in real time is therefore not only a software challenge: it is an infrastructure challenge. By providing the compute power, sovereignty, and isolation needed for this kind of steering, OUTSCALE positions itself as the Cloud and AI foundation that enables organizations, including in the most critical sectors, to turn uncertainty into decision.

To watch the replays of Morgan Zimmermann’s and Philippe Miltin’s talks, find the links below.

Organizations steerable through their Virtual Twins – Morgan ZIMMERMANN at OUTSCALE EXPERIENCES 2026:

OUTSCALE: the sovereign Cloud and AI foundation for critical organizations – Keynote by Philippe Miltin at OUTSCALE EXPERIENCES 2026:

Technology vision and new services – Keynote by Arnaud Bertrand at OUTSCALE EXPERIENCES 2026:

L’article Running an organization in real time: What it demands from Cloud infrastructure est apparu en premier sur OUTSCALE blog.

Why Industrial AI Needs Dedicated Infrastructure

OUTSCALE — Thu, 27 Aug 2026 13:00:40 +0000

Generative AI has long been envisioned as a service simply plugged into standard Cloud infrastructure. That assumption no longer holds once AI is expected to deliver reliable results on industrial topics: sizing a mechanical part, anticipating a supply chain disruption, validating a regulatory configuration. A general-purpose language model, however powerful, lacks an understanding of physical reality and can therefore produce results that are not physically correct. This observation, made by Florence Hu-Aubigny, Executive Vice-President Research & Development at Dassault Systèmes and Chief Architect of the 3DEXPERIENCE platform, during OUTSCALE EXPERIENCES 2026, reflects the same rationale behind the design of AI Factories by OUTSCALE: an infrastructure built from the ground up for training and running industrial AI models, within a sovereign and secure framework.

An architecture designed to retain nothing

The first guarantee offered by AI Factories rests on a principle that is simple to state but demanding to implement: no data is stored there on a lasting basis. The AI Factory operates as a compute space, not a storage space: data passes through, computation runs, and everything is then returned to OUTSCALE’s sovereign IaaS infrastructure, where certifications reside and where data persistence is fully guaranteed. Arnaud Bertrand, CTO of OUTSCALE and Senior Vice President of OUTSCALE, Dassault Systèmes, sums up this architectural choice plainly: the goal is to ensure that, at no point, any trace of customer data remains in the compute space.

This non-persistence is paired with physical and logical segregation by customer: each organization has its own dedicated AI endpoint, guaranteeing the isolation of its workloads and its intellectual property. This architecture was built in partnership with Nvidia (based on a generation of GB200 and GB300 components, paired with H200 GPUs) and with Bull for hardware integration, providing state-of-the-art compute capacity while retaining full control over its implementation.

Elastic access to multiple model families

Through a dedicated control plane, AI Factories provide access to a pool of models that includes Nvidia’s NIM models, such as Nemotron, as well as the models OUTSCALE already works with, such as Mistral. OUTSCALE will also add Aleph Alpha and LightOn to this pool to round out its sovereignty offering, and will provide access to LLaMA and OpenAI models as well. This pool powers a multi-model agentic platform that can orchestrate, depending on the use case, whichever model is best suited, whether open, sovereign, or international.

This elasticity comes with a shift in how GPU resources are allocated. Rather than static allocation (where a customer pays for reserved capacity that isn’t always fully used), OUTSCALE is building dynamic allocation, adjusted to actual usage.

One standard, two deployment scales

AI Factories are deployed according to two complementary approaches. On one side, international zones in Europe, the United States, and Asia offer maximum elasticity and allow workloads to be distributed across several continents. On the other hand, a strictly SecNumCloud zone, isolated and self-contained, is reserved for the most sensitive workloads, with no possibility of spillover to international zones.

It is this combination: non-persistence, per-customer segregation, model diversity, dynamic allocation, and dual-speed deployment – that allows OUTSCALE to offer a credible response to organizations operating in critical, regulated sectors: healthcare, energy, aerospace, defense, and the public sector. These AI Factories, initially designed to meet Dassault Systèmes’ internal needs, are now being gradually opened up to all companies and institutions that share the same requirement: industrializing AI without ever compromising on the sovereignty of their data.

To find out more, watch the replays of Arnaud Bertrand’s and Florence Hu-Aubigny’s talks.

Technology vision and new services – Arnaud Bertrand’s keynote at OUTSCALE EXPERIENCES 2026:

Dassault Systèmes: Industrial AI platform architecture – Florence Hu-Aubigny at OUTSCALE EXPERIENCES 2026:

L’article Why Industrial AI Needs Dedicated Infrastructure est apparu en premier sur OUTSCALE blog.

ÆTHER Consortium: Building Europe’s Industrial Capability for AI at Scale

OUTSCALE — Wed, 29 Jul 2026 08:22:06 +0000

Every year, European companies pay tens of billions of euros to American players for their Cloud software and services: a dependency now documented by several parliamentary reports, which call for a reconquest of European digital sovereignty.

But a dynamic of sovereignty cannot be decreed by a report: it is built through example, and through concrete industrial capabilities. This is precisely the ambition carried by the European ÆTHER consortium, in which OUTSCALE, the Cloud and AI brand of Dassault Systèmes, takes part.

What Is the ÆTHER consortium?

ÆTHER brings together complementary European players in energy, construction, high-performance computing, semiconductors, Cloud, and AI: 2CRSi, SiPearl, Axelera AI, Viridien, ÉS, Socomec, Haffner Energy, Equans, Demathieu Bard, Nhood, and Projex Ingénierie, alongside OUTSCALE.

The consortium pursues a clear ambition: to demonstrate that Europe possesses all the expertise required to design, build, power, operate, and scale AI infrastructure.

The consortium has announced its application for the European Commission’s “AI Gigafactory” call for proposals, launched as part of the EU’s digital and industrial sovereignty strategy. The project targets two sites in the Strasbourg region, FR-SXB1 and FR-SXB2, currently in advanced negotiations with the long-term ambition of reaching a total electrical capacity of more than 400 MW.

“Industrial AI will only deliver on its promise if Europe builds infrastructure equal to its ambitions”, Florence Verzelen, Executive Vice President EMEA, Dassault Systèmes

OUTSCALE's role: turning compute into sovereign, secure, and industrialized Cloud and AI services

Concretely, OUTSCALE brings to ÆTHER its expertise as a sovereign Cloud and AI operator: the ability to turn large-scale compute power into secure, governed, and industrialized services for businesses, institutions, and research organizations.

“As the Cloud and AI foundation of Dassault Systèmes, OUTSCALE brings to ÆTHER unique expertise at the intersection of sovereign cloud, high-performance computing, and mission-critical industrial applications. Our ambition is to enable businesses, public institutions, and research organizations to develop and operate their AI models in high-performance, secure, and sovereign environments that protect data, know-how, and intellectual property. With ÆTHER, Europe is equipping itself with a strategic industrial capability to design, host, and operate AI at scale”, Arnaud Bertrand, Chief Technology Officer, OUTSCALE, and Senior Vice President, Cloud R&D, Dassault Systèmes.

A capability that reinforces an already established strategy

Joining ÆTHER doesn’t start from scratch: it reinforces the AI Factories strategy that Dassault Systèmes has entrusted to OUTSCALE, which already aims to provide the Cloud, orchestration, and execution capacities needed to train, infer, and operate AI models at scale in critical environments. ÆTHER brings an additional European capability to this trajectory, aligned with the needs for sovereignty, performance, and scale in industrial AI.

All the pieces already exist: now they need to be connected

Europe today has every piece in the AI value chain: energy, Cloud, high-performance computing, semiconductors, industrial software, and research. The value of the ÆTHER consortium, like that of any AI Gigafactory project, will be measured by its ability to connect these links to serve concrete use cases: training models, adapting them, operating them, securing them, governing them, and integrating them into organizations’ business processes.

This is the ambition that OUTSCALE, Dassault Systèmes, and all the partners of the ÆTHER consortium carry forward together.

View the press release.

L’article ÆTHER Consortium: Building Europe’s Industrial Capability for AI at Scale est apparu en premier sur OUTSCALE blog.

Overcoming the challenges of implementing agentic AI

OUTSCALE — Tue, 28 Jul 2026 13:05:33 +0000

Intelligent automation is entering a new phase with agentic AI. By combining the execution capabilities of automation with the reasoning of generative AI, it enables autonomous agents to act directly at the core of business processes. Organizations expect three major benefits: cost reduction, faster turnaround times, and improved service quality.

Led by Simon LUSINCHI (Partner at Artefact) and Marie-Astrid de Foucauld (AI Ecosystem Manager at OUTSCALE) during the OUTSCALE EXPERIENCES event, this session analyzes the major obstacles related to deploying agentic AI in companies. It highlights the importance of going beyond a purely technological vision to jointly orchestrate human, business, and technical levers, which are key to sustainable transformation.

Understanding the stakes of agentic AI

The emergence of agentic AI is part of the ongoing evolution of technological uses, following RPA automation, traditional analytical AI, and then generative AI. Characterized by its ability to reason and chain actions, this new form of AI marks a turning point: it no longer limits itself to content generation, but decides, acts, and adapts based on its context.

This technological shift fundamentally transforms how projects are approached. The objective is no longer simply to optimize the time allocated to an isolated task, but to rethink and transform the overall functioning of an entire process.

Three methods to get started

To initiate an agentic transformation, organizations can choose between three methods:

Global strategy: map all processes, quantify potential value, and prioritize use cases to build an enterprise-wide agentic roadmap.
Function-based approach: first target a cross-functional area (HR, legal, marketing, administrative) and identify processes to optimize with agents.
Targeted pilots (PoCs): launch pilots on process fragments to build a methodology and quickly demonstrate value (e.g., commitment management).

A progression of benefits

The deployment of agentic AI can be understood across three levels of maturity: individual use, mutualization, and end-to-end autonomy. At each level, ROI becomes clearer, more collective, and more strategic.

Individual level : An assistant automates specific tasks for a person or function. The gain is real but remains diffuse, as it mainly improves local productivity.

Mutualization level : An agent orchestrates multiple actions around a common objective, serving a team or shared workflow. The value created becomes collective and easier to measure in operations.

Autonomous level : The agent takes charge of an entire business process end-to-end. This is where the impact becomes systemic, with performance indicators focused on execution speed, quality, cost, and scalability.

A systemic approach to transformation

To avoid the common mistake of trying to deploy artificial intelligence systematically, Artefact structures its approach around a global vision. Very often, the ideal solution does not lie in integrating an autonomous agent, but rather in simplifying automation or restructuring an existing process.
This multidimensional approach is structured around three strategic axes:

The human dimension: defining a global vision, building the internal narrative, setting appropriate safeguards, and continuously developing skills.
The business dimension: relying on precise process mapping to determine whether the added value will truly come from AI, an RPA solution, or simple organizational optimization.
The technological dimension: focusing on rigorous data management, robust architecture, and the establishment of clear governance.

Data governance and security requirements

The effectiveness of an AI agent is closely tied to the quality of its data architecture. To be fully usable, information must follow a rigorous process of collection, cleaning, structuring, and classification. However, it is crucial to avoid paralyzing the system through excessive classification into “confidential” or “restricted” categories, which could block access to useful data and significantly degrade overall performance.

At the same time, security is a fundamental pillar of the approach. Before considering the deployment of an agentic solution, any organization must anticipate and formalize its requirements in terms of network constraints, hosting conditions, integration protocols, and access rules. Beyond purely technical aspects, this reflection represents a true challenge of trust and overall governance.

Selecting the right solution

To address different levels of maturity and organizational constraints, OUTSCALE offers three complementary approaches based on the desired level of autonomy.

GPU as a Service is aimed at highly technical profiles seeking full control over infrastructure and models.
LLM as a Service enables rapid deployment of a private API within a sovereign and secure framework.
OUTSCALE offers a turnkey software solution designed to be accessible to non-technical users and to accelerate large-scale adoption.

OUTSCALE also relies on an ecosystem of cloud and consulting partners, including Artefact, to support clients at every stage of the project. This support covers initial assessment, use case scoping, deployment, and ongoing optimization of usage and performance.

Key Takeaways

1. Agentic AI fundamentally changes how processes are executed. It no longer simply assists users: it acts, orchestrates, and can make certain decisions within a defined framework. This requires organizations to rethink business processes as a whole, rather than automating only isolated tasks.

2. Success relies on a truly cross-functional approach. Three complementary dimensions must be aligned: the human dimension, with adoption and upskilling; the business dimension, with the selection of the most relevant use cases; and the technological dimension, with data quality, architecture, and integration. The challenge is therefore not only technical, but also organizational and operational.

3. The main obstacles relate to data and governance. Data quality, accessibility, and security directly determine agent performance. Choosing solutions adapted to the organization’s level of maturity is also critical to delivering reliable, measurable, and sustainable results.

L’article Overcoming the challenges of implementing agentic AI est apparu en premier sur OUTSCALE blog.

AI Factories: how OUTSCALE is industrializing AI for the most demanding organizations

OUTSCALE — Tue, 28 Jul 2026 09:12:16 +0000

Artificial intelligence is entering a new phase, that of industrialization, and calls for an infrastructure tailored to its own requirements: intensive computing, training, and running models at scale. For organizations in sensitive sectors (healthcare, finance, the public sector, defense), this equation comes with an added requirement: accessing this computing power while retaining full control over their data.

This is precisely the equation that AI Factories by OUTSCALE, Dassault Systèmes, are designed to solve.

A strategy born for the most demanding use cases

AI Factories were not designed in the abstract. They were first developed to meet Dassault Systèmes’ internal needs, combining the group’s virtual twin technologies with Nvidia’s AI infrastructure, open models, and accelerated computing libraries, to build science-validated industrial models.

Dassault Systèmes entrusted OUTSCALE with carrying this strategy forward: providing the infrastructure, orchestration, and security capabilities needed to train, infer, and operate AI models in critical environments. An expertise built to serve the world’s most demanding industrial players, now extended to all businesses and institutions.

“With Dassault Systèmes, OUTSCALE serves the most demanding industrial players on the planet […]. That’s our uniqueness: what is designed for the most demanding becomes an advantage for regulated markets,” Philippe Miltin, CEO, OUTSCALE.

Three structuring guarantees

Announced at OUTSCALE EXPERIENCES 2026, AI Factories combine three guarantees that matter to sensitive organizations:

Sovereignty, ensured notably through data non-persistence: transient training data is not stored persistently within the AI Factory, which functions as a computing space rather than a storage space. Data persistence remains guaranteed within OUTSCALE’s sovereign, SecNumCloud-qualified Cloud infrastructure.

Optimized resources and usage: dynamic management of GPU and CPU resources, made possible by the strategic partnership with Nvidia, which limits chronic overcapacity and pools scarce resources.

The ability to scale on global resources, as close as possible to each organization’s needs for both training and inference at scale.

One building block within a broader continuum

This strategy fits within a continuum specific to Dassault Systèmes’ industrial AI: one that connects virtual twins, science, simulation, real-world data, AI models, and IP protection. OUTSCALE provides the execution layer, enabling the training, running, and orchestration of the models and agents behind new product categories: virtual companions, generative experiences, virtual twins, and Industry World Models.

Unlike a simple GPU resource provider, OUTSCALE brings a complete layer of service, orchestration, security, and governance: the goal isn’t just to supply computing power, but to make it usable within critical environments, with the guarantees expected by regulated organizations.

Built for regulated and sensitive sectors

AI Factories are designed for businesses, institutions, researchers, and organizations in regulated sectors that want to develop, adapt, and operate their own artificial intelligence models without giving up control over their data, know-how, and intellectual property.

This approach builds on a compliance foundation already proven at OUTSCALE: SecNumCloud qualification, ISO 27001-27017-27018, HDS, and TISAX certifications, CISPE membership, and SOC 2 Type 2. Guarantees that make AI Factories a credible answer for Europe’s most demanding sectors.

Learn more about OUTSCALE’s sovereign Cloud infrastructure.

L’article AI Factories: how OUTSCALE is industrializing AI for the most demanding organizations est apparu en premier sur OUTSCALE blog.