Migrer vers le cloud est une étape stimulante, mais les décisions prises en amont conditionnent durablement la suite.

La véritable pression se fait sentir à mesure que les usages augmentent et que les architectures sont soumises à des charges plus soutenues. Ce qui fonctionne lors de la migration initiale ne tient pas toujours lorsque les systèmes doivent évoluer à l’échelle de plusieurs régions, équipes et charges de travail.

Les ingénieurs des Professional Services et les Customer Success Managers d’OVHcloud mettent en lumière des décisions souvent sous-estimées, mais déterminantes pour la réussite des migrations et des déploiements dans le cloud public, en posant les bases d’une croissance à la fois évolutive et durable.

Ce qui est négligé au départ, mais qui façonne l’évolutivité par la suite

Le succès à long terme d’un projet cloud se joue généralement au cours des six à douze premiers mois. C’est durant cette période que les choix d’infrastructure ont le plus d’impact et que la préparation opérationnelle devient critique. Souvent relégués au second plan lors d’une migration, le réseau, l’architecture régionale et les fondations opérationnelles s’imposent rapidement comme des éléments clés à mesure que les environnements se développent.

Les premiers déploiements peuvent fonctionner avec succès en utilisant des interfaces publiques et des configurations par défaut. Mais à mesure que les services se multiplient, le trafic interne augmente entre les API, les bases de données et les couches de traitement en arrière-plan. Sans un modèle de réseau privé, cette couche de communication peut introduire de la latence, exposer des données sensibles et entraîner, à terme, des refontes complexes.

« La sécurité doit être la priorité. Un simple ’lift and shift’, sans conception préalable d’une landing zone sécurisée et évolutive, ne suffit pas. Il est essentiel de mettre en place dès le départ une infrastructure adaptée aux besoins futurs. » – Olivier Javaux, Professional Services, OVHcloud

Construire la résilience au-delà du déploiement initial

La stratégie régionale obéit à la même logique. Le choix entre un déploiement en zone de disponibilité unique ou multiple influence directement la résilience, les mécanismes de bascule et la complexité opérationnelle. Revenir sur ces choix après le déploiement s’avère particulièrement complexe, surtout lorsque des dépendances de données et du trafic en production sont déjà en place. En intégrant dès le départ les perspectives de croissance, les exigences de disponibilité et les besoins de continuité d’activité, les organisations peuvent inscrire l’évolutivité et la durabilité au cœur même de la conception de leurs systèmes, et ainsi éviter des ajustements coûteux par la suite.

Les landing zones et les dispositifs opérationnels sont également trop souvent relégués au second plan, alors qu’ils jouent un rôle clé dans les opérations quotidiennes, la gestion des incidents et la conformité à mesure que les systèmes évoluent. L’un des enseignements majeurs réside dans un changement de posture : les équipes cloud doivent passer d’une logique centrée sur le déploiement de l’infrastructure à une approche intégrant dès le départ la visibilité, la gouvernance et le pilotage opérationnel. Les services managés et les plateformes standardisées accompagnent cette transition en réduisant la charge opérationnelle, tout en améliorant la supervision à mesure que les environnements gagnent en complexité.

La dimension humaine, enfin, ne doit pas être sous-estimée. Une intégration structurée, associée à des formations adaptées et à un alignement clair entre les parties prenantes opérationnelles, permet de limiter les incompréhensions et d’accélérer l’adoption. À l’inverse, lorsque la conduite du changement est négligée dès les premières phases, même des plateformes cloud techniquement solides peuvent être perçues comme risquées, freinant les déploiements et renforçant les résistances internes.

« Une formation adaptée doit être mise en place afin que les équipes ne perdent pas la maîtrise de leur infrastructure. » – Olivier Picquenot, Customer Success Director, OVHcloud

💡 Conseil de mise en œuvre :

Réaliser une évaluation de maturité cloud ou un proof of concept ciblé permet de valider la conception réseau et les mécanismes de résilience, et de poser ainsi les bases nécessaires à une évolutivité durable.

Le travail essentiel qui transforme des objectifs stratégiques en décisions d’architecture

La migration et le passage à l’échelle s’appuient sur des objectifs bien identifiés : améliorer les performances, maîtriser les coûts et accompagner la croissance. C’est dans la traduction de ces objectifs en décisions d’infrastructure que la complexité se révèle.

« L’évolutivité doit être étudiée dès le départ, notamment en tenant compte des profils de charge. L’un des principaux avantages du cloud réside dans sa capacité à s’adapter à la hausse ou à la baisse en fonction de la charge. » – Olivier Picquenot

Comprendre dès le départ les dépendances applicatives, les pics de charge, les exigences de conformité ou encore les écarts de compétences au sein des équipes est déterminant. Sans cette visibilité, les décisions liées à l’évolutivité deviennent réactives, prises dans l’urgence et sur la base d’informations incomplètes.

Les équipes doivent donc expliciter leurs hypothèses dès le début, en se posant des questions concrètes :

• Comment le trafic interne entre les services se comportera-t-il sous charge ?
• À quels moments les pics d’activité surviennent-ils et comment les mécanismes d’adaptation permettront-ils d’y répondre ?
• Quelles contraintes de conformité ou de localisation des données influencent le déploiement des ressources ?
• Les équipes disposent-elles des compétences nécessaires pour exploiter des plateformes complexes dans la durée ?

L’automatisation et les services managés apportent une réponse concrète à ces enjeux. L’Infrastructure as Code, les bases de données managées ou encore les services intégrés permettent de réduire la charge opérationnelle, tout en recentrant les équipes sur la création de valeur plutôt que sur la maintenance de l’infrastructure.

💡 Conseil de mise en œuvre :

Associer les objectifs métier à des indicateurs techniques mesurables permet ensuite, grâce à l’automatisation et aux services managés, de les traduire en une architecture réellement évolutive.

Les risques liés au passage à l’échelle et les fondamentaux opérationnels

Une croissance rapide introduit des risques opérationnels capables de fragiliser même des environnements bien conçus. Certaines pratiques fondamentales permettent toutefois de limiter ces dérives.

« La réponse la plus importante est l’automatisation. Avec des outils comme Terraform ou OpenTofu, les organisations sont prêtes à passer à l’échelle. » – Olivier Javaux

L’automatisation du provisionnement, de l’adaptation à la charge et du monitoring garantit des déploiements reproductibles et prépare les équipes à évoluer efficacement. En parallèle, la clarification des responsabilités, la standardisation des environnements et la réduction de la dispersion des outils facilitent le diagnostic des incidents, renforcent la conformité et contribuent à une meilleure maîtrise des coûts. À mesure que les équipes et les plateformes se développent, la cohérence devient un véritable levier d’efficacité.

La transition d’applications monolithiques vers des architectures en microservices ou vers Kubernetes reste complexe. Une approche progressive permet toutefois de sécuriser cette transformation. Commencer par un service à faible risque permet de valider les processus de migration, d’ajuster les mécanismes d’adaptation et d’étendre progressivement le périmètre, tout en renforçant la confiance des équipes.

💡 Conseil de mise en œuvre :

S’appuyer sur la reproductibilité constitue un levier clé pour accompagner l’évolution des plateformes. L’automatisation, la standardisation et le déploiement progressif permettent d’en assurer la stabilité dans la durée.

Éviter les coûts cachés

Les hypothèses héritées des systèmes existants, tout comme le surdimensionnement, constituent des pièges fréquents lors des migrations vers le cloud. Sans remise en question des modèles de conception, les équipes risquent de générer des coûts inutiles et d’alourdir la complexité opérationnelle.

Or, les environnements cloud évoluent rapidement. Une révision régulière de l’architecture permet de tirer pleinement parti des nouveaux services et d’adopter des modèles plus efficaces.

« Un modèle de tarification prédictif et transparent, dans lequel les clients paient uniquement pour les ressources réellement consommées, comme le stockage, la puissance de calcul ou la bande passante, permet de mieux anticiper les projets. » – Amarjit Toor

Conseil de mise en œuvre :

Éviter de reproduire à l’identique les modèles hérités implique d’évaluer en continu les services utilisés et d’ajuster l’architecture en fonction des usages réels.

Concevoir pour l’évolutivité

Réussir le passage à l’échelle des projets cloud suppose de concevoir dès le départ en intégrant la croissance, la sécurité et l’efficacité opérationnelle. Tout commence par les fondamentaux : automatiser de manière systématique et s’appuyer sur des expertises pour construire des bases solides en matière de réseau, d’observabilité, d’automatisation et de préparation opérationnelle.

Des équipes confiantes, concentrées sur la création de valeur plutôt que sur la gestion des incidents d’infrastructure, soutenues par une plateforme résiliente, c’est ce qui fait la différence sur le long terme.

Si vous êtes en phase de croissance et souhaitez que le cloud ne soit plus une source de complexité, découvrez des solutions cloud évolutives adaptées aux entreprises en développement.

Migrating to the cloud is exciting, but early decisions define what follows.

Real pressure emerges as usage grows and architectures are tested under sustained load. What worked for initial migration doesn’t always hold up when systems need to scale across regions, teams and workloads.

OVHcloud Professional Services Engineers and Customer Success Managers highlight the often-overlooked decisions that drive successful public cloud migrations and deployments, setting the foundations for scalable, future-ready growth.

What gets overlooked early, but shapes scaling later

The long-term success of a cloud project is defined in the first six to twelve months, where early infrastructure decisions often have the biggest impact and operational readiness is crucial. Networking, regional design and operational foundations may feel secondary during migration, but they quickly become central as environments grow.

Early deployments may run successfully using public interfaces and default configuration, but as services expand, internal traffic increases between APIs, databases and background processing layers. Without a private network model, that communication layer can introduce latency, expose sensitive traffic, and require disruptive redesign later.

“Security must be the priority. A simple ‘lift & shift’, without first designing a scalable and secure landing zone, isn’t enough. Ensure the correct infrastructure is in place at the beginning to account for future needs.” – Olivier Javaux, Professional Services, OVHcloud

Building resilience beyond initial deployment

Regional strategy follows the same pattern. Choices between single- and multi-availability zone (AZ) deployments influence resilience, failover behaviour and operational complexity. Changing these configurations after deployment is challenging, particularly when data dependencies and production traffic are already in place. By factoring in growth, availability requirements and business continuity early on, organisations can embed scalability and sustainability directly into their system design, alleviating the pressures of having to retrofit later.

Landing zones and operational systems are also common afterthoughts despite being central to day-to-day processes, incident response and compliance as systems scale. The key lesson is a necessary shift in focus: cloud teams need to move from simply deploying infrastructure to establishing visibility, governance and operational control from the outset. Managed services and standardised platforms further support this shift by reducing operational burden while improving oversight as environments expand.

The human dimension cannot be overstated. Proper onboarding, training and alignment across operational stakeholders helps reduce confusion and accelerate adoption. When change management is overlooked in the early stages, even well-architected cloud platforms can feel risky, slowing delivery and increasing resistance.

“The correct training must be in place to ensure teams don’t feel like they’re losing grip on the infrastructure.” – Olivier Picquenot, Customer Success Director, OVHcloud

💡 Implementation tip:

Conduct a cloud maturity assessment or small proof-of-concept to validate network design and resiliency. These early choices become the difference that scales later.

The vital work turning high-level goals into architecture decisions

Migration and scaling begin with clear objectives: improve performance, control costs, and support growth. Translating these into infrastructure decisions is where complexity creeps in.

“Scalability must be studied from the start, including load patterns. One of the main advantages of the cloud is the capacity to scale up or down depending on the load.” – Olivier Picquenot

Understanding dependencies, peak load patterns, compliance requirements and team skills gaps early on is essential. Without this visibility, scaling decisions are made reactively using incomplete information.

Teams should clarify assumptions from the start by asking practical questions early:

• How will internal traffic between services behave under load?
• What are the peak times, and how will scaling respond?
• Which compliance or data residency requirements affect where resources are provisioned?
• Does the team have the skills to operate complex platforms long term?

Automation and managed services help bridge the gap. Infrastructure-as-code, managed databases and integrated systems reduce operational overhead and frees teams to focus on building value, rather than maintaining infrastructure.

💡 Implementation tip:

Map business goals to measurable technical outcomes. Automation and managed services turn intent into scalable architecture.

Scaling risks and operational fundamentals

Rapid growth introduces operational risks that can destabilise even well-designed environments. Foundational practices can prevent these issues.

“The most important answer is automation. Using tools like Terraform or OpenTofu, organisations are ready to scale.” – Olivier Javaux

Automating provisioning, scaling and monitoring ensures repeatable deployments and prepares teams to scale efficiently. Meanwhile, defining ownership, standardising environments and reducing tool sprawl simplify troubleshooting and improve compliance as costs lower. Consistency becomes a force multiplier as teams and platforms grow.

Moving from monolithic applications to microservices or Kubernetes is complex, however, a phased migration approach helps to avoid failure. Starting with a low-risk service allows teams to validate the migration process, tune scaling and gradually expand while building confidence.

💡 Implementation tip:

Scale through repeatability. Automation, standardisation and phased delivery create stability as platforms grow.

Avoiding hidden costs

Legacy assumptions and over-provisioning are common traps in cloud migration. Without rethinking design patterns, teams risk creating unnecessary cost and operational complexity.

Cloud environments evolve rapidly. Reviewing architecture regularly ensures teams benefit from improved services and efficient patterns.

“A predictive, transparent pricing model where customers are charged only for the resources they consume, such as storage, compute power or bandwidth, allows for greater confidence in forecasting projects” – Amarjit Toor

💡 Implementation tip:

Avoid lifting legacy patterns unchanged. Continuously assess services and adjust architecture to match real-world demand.

Designing for scale

Scaling cloud projects successfully means designing for growth, security and operational efficiency from day one. Start with the fundamentals – automate relentlessly and leverage expertise when building the foundations for networking, observability, automation and operational readiness.

This is the difference that scales, resulting in a resilient platform and a confident team focused on value, rather than troubleshooting infrastructure.

If you’re navigating growth and want cloud to be one less thing to worry about, explore scalable cloud solutions for growing businesses.

For IT leaders, the important question is not whether blockchain is “the next big thing.” 

The question is actually much simpler: where and how does this technology solve real problems better than conventional architectures?

To answer that, let’s begin with the fundamentals.

What blockchain really is, and how it works

A blockchain is a distributed database made up of blocks of data linked together in chronological order. Each new block contains information about transactions or events, along with a cryptographic reference to the previous block. This creates an auditable chain of records that is extremely difficult to alter retrospectively.

Unlike a traditional database, which is generally administered by one organisation, a blockchain is maintained collectively by participants in the network. Copies of the ledger are distributed across multiple systems, helping to ensure synchronisation, resilience and transparency.

When a transaction is submitted to a blockchain network, it is checked according to the rules of that network. Once validated, it is added to a block and propagated across the network so that participants agree on the updated state of the ledger. This agreement process is known as consensus.

The result is a system designed to support tamper-resistant, highly available and traceable record-keeping.

Public, private, permissioned and permissionless blockchains

Not all blockchains work in the same way. For business leaders, one of the most important distinctions is the difference between public and private networks, and between permissioned and permissionless models.

Public blockchains are open networks. In general, anyone can access them, and they are often designed for broad transparency and participation. These networks are especially relevant where openness, interoperability and shared trust are important.

Private blockchains are restricted to a specific organisation or group of participants. They are usually designed for tightly controlled business processes, where governance, confidentiality or performance requirements call for more control.

A second distinction concerns permissions.

In a permissionless blockchain, participants can typically join the network and interact with it without prior approval, provided they follow the network’s technical rules.

In a permissioned blockchain, roles and actions are controlled more tightly. Specific participants may be allowed to read, write, validate or administer the network based on predefined permissions.

These models are not interchangeable. The right choice depends on the use case, the governance model, the sensitivity of the data, and the degree of openness required.

How infrastructure factors in

Blockchain discussions often focus on tokens, protocols or applications. But for IT decision-makers, infrastructure is just as important.

A blockchain environment depends on reliable underlying systems to ensure performance and trust. Three requirements stand out in particular: security, speed and uptime.

1. Security

Security is indispensably foundational. Blockchain networks are designed to preserve data integrity, but they still need to be protected at the infrastructure and operational levels.

This includes safeguarding validator or node environments, protecting sensitive data during processing, enforcing strict identity and access controls, and defending services against network attacks such as DDoS events. In some blockchain contexts, security solutions must also be tailored carefully so that legitimate high-volume traffic is not mistaken for malicious traffic.

2. Speed and low latency

Many blockchain applications are highly sensitive to latency. Validation, data propagation and user-facing responsiveness all depend on fast, stable connectivity.

This is especially relevant for environments such as decentralised finance, exchanges, validator operations, RPC services and analytics pipelines, where milliseconds can affect performance and user experience. The network layer matters a great deal: bandwidth, routing quality, geographic distribution and private interconnection can all influence outcomes.

3. High availability and resilience

Blockchain systems are expected to operate continuously. Downtime can mean missed transactions, degraded service, failed validations or loss of trust.

That makes blockchain’s resiliency critical. Distributed hosting, strong service-level commitments, redundancy, and multi-regional design can all play an important role in keeping blockchain workloads available and synchronised.

For IT leaders evaluating providers or architectures, blockchain should therefore be treated as both an application question and an infrastructure question.

Blockchain goes beyond cryptocurrency

Perhaps the biggest misconception for blockchain is that it begins, and ends, with crypto.

Cryptocurrency is just one application of blockchain, and it is far from the only one. The technology can also support a wide range of business and public-sector use cases where transparency, traceability, automation or shared trust are needed.

Examples include:

• Digital identity and access models, where blockchain can help verify claims or credentials while preserving control and traceability
• Supply chain tracking, where organisations need reliable visibility into provenance, handling and transfers across multiple parties
• Smart contracts, which can automate actions when predefined conditions are met
• Decentralised applications, where services run on distributed infrastructure rather than a single central backend
• Enterprise recordkeeping, where multiple stakeholders need a shared, verifiable source of truth

The key point is that blockchain is valuable beyond the fact that it is decentralised. It is valuable when decentralisation, immutability and shared verification solve a real business or operational challenge together.

A practical view for IT leaders

For IT leaders, blockchain should be approached with the same discipline as any other architecture decision.

And that means asking:

• What trust problem are we solving?
• Do we need a shared ledger across multiple parties?
• What governance model fits the use case?
• What are the performance, security and compliance requirements?
• Which infrastructure will support this reliably at scale?

In some cases, a conventional database will still be the right answer. In others, blockchain offers a stronger foundation for transparency, resilience and distributed trust.

The important thing is to move beyond the hype. Used in the right context, blockchain is a practical architectural model with applications that extend well beyond cryptocurrency.

As adoption grows, IT leaders who understand both the technology and its infrastructure requirements will be those best placed to identify where it can deliver meaningful business value.

This article is based on the guide “The Blockchain Blueprint”, available to download for free.

1/ AI has become a new paradigm in the Software Publishers ecosystem

Seizing the opportunities offered by AI is no longer just a marketing requirement. It’s now a matter of survival for software publishers. While the SaaS market has increased and has remained buoyant ever since COVID pandemic, we have seen companies streamlining the number of SaaS services they use. They had too many services (269 applications on average per company) that are still largely underused.^[1]

In this context, AI is seen as a way to make a difference. How? By consolidating the added value of software and enhancing its features. This is all the truer given that you no longer need to be a so-called “DeepTech” company to use AI. Now, any software publisher can now have a go at it, without any specific skills. In many cases, it is possible to leverage pre-trained AI algorithms to infer the model with its own data and produce results. The Franco-American startup Hugging Face, already known as the “Github of AI”, has made available more than 500,000 open-source artificial intelligence models and 250,000 sets of data.

Are you ready? Here’s what AI can bring to your application development.

2/ Organising and enriching data to better exploit it

Thanks to the combined advances of machine learning, natural language processing (NLP), computer vision and the advent of vector^[2] databases, one of the new capabilities of AI is to organise your data so that it can be immediately used: sorting, indexing and even enriching it. What previously required the rare skills of data scientist profiles is now easily within reach.

Carried out from the collection phase or retrospectively on older data, data pre-processing by AI opens up possibilities for immediate utilisation of your data, starting with AI-powered search. AI’s ability to understand both the user’s request (prompts written in natural language), and indexed content is a significant contribution to research. The aim here is to display the expected answer by directly synthesising the information and attributing it to its source, instead of using ranking criteria. This revolution was led by a long-time OVHcloud customer, the French platform Algolia. Is the next challenge for OpenAI to compete with Google in search engines?^[3]

The Retrieval-Augmented Generation (RAG) technique optimises the results of large language models (LLM) by connecting them to specific resources, without retraining the model. In short, it’s about training an AI model with a well-defined corpus of external and/or internal data that can be added on the fly, to avoid off-topic or unsourced results and other AI hallucinations.^[4]

There is, however, a barrier to model training and specialisation via RAG: the lack or difficulty of exploiting certain data due to regulatory reasons. Here, too, AI could prove to be a game-changer, with the creation of “synthetic data”^[5]: artificial data with the same characteristics as the original data by sampling and modelling their probability distribution. It has the potential to transform the way we consume, exchange and monetise data.

3/ Making sense in real time and predicting/reducing uncertainty

AI significantly improves predictive modelling by analysing historical patterns faster and more efficiently. Predictions can be refined by basing it on a higher quantity of data. Where predictive scenarios can be multiplied by playing on an unprecedented number of factors. It also becomes possible to generate and adjust these predictions almost in real time. Idenifying trends and providing valuable insights to make more informed decisions faster and with less effort.

In the field of LegalTech, the French search engine Predictice, an OVHcloud client since its beginnings, is collecting case law from courts in opendata to feed algorithms that can provide predictions on the chances of a trial succeeding based on a multitude of parameters, with the aim of guiding lawyers in developing their defence strategies.

The marketing industry is also using AI to bring brands closer to influencers. By calculating the ‘affinity’ between the audience of an account and the service or product that a company needs to market. This will then help ‘micro-influencing’ or even ‘nano-influencing’ emerging.

Applications for predictive AI are virtually limitless. Whether it’s detecting fraud, optimising clinical or consumer testing, anticipating sales changes to optimise a production line and tie up less capital in inventory, organising predictive infrastructure maintenance based on data collected by connected sensors. The list goes on.

4/ Revolutionising the man-machine interface and supporting developers

Beyond the obvious applications, for example in customer service, chatbots have become a new way of accessing data and querying it using natural language. “Prompts” enable the streamlining of application interfaces, or generation of dashboards on the fly, which eliminates the need for coding or clicking.

In short, AI could renew interest in “progressive disclosure”, a user-interface design concept popularised in 2000 by Jef Raskin in his book The Humane Interface,^[6] which is based on three principles:

• Initial simplicity: at first glance, only critical features or information are visible

• Easy access to additional information as needed by the user

• Customisation: users can adapt the interface so that it better responds to their needs and preferences (in this case, AI has the ability to do this even without the user’s intervention).

AI is changing the landscape of software development. From simple wizards to tools like GitHub Copilot, developers now have resources to create, explain, enhance, correct, and optimise their code. Both beginners and veteran developers can use these tools.

While “no code” is still a pipe dream for programming complex applications, “low code” has become the norm. Shouldn’t a developer focus more on assembling tech building blocks rather than mastering a programming language?

The combination of low Code and DevOps is particularly effective for quickly bringing an idea to fruition and boosting innovation capacity. Especially since AI also automates some of the testing, and speeds up the deployment of software.

5/ Hyper-personalisation of the customer/user experience

We did not wait for AI to start building recommendation engines. Indeed, the fields of e-commerce, music or video streaming have known a huge success. You are probably wondering what’s new is the power of customisation that AI can enable? Well, you will be surprised. It’s no longer about building groups of users with similar behaviours. It has now become possible to personalise recommendations for a single user, leaving behind the era of mass marketing.

Best of all, this customisation, coupled with generative AI, could lead to a generation of unique, individualised content. A brand might consider creating a marketing campaign whose visuals are generated from the company’s advertising history (previous campaigns, to teach the AI model the brand’s communication codes) and adapted to the user’s tastes.

And the gaming world is no stranger to this, as it is already thinking about using AI to boost interaction from non-gamers. Even astrology, which up until now has been sufficiently vague so that everyone can find a grain of truth within it, cannot resist the siren song of AI-assisted hyper-personalisation.^[7] This hyper-personalisation also brings promises in the fields of education, training or even health. Each one shares a future where one of its cornerstones will be individualised outputs, such as educational content, treatments or health advice.

6/ Assist, optimise, automate: from “Micro SaaS” to creating AI agents (agentisation)

The last big family of AI breakthroughs is probably the least spectacular in appearance, but it is undoubtedly the one that is currently driving the most innovation among SaaS publishers. In this new generation of applications, AI is permeating everywhere and playing a central role, not only as a support tool, but also as a robot capable of making decisions and executing tasks without human intervention.

In this day and age, what could be more surprising than the number of hours companies waste on Excel or PowerPoint to perform tasks that AI algorithms can now perform effortlessly?

AI has already had the capacity to handle multiple time-consuming and repetitive operations for a few years. From analysing data to generating reports, optimally planning a tour or writing a draft response to an email, AI can generate significant productivity gains within enterprise applications.

To serve its legal sector clients, Septeo Group has made AI a top priority, offering them end-to-end solutions for drawing up contracts and legal documents. The human resources industry also provides some examples of software that boosts their users’ productivity by helping them with everyday tasks.^[8]Tortus.ai, a chatbot capable of assisting a doctor during a consultation by summarising it for the patient’s record and suggesting the contents of prescriptions for further examination, shows how far automation can go, even in a field known to be complex.^[9]And for a more general purpose, the Automatic Document Reading (ADR) solution developed by Recital.ai gives an idea of future developments in electronic document management.

Finally, the trend toward “micro-SaaS”, referring to lightweight (or even cobbled-together) software solutions that serve highly targeted needs, often with the help of AI-based’ tools, shows that there are still many gaps in the professional applications market. There is ample room for improvement, and it doesn’t have to cost a fortune.

Alongside this trend of micro-SaaS, the “agentisation” of AI services appears to be an ideal solution for automating complex tasks, by using an agent to organise a workflow that chains the processing of several services through their APIs. Enough to work miracles – or almost, while we wait for a hypothetical super-intelligent entity capable of organising all these based on a simple scheme of intent.

Careful, quick time-to-market doesn’t necessarily mean acceleration

In a rush to move swiftly, there is a tendency to choose the fastest option, which involves relying on pre-built AI services and endpoints – offered directly by companies that develop widely used general-purpose or specialised AI models. Watch out for potential risks! Such as data leaks and unauthorised use of your valuable data to train models that don’t belong to you. It could ultimately benefit your rivals. This also includes the risks of getting locked into non-reversible services. Our next blog post will explore the risks involved, the state of European regulation, and the advancement of “trusted AI”.

In the meantime, you can rely on OVHcloud’s services to safely test and innovate with AI. OVHcloud, the leading European cloud provider offers various solutions for training, deploying and inferring models based on your expertise. Our offerings include on-demand GPU servers, pre-trained models to quickly integrate features into:

– your applications (AI Endpoints),

– ready-to-use work environments (Notebooks as a Service) for your developers and data scientists,

– AI Training and AI Deploy services for orchestrating compute resources to suit your needs.

Find out how to use OVHcloud AI Machine Learning to fuel your business: https://www.ovhcloud.com/en-gb/public-cloud/ai-machine-learning/

^[1] https://www.cio-online.com/actualites/lire-le-saas-une-poche-de-depenses-a-optimiser-pour-la-dsi-15510.html

^[2] https://blog.ippon.fr/2023/08/30/les-bases-de-donnees-vectorielles-2/

^[3] https://www.usinenouvelle.com/article/openai-va-devoiler-un-engine-de-recherche-to-compete-google.N2212813

^[4] https://en.wikipedia.org/wiki/Hallucination_(artificial_intelligence)

^[5] “Sky Engine AI raises $7 million for its synthetic data generation cloud solution”:

https://www.usine-digitale.fr/article/sky-engine-ai-leve-7-millions-de-dollars-pour-sa-solution-cloud-de-generation-de-donnees-synthetiques.N2206767

^[6] https://lagrandeourse.design/blog/actualites/progressive-disclosure-simplifier-linterface-pour-mieux-guider-lutilisateur/

^[7] https://madame.lefigaro.fr/astro/l-intelligence-artificielle-est-elle-le-futur-de-l-astrologie-20231231

^[8] https://meetcody.ai/fr/blog/7-principaux-outils-dia-saas-pour-les-rh-en-2023/

^[9] To discover more applications of generative AI in healthcare, read this article: https://www.mind.eu.com/health/essentiels/ia-generative-ou-sont-les-opportunites-en-sante/

Since the dawn of cloud, customers have spent hours, days and weeks deciding which cloud environment is right for them and their IT estate. The channel has been an intrinsic part of this evaluation, with partners supporting their customers and helping them make the right decision for their own specific needs.  

Today, cloud is not only a mature technology, but it’s an interoperable one as well. Organisations use multiple clouds for different applications and workloads, selecting best of breed vendors to fit their needs. This multi-cloud approach is fast becoming the norm. We recently surveyed over 500 technology buyers from the UK, and almost two thirds (62%) are actively using multi-cloud environments today. A further 23% are either transitioning to multi-cloud or exploring the possibility of multi-cloud in future.  

Although there are a number of organisations that do use a single cloud provider, a staggering 65% of organisations say that their use of multi-cloud will increase in the next two years, representing a huge opportunity for the channel. Mixing and matching workloads to the right cloud environments can bring benefits, but also brings more complexity, with integration into other applications and a greater number of endpoints to secure. Around a third (31%) of IT decision makers said that they were concerned about securing their multi-cloud environments, and 27% understood that multi-cloud would make their estates considerably more complex. 

So what does this mean for the channel? How do channel partners adapt to address these market dynamics? Partners can provide invaluable experience and value to customers, helping to tame their multi-cloud environments. By getting to know a customer’s infrastructure, helping them to choose the right vendors and giving good consultancy, channel partners can not only help customers transition to multi-cloud effectively, but build both long-term relationships and long-term margin, in particular by adding complementary support and consultancy services.  

We’re at a key inflection point and the time to act is now. Three quarters of respondents stated they need more support and expertise, with only a quarter of organisations see multi-cloud as ‘plain sailing’. Around half (46%) are ‘getting there, one step at a time’, with a fifth (20%) admitting that if multi-cloud was a relationship status, theirs would be ‘it’s complicated’.  

But whether your customer is a mature multi-cloud user or just starting down the road, it’s almost certainly a journey that they won’t diverge from, and as their complexity grows, so will a desire to seek help from channel partners. The benefits of multi-cloud are well understood: in our poll of over five hundred customers, only one believed that there were no benefits to multi-cloud. Everyone else knew that using the right cloud for the job could give you better flexibility, better portability, lower risk and better cost-effectiveness.  

And with those kinds of benefits on offer, it’s quite clear that we’re going to be living in a multi-cloud future, and the channel opportunities are there for the taking.