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Introduction
Artificial intelligence (AI) has been on the radar of companies for some time now. But as it becomes part of everyday life, from process automation to real-time data analysis, a recurring question arises: how to choose the best model to put AI into practice?
This decision is gaining increasing importance. According to McKinsey , 78% of companies worldwide already use some form of AI in their internal processes . But even with this progress, many organizations still face a dilemma: should they opt for a self-hosted cloud solutions , with greater agility and scalability?
What's at stake here goes beyond technology . It involves practical issues , such as who manages the data, what the costs are, how quickly it's possible to scale, and most importantly, which model best fits the reality and objectives of each company.
In this article, we will present how the two models work, highlight the main differences, and discuss what to consider before making your choice. We will also provide an overview of the latest trends in AI and show how Skyone can support your company with a flexible, secure structure prepared to evolve with you.
Shall we go?
Self-hosted and cloud models : two paths to applying AI.
When we talk about actually putting artificial intelligence into practice, it's necessary to understand that there are different paths, and they begin with how the technology will be implemented and managed self-hosted and cloud models . Both have the potential to deliver robust results, but operate with quite distinct logics and responsibilities .
Next, we explain what characterizes each of them, how they work, and in what contexts they are usually applied
Self-hosted model : AI under your control.
self-hosted model , the company is responsible for hosting, running, and maintaining the entire AI infrastructure . This means that the models run on their own servers , either locally or in dedicated private cloud environments, with complete control over the data, processes, and technical adjustments.
This model is frequently chosen by organizations that handle sensitive information , have compliance requirements , or need a high level of customization in their algorithms . By adopting this management model, the company also centralizes decisions regarding security, performance, and scalability, which requires a qualified technical team and robust infrastructure.
Despite its complexity, self-hosted solutions offer a degree of autonomy that is difficult to achieve with third-party solutions —making them strategic for those who value absolute control and advanced flexibility.
Cloud model : AI as a service
cloud model, on the other hand, operates based on services provided by large platforms such as AWS , Microsoft Azure , or Google Cloud . Here, the company accesses AI as a service, using processing resources, storage, and ready-made models via the internet , without needing to build and maintain its own infrastructure.
This approach is ideal for businesses seeking rapid implementation, lower initial costs, and on-demand scalability . Instead of worrying about the technical operation of AI, the team can focus on using the technology to generate value, such as automating processes, extracting insights from data, or creating smarter customer experiences.
Furthermore, the cloud facilitates constant updates and access to cutting-edge resources , with direct support from providers — which can be a significant differentiator in environments of accelerated innovation.
These two models represent distinct approaches , each with its own advantages, challenges, and most common applications. But when we put them side by side, the differences become even more evident.
Therefore, below, we will compare the main criteria that influence this decision. This will help you understand not only what changes in theory , but especially what changes in practice .
Head-to-head: comparing both models
self-hosted and cloud AI models work , it's worth taking a closer look at what actually changes in practice between them . The choice between one or the other has a direct impact on areas such as IT infrastructure, data management, operational scalability, system maintenance, and cost control.
By comparing these points, many companies realize which model best suits their reality, or even if they prefer to consider a hybrid approach. Below, we analyze the main criteria that influence this decision.
Infrastructure and maintenance
self-hosted model , the company assumes full responsibility for the technical infrastructure and operation of the environment . This includes the purchase and management of servers, network, storage, and processing, as well as the maintenance of all of this: updates, security, monitoring, and support. This total control allows for deep customizations , but requires significant investments and a dedicated technical team.
In the cloud , both infrastructure and maintenance are the responsibility of the provider . The user company accesses ready-to-use resources, with updates, fixes, and availability guaranteed as part of the contracted service. The focus shifts from technology management to the use of AI itself, with a much shorter implementation time
Data
self-hosted solutions , the data remains under the complete control of the company . This is especially relevant for organizations that handle sensitive information or are subject to compliance requirements , such as LGPD (Brazilian General Data Protection Law) or regulations in the financial sector.
cloud model , data is processed in external environments controlled by the provider . Although major players offer robust security standards, this approach requires trust in the provider's policies and structure, as well as careful analysis of contracts and terms of use .
Scalability
With the self-hosted , expanding operations means acquiring more internal resources (such as servers or licenses) and performing technical reconfigurations . This takes time and depends on the capacity of the installed infrastructure.
In the cloud , scaling is fast and flexible . Simply adjust the contracted services to access more processing power, storage, or tools, almost immediately . This is useful during peak periods or for businesses that are growing rapidly.
Costs
self-hosted model typically hardware acquisition , licenses, and infrastructure setup . In contrast, costs over time tend to be more predictable , since the company controls the operation.
In the cloud , on the other hand, the payment model is pay-per-use . It's possible to start small and expand as needed, without large initial investments. However, this model requires attention to avoid losing control of expenses as usage increases.
As you can see, it's now easier to visualize the differences between the two models, especially when we put all the criteria side by side. To make it easier and to conclude, the comparative table summarizes the main aspects that should be on the radar of anyone considering this decision:
| Criterion | Self-hosted AI model | Cloud AI model |
| Infrastructure and maintenance | Managed by the company. Requires investment, a technical team, and ongoing support. | Managed by the provider. Quick activation with no internal management required. |
| Data | Stored internally. Greater control and compliance. | Processed externally. Depends on the provider's policy. |
| Scalability | Slower expansion, requiring physical infrastructure. | Immediate scalability based on demand. |
| Costs | High initial investment. More predictable recurring costs. | Low initial cost. Variable cost depending on usage. |
The comparison makes it clear that there is no universally best model . It all depends on what each company needs to prioritize. In some cases, the control and customization of self-hosted solutions are key; in others, the agility and elasticity of the cloud are more important.
However, the decision is not simply about infrastructure or budget. To make a more informed choice, it is necessary to understand where each model truly delivers value , considering the business context, industry demands, and the organization's level of digital maturity.
And that's what we'll continue to explore next.
Weighing the pros and cons: when does each model make the most sense?
After comparing the models point by point, it's time to move from theory to reality . After all, the choice between self-hosted and cloud goes beyond the technical specifications. It involves questions like: what risks does my company need to avoid? How quickly do we need to evolve? Do we already have the foundation to support an AI operation internally?
This is the key turning point: understanding when each model makes the most sense, according to the organization's current situation and objectives.
self-hosted model stands out
self-hosted model is often adopted when complete control over AI operations is a requirement , not just an advantage. In sectors such as Finance, Healthcare, and Government, for example, data protection and regulatory compliance impose limitations that make the cloud unfeasible in certain project layers.
Thus, it appears as a natural choice when:
- The business involves sensitive data and strict regulatory obligations , as is the case in banks, hospitals, insurance companies, and government agencies;
- A solid technical structure already exists , with internal teams prepared to maintain, adjust, and evolve the operation safely;
- AI projects have strategic value and require differentiation , such as proprietary models, complex integrations, or algorithms trained on internal databases.
As an example of a use case, we can mention J.Hilburn , an American brand specializing in personalized fashion, which opted for a dedicated infrastructure under its own management to process sensitive customer data with maximum security . With this approach, the company managed to reduce order processing time by 50% , while maintaining complete control over the operation.
cloud model shows the most advantage
cloud model shines when the priority is agility, on-demand scalability, and lower management complexity . It fits well in contexts such as:
- Startups or innovation departments within large companies that need to experiment, test, and launch projects quickly;
- Lean teams or growing structures that lack the resources to operate and maintain robust environments on their own;
- Companies seeking continuous access to the latest AI technologies, with automatic updates and support from major providers.
Another real-world example: Strise.ai , a compliance analytics startup , migrated its models to Google Cloud and, with Dataproc and GKE , managed to triple its processing capacity in less than five minutes .
These two scenarios only reinforce what we've been saying: the best choice isn't based on labels, but on consistency with each company's reality . What seems like a dilemma today can actually be a starting point for thinking about AI in a more flexible way, combining the best aspects of each model.
In the next section, we'll look precisely at this hybrid future that is already beginning to take shape, and understand how it can unlock new possibilities for businesses. Stay with us!
Trends: Evolving AI and what's next
When we talk about trends, we're not dealing with distant predictions: we're looking at decisions that are already at the heart of the most modern digital strategies . Companies that previously saw AI as an isolated project now view the technology as a living part of the operation, malleable, connected and, above all, adaptable to what the business needs.
In this scenario, movements are emerging that are reshaping how artificial intelligence is adopted, managed, and evolved in companies. And the most interesting thing is that these transformations don't come from a single path, but from the intelligent combination of different approaches . Below, we highlight the five most relevant trends that are paving this new moment for AI.
- Hybrid AI as a corporate strategy cloud or 100% on-premise environments are becoming outdated. Companies are adopting hybrid architectures that combine the elasticity of the public cloud with the control of private environments, especially in regulated sectors. According to Foundry , 64% of medium-sized companies already prioritize this mixed integration to optimize cost, security, and performance .
- Open-source drives accessible innovation : models like LLaMA and Mistral are paving the way for more customizable and cost-effective AI. With open-source code, companies can train models on their own data, adapt algorithms, and avoid the " lock-in " of large players . Today, more than 90% of companies using AI already incorporate open-source into their stack , according to GitHub Octoverse .
- Small Language Models (SLMs) – Practical and lightweight AI : Instead of relying on giant, expensive-to-operate models, many companies are adopting SLMs, which are smaller, faster models focused on specific tasks. This approach reduces computational costs and enables applications on mobile devices, sensors, and local operations, directly impacting agility and privacy. Models like Phi-2 and TinyLLaMA are leading this movement .
- Edge and agentic AI drive local decisions : running AI directly at the edge (sensors, cameras, equipment) allows for real-time decisions with lower latency and greater contextualization. This model is ideal for sectors such as Logistics, Retail, Manufacturing, and autonomous vehicles. Combined with agentic AI (artificial intelligence with the autonomy to perform tasks), this trend is redesigning how systems react to dynamic environments .
- Open standards and interoperability as a competitive advantage : integrating different AI models and platforms without compromising security and governance has become a strategic priority. The Model Context Protocol (MCP), launched by Anthropic in November 2024, is establishing itself as an interoperable standard, supported by players such as OpenAI and Google , that allows AI systems to exchange contextual information securely and scalably .
These trends send a clear message : the future of AI lies not in choosing a single path, but in building an intelligent journey connected to the purpose of the business. It's not just about technology, but about orchestrating decisions that bring security, scalability, and real strategic value.
And it is precisely at this point that we at Skyone position ourselves : as a partner to help your company transform possibilities into results, with a flexible, secure structure designed to evolve with you and your business!
Ready to choose? Skyone helps you decide
Skyone is more than a supplier: it's your platform for purposeful AI. Here, we unite cloud , data, artificial intelligence, and security in an integrated framework capable of simplifying decisions and unlocking innovation with agility and confidence.
With our modular approach , you choose how and where to start. Need more control? We structure self-hosted with high governance. Looking for rapid scaling? We deliver cloud ready to grow with your business. And if you want the best of both worlds, we support seamless hybrid architectures.
More than that, we offer a marketplace of ready-to-use AI agents (such as customer service assistants, recommendation engines, and predictive analytics) that integrate into your operation without complicating your architecture.
This combination already supports companies in sectors such as Retail, Industry, Agribusiness, and Hospitality to smarter decisions protect strategic data, and real
efficiency Are you evaluating the ideal model for your company to gain traction? Talk to a Skyone specialist and discover how to apply AI in a secure, scalable, and connected way to your business!
Conclusion
Ultimately, choosing between a self-hosted or cloud-based isn't just a technical issue: it's a way to position your business for what's coming next .
If we've learned anything from this article, it's that the right answer depends on your company's current situation , the risks you need to mitigate, and the speed at which you want (or need) to innovate. And, more than that, that you don't need to fit into a "predefined box".
The future of artificial intelligence will be built by companies that strategically combine technologies, with the freedom to scale, adapt, and evolve as reality changes. And it is precisely this freedom that we at Skyone help you achieve , with a structure prepared for both worlds, and a team that walks alongside you, from planning to execution .
If AI is already on your radar, now is the time to turn intention into action. And to confidently delve deeper into this path, why not continue exploring other content on the Skyone blog? Here, we always bring articles, guides, and analyses to support every step of your digital journey.
See you in the next click!
self-hosted and cloud AI models
The decision about which AI model to adopt doesn't always come with easy answers. Each business has its own context, pace, and priorities.
To help you navigate this landscape with more confidence, whether you're starting from scratch or refining an existing strategy, we've compiled answers below to the most frequently asked questions about self-hosted and cloud AI .
How do you build an AI model?
It all starts with a clear definition of the problem to be solved. Next, it is necessary to gather and organize quality data, which will serve as the basis for training the model. With this in hand, the team chooses the most appropriate AI architecture (such as language, classification, or predictive models), trains the model, validates results, and adjusts as needed.
self-hosted AI models , which offer greater control over each stage. Those seeking agility and less complexity, on the other hand, benefit from cloud , with access to ready-to-use models and scalable infrastructure.
self-hosted and cloud AI models ?
The main difference lies in how the technology is hosted and managed. In the self-hosted , everything is the company's responsibility: infrastructure, security, data, and maintenance. This guarantees complete autonomy, but requires more investment and technical expertise.
cloud model , AI is consumed as a service. The company accesses tools, models, and resources via the internet, with lower initial costs and faster implementation. Responsibility for technical operation falls to the cloud provider.
How do I know which model is ideal for my company?
The choice depends on three main factors: digital maturity, level of control required, and urgency to generate value with AI. Companies with stringent security requirements, or that already have a robust technical infrastructure, tend to opt for self-hosted solutions . Organizations seeking flexibility, rapid scaling, or starting with fewer barriers, on the other hand, prefer the cloud .
Furthermore, in many cases, the ideal solution lies in combining both. This is what we call hybrid architecture, which allows us to extract the best of both worlds.
