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Introduction
It is not today that Artificial Intelligence (IA) entered the radar of companies. 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 the AI into practice?
This decision has gained more and more weight. According to McKinsey , 78% of companies worldwide already use some form of AI in their internal processes . But even in the face of this advance, many organizations still see each other in the face of an impasse: to follow a self-hosted , with more control and personalization, or adopt cloud , with more agility and scalability?
What is in question here goes beyond technology . It involves practical issues , such as those who take care of the data, what costs are involved, how fast it is possible to scale, and especially which model fits better in the reality and goals of each company.
In this article, we will present how the two models work, where are the main differences and what to consider before making your choice. We will also bring an overview of the latest IA trends, and show how Skyone can support your business , with a flexible, secure and prepared structure to evolve with you.
Let's go?
Self-hosted and cloud models : two routes to apply
When we talk about putting artificial intelligence to actually run, we must understand that there are different ways, and they start with the way technology will be implemented and managed . Among the most adopted options today are the Self-Hosted and Cloud . Both have the potential to deliver robust results, but operate with very different logics and responsibilities .
Next, we explain what characterizes each of them, how they work and in which contexts are often applied
SELF-HOSTED MODEL : WAS UNDER YOUR CONTROL
self-hosted model , the company is responsible for hosting, executing and maintaining all AI infrastructure . This means that models are performed on their own servers , whether locally or in environments dedicated to the private cloud, with full control over data, processes and technical adjustments.
This model is often chosen by organizations that deal with sensitive information , which have strict compliance or that need a high level of customization in the algorithms . By assuming this management, the company also centralizes decisions on safety, performance and scalability, which requires qualified technical team and robust infrastructure.
Despite the complexity, Self-Hosted offers a difficult degree of autonomy to achieve in outsourced solutions -which makes it strategic for those who value absolute control and advanced flexibility.
Cloud Model : I was going as a service
Cloud model 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, storage and ready -made models via the internet , without having to set up and maintaining its own infrastructure.
This approach is ideal for business seeking speed in implementation, lower initial cost and on demand scale . Instead of worrying about AI technical operation, the team can focus on using technology to generate value, such as automating processes, extracting insights , or creating smarter experiences for customers.
In addition, the cloud facilitates constant updates and access to cutting -edge resources , with direct support from providers - which can be an important differential in accelerated innovation environments.
These two models represent different approaches , each with its most common advantages, challenges and applications. But when we put the two side by side, the differences are even more evident.
Therefore, then 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 .
Face to face: comparing both models
self-hosted and cloud models work , it is worth looking more closely at what really changes in practice between them . The choice between one or the other has a direct impact on areas such as IT structure, data management, operation scalability, system maintenance and cost control.
It is comparing these points that many companies realize which model best serves their reality or even if they prefer to consider a hybrid approach. Next, we analyze the main criteria that influence this decision.
Infrastructure and Maintenance
Self-Hosted model , the company assumes all the responsibility for the technical structure and the operation of the environment . This includes purchase and management of server, network, storage and processing, as well as maintaining all this: updates, safety, monitoring and support. This total control allows deep customizations , but requires significant investments and a dedicated technical team.
In the cloud , both infrastructure and maintenance are under the responsibility of the provider . The user company accesses the resources ready for use, with updates, corrections and availability guaranteed as part of the contracted service. The focus moves from technology management to the use of AI itself, with much shorter implementation time
Data
self-hosted solutions , data remains under the full domain of the company . This is especially relevant to organizations that deal with sensitive information or are subject to compliance requirements such as LGPD (General Data Protection Law) or financial sector standards.
cloud model , the data is processed in outdoor environments, controlled by the provider . Although major players offer robust safety patterns, this approach requires confidence in supplier policy and structure, as well as careful analysis of contracts and terms of use .
Scalability
With the Self-Hosted , expanding the operation 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 structure.
In the cloud , climbing is fast and flexible . Just adjust the contracted services to access more processing, storage or tools, almost immediately . This is useful at peak or business that grow fast.
costs
Self-Hosted model usually requires a high initial investment, with hardware , licenses and infrastructure assembly . On the other hand, costs over time tend to be more predictable as the company controls the operation.
In cloud , on the other hand, the payment model is for use . It is possible to start with little and expand as needed without large initial investments. However, this model spending control
As you can see, it is now easier to see the differences between the two models, especially when we put all the side by side criteria. To facilitate and finish, in the following comparative table
| Criterion | SELF-HOSTED MODEL | CLOUD AI MODEL |
| Infrastructure and Maintenance | Managed by the company.Exigence Investment, Technical Team and Continuous Support. | Managed by the provider. Quick and without internal management. |
| Data | Internally stored.maior control and compliance. | Processed externally. It depends on the provider's policy. |
| Scalability | Slower expansion, with the need for physical structure. | Immediate scalability as required. |
| costs | High initial investment. More predictable recurrent costs. | Low initial cost. Variable Customer as used. |
The comparison makes it evident that there is no universally better model . It all depends on what each company needs to prioritize. In some cases, the control and customization of Self-Hosted are key; In others, cloud and elasticity speak louder.
However, the decision is not limited to infrastructure or budget. To choose more safely, you need to understand where each model really delivers value , considering the business context, the demands of the industry and the degree of digital maturity of the organization.
And that is what we continue to explore below.
In the scale: When each model makes more sense?
After comparing the models point to point, it's time to get out of theory and get in everyday life . After all, the choice between Self-Hosted and Cloud goes beyond the datasheet. She goes through questions such as: What risks does my business need to avoid? How fast do we need to evolve? Do we already have the basis for sustaining an AI operation internally?
This is the key turn: Understanding when each model makes more sense, according to the moment and goals of the organization.
self-hostad model stands out
self-hosted model is often adopted when the full control of AI operation is a requirement , not just an advantage. In sectors such as financial, health and government, for example, data protection and regulatory compliance impose limits that make cloud unfeasible in certain layers of the project.
Thus, it appears as a natural choice when:
- The business involves sensitive data and strict regulatory obligations , such as banks, hospitals, insurers and public agencies;
- There is already a solid technical structure , with internal teams prepared to maintain, adjust and evolve the operation safely;
- The AI project has strategic value and requires differentiation , such as proprietary models, complex integrations or internal bases trained algorithms.
As an example of a use case, we can mention J.Hilburn , the American brand of personalized fashion, which opted for a dedicated infrastructure and under its management to process sensitive customer data with maximum security . With this approach, the company was able to reduce the order analysis time by 50% , maintaining full control over the operation.
Where the cloud shows more advantage
cloud model shines when the priority is in agility, scalability on demand and less management complexity . It fits well with contexts like:
- Startups or innovation areas in large companies that need to try, test and launch projects quickly;
- Lean teams or growing structures , which have no 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 large providers.
Another real example: Strise.ai , compliance analysis startup , migrated its models to Google Cloud and, with Datoproc and GKE , and managed to triple its processing capacity in less than five minutes .
These two scenarios only reinforce what we claim: the best choice is not on labels, but in coherence with the reality of each company . What today seems like a dilemma can actually be a starting point for thinking AI more flexibly, combining the best each model.
In the next section, let's look at this hybrid future that is already beginning to consolidate, and understand how it can unlock new possibilities for business. Continue with us!
Trends: Evolving and what comes ahead
When we talk about trends, we are not dealing with distant predictions: we are looking at decisions that are already at the center of the most modern digital strategies . Companies that previously saw AI as an isolated project now face technology as a living part of the operation, moldable, connected and, above all, adaptable to what the business needs.
In this scenario, movements arise that are redesigning as artificial intelligence is adopted, managed and evolved in companies. And most interestingly, these transformations do not come from a single way, but from the intelligent combination of different approaches . Then we highlight the five most relevant trends that are paving this new moment of AI.
- Hybrid AI as a corporate strategy : 100% cloud or 100% on-premise are getting behind. 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 companies already prioritize this mixed integration to optimize cost, safety and performance ;
- Open-Source boosts affordable innovation : Models like Llama and Mistral are paving the way for a more customizable and economically viable AI. With open source, companies can train models in their own data, adapt algorithms and avoid the “ lock ” (“locked”) of large players . Today, more than 90% of the companies they use were already incorporating Open-Source into their stack , according to Git Hub Octoverse ;
- Small Language Models (SLMS) - Practical and lightweight : instead of depending on giant and expensive models to operate, many companies are adopting SLMs, which are smaller, faster and faster models focused on specific tasks. This approach reduces computational cost and enables applications on mobile devices, sensors and local operations, with a direct impact on agility and privacy. Models like Phi-2 and Tinyllama have been leading this movement ;
- EDGE and Agentic Ai boost local decisions : Performing Ia directly on the edge (sensors, cameras, equipment) allows real -time decisions, with less latency and greater contextualization. This model is ideal for logistics, retail, manufacturing and autonomous vehicles sectors. Combined with Agentic AI (artificial intelligence with autonomy to perform tasks), this trend is redesigning as systems react to dynamic environments ;
- Open patterns and interoperability as a competitive advantage : integrating different AI models and platforms without giving up safety and governance has become a strategic priority. The Model Context Protocol (MCP), launched by Anthropic in November 2024, is consolidating itself as an interpensers, supported by players such as OpenAi and Google , which allows AI systems to exchange contextual information safely and scalably .
These trends make a clear message : AI's future is not to choose a single way, but to build a smart journey and connected to the purpose of the business. It is not just technology, but to orchestrate decisions that bring safety, 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 turn possibilities into results, with a flexible, safe and designed structure to evolve with you and your business!
Ready to choose? Skyone helps you in the decision
Skyone is more than a supplier: it is its purpose -to -purpose platform . Here we join cloud, data, artificial intelligence and security in an integrated structure, 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? Self-Hosted environments with high governance. Search Quick Scale? cloud solutions ready to grow with your business. And if you want the best of both worlds, we support hybrid architectures with full fluidity.
More than that, we offer a marketplace of AI agents ready for use (such as service assistants, recommendation mechanisms and predictive analysis), which integrate with their operation without complicating their architecture.
This combination already supports companies from sector such as retail, industry, agribusiness and hospitality to smarter decisions protect strategic data and gain real efficiency .
Are you evaluating the ideal model for your business to gain traction? Talk to a skyone expert and find out how to apply it safely, scalable and connected to your business!
Conclusion
In the end, choosing a self-hosted or cloud is not just a technical issue: it's a way to position your business to what comes ahead .
If we learn something throughout this article, the right answer depends on the moment of your business , the risks you need to mitigate and the speed with which you want (or need) to innovate. And more than that, you don't have to fit into a "predefined box."
The future of artificial intelligence will be built by companies that strategically combine technologies, freedom to climb, adapt and evolve as reality changes. And it is exactly this freedom that we at Skyone have helped you conquer , with a structure prepared for both worlds, and a team that walks along with you, from planning to execution .
If AI is already on your radar, this is the time to turn intention into motion. And to deepen this path with confidence, how about following other content on the Skyone blog? Here, we always bring articles, guides and analyzes to support each stage of your digital journey.
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-Hosted and Cloud models
The decision on which model of adopting does not always come with ready answers. Each business has its context, its rhythm and its priorities.
To help you navigate this scenario with more confidence, whether starting from scratch or refining a strategy already in progress, we have gathered the answers to the most frequent questions about AI -Hosted and Cloud .
How to build an AI model?
It all starts with the clear definition of the problem to be solved. Then 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 models, classification or predictive), trains the model, validates results and adjusts as needed.
self-hostad models , with more control over each step. Already those seeking agility and less complexity benefit from cloud , with access to models ready for use and scalable infrastructure.
Self-Hosted and Cloud models ?
The main difference is in the way technology is hosted and managed. self-hosted model , everything is under the responsibility of the company: infrastructure, security, data and maintenance. This guarantees total autonomy, but requires more investment and technical expertise.
In the cloud , AI is consumed as a service. The company accesses tools, models and resources via the internet, with lower initial cost and faster implementation. The responsibility for the technical operation falls on the cloud provider.
How to know which model is ideal for my business?
The choice depends on three main factors: digital maturity, required control level and urgency to generate AI value. Companies with rigorous safety requirements, which already have robust technical structure, tend to opt for self-hosted . Already organizations that seek flexibility, climb fast or start with less barriers prefer the cloud .
And yet: In many cases, the ideal answer is in the combination of the two. This is what we call hybrid architecture, which allows you to extract the best of both worlds.
