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AI is gradually moving from isolated experiments to full-scale use in business processes. Companies are developing corporate AI assistants, automating document workflows, integrating AI into customer service, analytics, manufacturing processes, and computer vision.
According to the AI Focus 2026 study, which surveyed 207 Ukrainian companies, 93% of respondents are already using AI, while 62% report a positive impact on productivity and business performance. According to a Gartner forecast, by the end of 2026, up to 40% of enterprise applications will feature integrated task-specific AI agents.
At the same time, a practical question arises: where should AI models be deployed, how can they be provided with the required resources, integrated with corporate data, and how can costs be controlled as workloads scale?
When AI is used as an external service for individual tasks, companies usually do not need to manage the underlying infrastructure. However, corporate AI assistants, RAG systems, document automation, and computer vision require integration with internal data, stable performance under load, and controlled access.
Resource requirements also vary at different stages of a project. Model testing, adaptation, and processing real-world requests generate different workloads, which means the infrastructure configuration needs to be selected and adjusted according to actual requirements.
At this stage, companies need to determine where the model will run, what GPU resources it requires, how the environment will scale, and how costs will be controlled. As a result, an AI project becomes not only a matter of choosing the right model but also a full-scale infrastructure challenge.
The Cloud for AI Tasks provides access to GPUs within a ready-to-use cloud environment. The required vGPU, CPU, RAM, storage, and network resources are allocated based on the needs of a specific project.
This environment can be used to:
PARKOVYI Data Center provides a public cloud powered by NVIDIA L40S GPUs, as well as private cloud environments configured according to the requirements of specific projects.
The public cloud is suitable for quickly launching pilot projects, testing models, and handling workloads that vary over time. A private environment can be used for systems with increased requirements for isolation, security, and performance.
This approach allows companies to get started without purchasing, deploying, and subsequently upgrading their own GPU hardware. Resources can be allocated for a specific task, while the configuration can be adjusted once actual performance data becomes available.
A company can create an assistant for working with contracts, policies, instructions, and technical documentation.
In a RAG system, the model does not simply generate an answer. It first retrieves the relevant information from the corporate knowledge base. For example, an employee may ask about the procedure for approving a contract. The system finds the current policy, generates a concise answer, and provides a link to the source.
In this scenario, GPU resources ensure that the model can process real user requests quickly.
AI can recognize invoices, completion certificates, contracts, and other documents, determine their type, and transfer the required data to accounting or business systems.
Such workloads are often uneven. For example, the number of documents may increase significantly at the end of the month. In a cloud environment, resources can be increased during peak periods and then reduced back to the baseline configuration.
A model can analyze customer requests, identify their subject, search for relevant information in a knowledge base, and prepare a draft response for an operator.
At the initial stage, it is better to leave the final decision to an employee. This makes it possible to assess response quality, collect performance data, and gradually automate routine operations.
A GPU cloud can be used to train and run models that analyze images or video. Possible use cases include product defect detection, object recognition, production process monitoring, and video analytics.
During the pilot stage, the team can test several models and configurations without purchasing separate hardware for each option.
A cloud model is particularly suitable when:
An in-house server may be justified for stable 24/7 workloads when the configuration has already been validated and the company has a team responsible for maintaining the infrastructure.
The comparison should not be limited to GPU rental costs versus the purchase price of hardware. The total cost of owning infrastructure also includes delivery, deployment, power supply, cooling, administration, redundancy, repairs, and future upgrades.
It is also important to consider the risk of hardware downtime and the time required to replace or expand the infrastructure. In a cloud environment, the configuration can be changed without purchasing a new server.
An AI project should start not with selecting the maximum GPU configuration, but with defining a specific business task.
Before launch, the following should be determined:
For example, an AI assistant can be evaluated based on answer accuracy and information retrieval time. For document processing, relevant metrics may include the percentage of correctly recognized fields and the cost of processing a single document. For customer service, the metrics may include response preparation time and the percentage of recommendations accepted by operators.
Not every project requires model fine-tuning. When working with corporate documents, it is often more practical to start with RAG, test retrieval and response quality, and only then determine whether additional model adaptation is necessary.
This approach helps select resources based on the actual task rather than overpaying for a configuration whose capabilities will not be fully used.
PARKOVYI Data Center provides cloud resources for testing, training, adapting, and deploying AI models. The team helps select the appropriate vGPU configuration, deploy the required environment, and prepare the infrastructure for further scaling.
To create an initial configuration, it is enough to provide:
Based on these parameters, a test environment can be prepared, a pilot project can be conducted, and the actual performance and cost of the future AI service can be evaluated.
Learn more about the Cloud for AI Tasks from PARKOVYI Data Center and submit a request to select the right configuration for your project.
