in Blog

April 04, 2023

How to choose the best platform for AI cloud deployment?


Artur Haponik

CEO & Co-Founder

Reading time:

10 minutes

According to Grand View Research, the global AI market is expanding at a CAGR of 40%. [1] This monumental growth can be attributed to the increasing need to streamline processes and cut costs by automating key processes. To achieve this, organizations need to deploy relevant AI models to meet their needs.

Traditionally, organizations focused primarily on on-premises deployment. But, due to the costs and scalability limitations presented by on-premises deployment, businesses are now looking for more feasible solutions. That’s where cloud platform AI deployment comes in.

Cloud platform deployment offers an efficient, cost-effective solution for all your AI needs. With that said, most cloud platforms are designed to handle specific AI use cases and enterprise solutions, thus necessitating the need to find an appropriate platform for deploying AI models.

This guide will explore the various factors you need to consider when choosing a cloud platform. It also provides a detailed look at the various cloud service models on the market. But first, let’s take a look at what AI cloud deployment is.

What is AI cloud deployment?

AI cloud deployment is the process of deploying AI solutions like machine learning and deep learning models to cloud-based infrastructure. The process involves everything from implementing the model to operating workloads on the cloud.

Types of cloud deployment models

There are four different types of cloud deployment models, each uniquely suitable for different use cases. They include:

  • Private Cloud
  • Public cloud
  • Hybrid cloud
  • Community cloud

Types of cloud deployment models, infographic

Private cloud

A private cloud is a cloud infrastructure owned and operated by a single organization. These infrastructures can be hosted either internally or externally, depending on the organization’s needs and preferences. Private cloud infrastructures typically offer greater control over data security, making them suitable for organizations with high-security demands.

Public cloud

Public cloud infrastructures are owned and operated by third-party service providers who manage and maintain the infrastructure. These infrastructures are typically available as paid services. They are mainly used for general purposes such as computing, hosting websites, or running software applications. You can also use public cloud infrastructures for storage.

Hybrid cloud

Hybrid cloud infrastructures are a combination of public and private cloud infrastructures. Essentially, organizations leverage both private and public infrastructures for different applications. For instance, an organization may use the public cloud to make sales and interact with customers and a private cloud to store data.

Community cloud

A community cloud infrastructure is a type of cloud computing infrastructure that is mutually shared among organizations with similar needs, such as security, compliance, or privacy requirements.

Community cloud infrastructures are typically owned, managed, and operated by a third-party service provider, with access limited to a specific group of users. This type of cloud computing architecture is suitable for organizations that need centralized computing capabilities to share data and execute their projects, such as banks and trading firms.

Read more about Relation between cloud computing and artificial intelligence

Types of cloud service models

Cloud services provide businesses with scalable and cost-effective solutions to meet their computing needs without the need to invest in expensive hardware and the skilled workforce required to maintain it. There are three types of cloud services that offer different service models. These service models include:

Types of cloud service models

Infrastucture as a service (IAAS)

IaaS service models typically provide the fundamental building blocks of basic computing infrastructure. This includes everything from servers and storage to networking and visualization. Here, the service provider manages the infrastructure and grants access to users through the internet.

You can access the services through an API, which enables you to control the operating system, applications, and middleware. IaaS services are typically charged on a pay-as-you-go model, whereby you’re billed according to factors such as the amount of processing power you consume over a given timeframe or the volume of storage resources you use.

Software as a service (SAAS)

SaaS has the largest market share in cloud computing, with experts predicting it to reach $883.34 by 2029. [2] This service typically provides access to applications over the internet. These applications are generally managed and maintained by the service provider, thus relieving your organization of any installation and maintenance requirements.

Software as a Service model is billed based on several factors, including usage time, number of users, number of transactions processed, and volume of data stored.

Platform as a service (PAAS)

PaaS models offer the benefits of both IaaS and SaaS on a single platform. Essentially, the model offers a cloud-based platform where organizations can deploy applications without having to manage the underlying infrastructure. [3]

Key factors to consider when choosing a cloud platform for AI deployment

Before choosing a suitable service provider, you first need to evaluate your specific business needs. This way, you’ll be comparing the service providers based on an objective checklist rather than comparing them against each other. Once you have that figured out, consider the following factors before settling on a specific model:

Key factors to consider when choosing a cloud platform for AI deployment

Certifications and standards

This is one of the most overlooked factors among organizations choosing a cloud platform for AI deployment. While certifications and standards don’t necessarily determine a platform’s quality, they serve as clear indicators that the service provider adheres to industry best practices and standards.

Data governance and security

According to a report by IBM and Ponemon Institute, it takes an average of 277 days for security teams to identify and contain a cybersecurity threat. [4] Therefore, you need a service provider with a robust security system designed to limit the possibility of breaches as well as identify and contain them as soon as they happen.

Everything from the location of your service provider’s servers to the subsequent laws they are subjected to can play a major role in determining the provider’s security readiness. If you have sensitive data, you should look for a provider that gives you control over the geographical area where your data is stored, processed, and managed.

The ideal platform should have the ability to protect data both in transit and at rest through encryptions to limit exposure to unapproved administrator access. Additionally, the provider should have risk-based security protocols that support your organization’s security policies and processes.

Technologies and service roadmap

Before settling down on a specific service provider, you need to ensure that the platform and preferred technologies implemented on the service align with your business environment and support your cloud objectives.

While you’re at it, you need to consider whether the provider’s services, standards, and architectures suit your workloads and management preferences. You also need to consider how much customization you may need to do to make your organization’s workloads suitable for the platform.

On the bright side, many service providers offer migration services during the assessment and planning phase. With that said, some large-scale public service providers offer limited support. Therefore, you may need third-party support to fill the skill gaps.

The provider’s roadmap for service development also plays a crucial role in determining its suitability for your intended purposes, especially in the long term. This is especially vital if you plan to use one cloud service provider. In that case, you should choose a service provider that offers a wide range of compatible services.

Reliability and performance

You can measure the reliability of a service provider by checking their performance against their SLAs (service-level agreements) for the last 6 to 12 months. [5] Most service providers publish these stats in public, but you can always request to see them.

While you’re at it, you shouldn’t expect absolute perfection. Downtime in cloud platforms is inevitable, and every service provider will experience it at some point. What really matters is how the provider deals with the downtime. Therefore, you need to ensure that the service provider has an established, effective process for dealing with planned and unplanned downtimes.

Data recovery is also a crucial aspect to consider. You should understand the provider’s data recovery provisions and assess their ability to support your data preservation expectations. This includes everything from the criticalness of data to scheduling and integrity checks, as well as backup and restore capabilities.

Your adoption agreement should also include provisions for the burden of proof, as your team may be responsible for implementing some of these processes.


All service providers have unique pricing models. They compute cloud costs differently, making it virtually impossible to make a side-by-side comparison. To get around these limitations when comparing costs, you first need to consider your organization’s requirements, then decide which pricing model suits your needs.

In most cases, long-term consumption timelines are better priced. Additionally, different organizations offer different cost assessments when scaling up or down. Some also have hidden charges, so you should carefully scrutinize the contract before making your decision.

The benefits of deploying AI models in the cloud

Improved data management

The average organization manages about 162.9 TB of data. This is even higher for enterprises with an average of 347.56TB of data. [6] Managing all this data on an on-premises platform requires a lot of infrastructure, which after some time, becomes obsolete, forcing you to scale your infrastructure, which translates to higher operational costs.

Cloud computing offers robust data management solutions by providing scalable data storage solutions. Using cloud service providers can also help streamline certain repetitive tasks like identifying, classifying, and indexing different types of data.

Reduced capital expenditure

Cloud service providers offer on-demand scalability, which saves a tremendous amount of money on infrastructure costs. Additionally, some service providers offer pay-as-you-go pricing, which goes a long way in reducing operational costs.

Improved flexibility

Cloud platforms offer a wide array of tools and services that can be customized to meet your unique needs. For instance, most major service providers like Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure offer a wide variety of AI services like natural language processing tools and computer vision services that can be easily integrated into your AI model to broaden its capabilities. [7]

Some service providers also offer development and deployment tools like Jupyter notebooks and containerization tools, making it easier to design, build, and deploy AI models.


Cloud platforms enable collaboration among data scientists, developers, and business stakeholders by providing a centralized location for AI development, testing, and deployment. For instance, development applications like Jupyter notebooks and collaborative coding tools allow teams to work together and share ideas in real time.

Developers can also integrate the cloud platform with source control tools like Git, which allows development teams to share code changes and collaborate on development workflows.

Final thoughts

Deploying AI models on the cloud offers more benefits than on-premises deployment. It is much cheaper and lessens your operational responsibilities since you don’t have to manage and maintain the platform.

With that said, not all cloud platforms can serve your organization’s intended purposes. Most cloud platforms are specially designed to handle specific use cases, thus necessitating the need to choose a platform that aligns with your objectives.

Considering the factors discussed above will help you choose a cloud platform that fits your budget and promotes your overall business goals. Check out our AI consulting services to find more.


[1] AI Market. URL: Accessed March 29, 2023
[2] Saas Market. URL:  Accessed March 29, 2023
[3] Paas. URL: Accessed March 29, 2023
[4] Data Breach. URL: Accessed March 29, 2023
[5] Outsourcing SLA. URL: Accessed March 29, 2023
[6] Data Analysis 2016. URL: Accessed March 29, 2023
[7] Comparing Machine Learning as a service: Amazon, Microsoft Azure, Google
Cloud AI, IBM Watson. URL: Accessed March 29, 2023


Artificial Intelligence