AI Infrastructure Solutions: SaaS vs. Self-Managed Setups (On-Prem & VPCs)

Key Takeaways

• AI infrastructure comprises hardware, software, and network resources for developing, training, and deploying AI models.
• You can deploy AI applications in third-party or self-managed setups, such as Virtual Private Clouds (VPCs) and on-premise.
• SaaS offers lower upfront costs and easier management but has limited customization and potential security concerns. 
• Self-managed solutions typically provide more control and security but require higher initial investment and expertise.
• Factors to consider when choosing AI infrastructure include scalability, performance, data security, regulatory compliance, and cost efficiency.
• We recommend self-managed solutions for businesses in highly regulated industries.

What Is an AI Infrastructure? 

AI infrastructure refers to the underlying hardware, software, and network resources needed to develop, train, deploy, and run artificial intelligence (AI) applications and machine learning models. 

This includes:

• Computing resources like Tensor Processing Units (TPUs) and Graphics Processing Units (GPUs)
• Data storage and databases
• Networking capabilities
• AI platforms and frameworks
• Security and governance tools

All of these resources can affect various aspects of your business, including data privacy and security, regulatory compliance, machine learning model performance, total cost of ownership, and integration with existing systems.

Having a strong AI infrastructure is especially important for highly regulated industries like healthcare, finance, and insurance. These industries handle sensitive personal and financial data, which makes data privacy and security key.

Key Parts of an AI Infrastructure

A robust AI infrastructure is made up of various parts, such as:

1: Computational Power

This includes hardware like General Processing Units (GPUs), Tensor Processing Units (TPUs), and specialized AI chips. High-performance hardware is essential for processing large datasets quickly and accurately.

• For example, hospitals will likely need high-performance GPUs to process medical imaging data for AI-powered diagnostics. These GPUs accelerate the analysis of CT scans and MRIs, enabling quicker and more accurate diagnoses.

2: Data Storage and Management

Efficient data storage systems are necessary for handling and analyzing vast amounts of data.

• Insurance companies, for example, may use cloud-based data lakes to store and analyze vast amounts of policyholder data for risk assessment. 

3: Networking and Connectivity

High-speed, low-latency networks are essential for seamless data transfer and communication. Reliable and fast networks are crucial for real-time data processing and communication between AI components.

• Financial trading firms, for example, rely on ultra-fast networks to execute AI-driven algorithmic trades in milliseconds. These high-speed connections are critical for maintaining a competitive edge in trading markets where every millisecond counts​.

4: AI/ML Frameworks and Tools

Frameworks like TensorFlow and PyTorch provide the tools and libraries needed to efficiently develop and deploy AI models.

• For example, banks use machine learning frameworks to develop AI fraud detection and credit scoring models. These frameworks let businesses build sophisticated models that can analyze transaction patterns and identify fraudulent activities in real time​. 

5: Security and Compliance Tools & Techniques

Tools and techniques like encryption, access controls, and audit trails protect sensitive data and ensure regulatory compliance. Robust security measures are essential to protect data integrity and comply with industry regulations.

• As an example, healthcare providers should implement robust security measures to protect patient data used in AI applications and ensure HIPAA compliance. This includes encrypting data, setting strict access controls, and maintaining audit trails to monitor data usage and access​.

What to Look for In AI Infrastructure Solutions

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Here are the primary factors to consider when choosing your AI infrastructure solutions:

Scalability

Your infrastructure should be able to scale up or down based on the workload and data volume. This will allow it to handle varying amounts of data without significant downtime or decreased performance. 

• For example, in the insurance industry, scalable AI solutions can efficiently process large volumes of claims data during peak periods.

Performance

High-speed processing and low latency are critical for real-time AI applications because they directly impact the efficiency and responsiveness of AI systems. Performance considerations include the types of computing resources used and the speed of data access and transfer.

• For example, financial trading firms need high-performance AI systems to execute trades in milliseconds and maintain a competitive edge.

Data Security

Our clients from healthcare, finance, and insurance often mention concerns about data security—and rightfully so, considering they deal with sensitive and personal information.

This is why we typically recommend deploying AI on a setup they manage themselves. 

Self-managed AI infrastructure lets businesses implement customized security measures, including encryption and access controls, restricts third-party access, and minimizes security risks.

In contrast, third-party AI infrastructure typically offers less flexibility and control over data protection and compliance measures. It also gives data access to additional parties, which increases the risk of breaches. 

Regulatory Compliance

Note that the AI infrastructure needs to comply with relevant industry regulations, such as HIPAA in healthcare or AML in finance.

Cost Efficiency

Cost is a more complicated factor since it highly depends on what type of AI infrastructure you’ll use and whether you’ll manage it yourself. 

If you’ll deploy AI on your on-premise infrastructure, evaluate aspects like:

• The total cost of ownership, including initial setup fees 
• Maintenance costs
• Operational costs

Although the cost of on-premise AI infrastructure can seem difficult to justify, it has important benefits over using third-party solutions—especially when it comes to maintaining control over your data. 

The most balanced choice, however, would probably be using a self-managed virtual private cloud (VPC). It requires smaller upfront investments and typically has lower maintenance costs. It also offers a higher level of security than a setup managed by a third party.

Flexibility and Customization

Customization is another factor to consider, as each business has its unique challenges and goals. With that in mind, you should choose an AI infrastructure you can tailor to your needs.

For example, you should be able to easily add new features and integrate your AI infrastructure with different data sources and existing systems.

2 Main Types of Infrastructure Solutions

The first thing to decide is whether you want to deploy AI applications in a setup you’ll manage yourself or have it managed by a third party (SaaS). Let’s take a closer look at each approach.

1: SaaS

When deployed as a SaaS solution, AI applications are hosted and managed by a third-party provider and made available to customers over the Internet. This cloud-based model allows businesses to use advanced AI tools without needing to invest in or maintain their own hardware and infrastructure.

In this case, you can expect to: 

• Pay recurring, often monthly fees.
• Have the provider handle all maintenance, updates, and security patches, reducing the burden on your organization’s IT team.
• Easily scale up or down based on demand, which is useful for fluctuating workloads.
• Share the infrastructure with other customers.

2: Self-Managed Environments 

The second option is to deploy and manage AI on infrastructure that you either own or manage yourself.

There are two main types of self-managed environments:

Self-Managed VPCs

A VPC (Virtual Private Cloud) is an isolated section of a public cloud provider's infrastructure dedicated to a single client. 

This setup lets your business leverage the benefits of cloud computing and maintain tighter control over data security and compliance than the SaaS approach.

In fact, many people consider cloud infrastructures to be more secure even compared to on-premise infrastructures, since reputable cloud providers heavily invest in security. However, keep in mind that VPCs are not under your direct control—at least not fully. 

So, our verdict is that going with self-managed VPCs is almost always better than the typical SaaS approach, but may be either inferior or superior to on-premise infrastructures, depending on how well organizations set up and maintain them. 

On-Premises

On-premise solutions refer to software and hardware physically located and managed within an organization's facilities. It provides full control over the infrastructure, including hardware, software, and data. 

• On-premise software can be tailored to your business’s specific needs, providing flexibility to optimize and functionality for unique requirements.
• All data and processing remain within the organization's physical boundaries.
• Having full control of your infrastructure sounds great on paper, but it requires significant upfront investment in hardware and ongoing maintenance.

With all these options available, choosing the right one for your business is overwhelming. To make things easier, we’ve compiled a list of pros and cons for each choice.

SaaS vs. On-Premise AI Solutions

Pros and Cons of SaaS

Some advantages of going down the SaaS route include:

• Scalability: SaaS infrastructure allows businesses to quickly scale computing resources up or down based on demand without purchasing additional hardware. For example, an insurance company can expand its claims processing capacity without infrastructure upgrades.
• Cost-effectiveness: Lower upfront costs as there's no need for expensive hardware and infrastructure investments. A startup fintech company can offer banking services using SaaS solutions, avoiding costly initial IT infrastructure.
• Accessibility: SaaS infrastructure enables access to services from multiple locations via the Internet, promoting remote work and collaboration. This means doctors can access patient records securely from multiple hospitals or clinics.

However, some cons are:

• Limited control over underlying infrastructure: Businesses have less ability to customize hardware or network configurations to specific needs.
• Data security and privacy concerns: Data resides on shared infrastructure, which may not meet the stringent security requirements of some industries. For example, insurers might face challenges in ensuring policyholder data is sufficiently protected on shared systems.
• Dependence on Internet connectivity: Service availability relies on a stable Internet connection, which could impact critical operations. For example, if Internet connectivity is lost, a clinic's operations could be disrupted, which prevents access to patient records.

Pros and Cons of On-Premise AI Solutions

Another option is on-premise AI, which provides advantages such as:

• Full control over data and infrastructure: On-premises software lets businesses maintain full control over their hardware, software, and data. This level of control ensures that the infrastructure can be tailored to meet specific security, compliance, and operational requirements​.
• Enhanced security and compliance: Keeping data on-premises enhances security, as it eliminates the risks associated with transmitting data over the internet. For finance companies, deploying a loan origination solution within on-premises infrastructure ensures enhanced data security and compliance with financial regulations. 
• Customizable to meet specific business needs: On-premise AI infrastructure can be customized to fit the exact needs of the organization, allowing for greater flexibility in how the technology is deployed and integrated with existing systems

But it also comes with downsides like:

• Higher initial investment and operational costs: Deploying on-premise AI infrastructure requires significant upfront capital to purchase hardware and software. Additionally, ongoing maintenance costs can be substantial.
• Requires significant in-house IT expertise: Managing on-premise infrastructure requires a skilled IT team to handle installation, configuration, maintenance, and troubleshooting. Without a robust IT department, this can be a massive challenge for businesses​.
• Longer deployment times and maintenance responsibilities: Setting up on-premise AI infrastructure can take considerable time due to the need for hardware installation and software configuration. Ongoing maintenance is also the responsibility of the organization, which can be resource-intensive.

SaaS vs. Company-Managed VPC

Deciding between deploying the AI on your provider’s cloud and your own VPC involves considering the trade-offs between security, control, and cost. Each approach serves different organizational needs and preferences.

Pros and Cons of Company-Managed VPC

You might consider going with a self-managed VPC, which has pros such as:

• Enhanced security and compliance: A company-owned VPC protects sensitive information in a dedicated environment.
  Customized security configurations: Organizations can implement tailored security protocols that go beyond standard offerings. For example, insurance companies can create custom security rules for different types of policyholder data, ensuring appropriate protection levels for various data categories.
• Compliance and auditing: Company-owned VPCs allow for easier compliance with industry regulations. Healthcare organizations can maintain detailed audit logs of all data access - essential for HIPAA compliance.

Although, a few disadvantages include:

• Ongoing management overhead: Maintaining VPC infrastructure requires continuous monitoring and adjustment. This is because regular updates to routing tables, access controls, and network policies are necessary.
• Initial setup complexity: Designing and implementing a custom VPC architecture requires significant planning and expertise, as network topology and security groups need to be carefully configured.
• Disaster recovery complexity: Implementing robust disaster recovery solutions for VPC infrastructure requires additional planning and resources. Careful planning is also required when replicating VPC configurations across regions or zones. For example, an insurance company may face challenges in replicating its claims processing VPC across regions while adhering to various state regulations.

On-Premise vs. Company-Owned VPC

The key differences between the two types of self-managed environments include:

• Control and customization: On-premise solutions offer maximum control over the entire infrastructure, including hardware, software, and data. Company-owned VPC also offers a lot of over-network configurations and security settings within the cloud environment but is slightly more limited. This is because the cloud provider still manages the underlying physical infrastructure.
• Scalability and flexibility: On-premise AI has limited scalability that depends on the physical resources available within the organization, meaning that you might need to purchase your own IT infrastructure. Meanwhile, internal VPCs leverage the inherent scalability of cloud infrastructure. 
• Cost and resource management: On-premise implementation involves high initial capital expenditure for purchasing hardware and setting up the infrastructure, while client-owned VPC typically involves lower upfront costs since the infrastructure is rented rather than purchased.

Both are great options for healthcare, insurance, and finance businesses, but the choice will ultimately depend on your business's unique goals and challenges.

AI Infrastructure vs. Conventional IT Infrastructure 

AI infrastructure differs significantly from conventional IT systems in terms of performance requirements, scalability, and the nature of the workloads it supports. Understanding these differences is crucial for organizations aiming to integrate AI effectively.

Key Differences

• Performance: AI infrastructure requires high-performance computing resources such as GPUs for their parallel processing capabilities, whereas conventional IT systems focus on general-purpose computing with standard GPUs.
• Scalability: AI infrastructure supports dynamic scaling to handle large datasets and varying computational demands. In contrast, conventional IT systems are less flexible and have fixed capacity (unless upgraded).
• Specialization: AI infrastructure involves specialized tools and frameworks for AI development, such as TensorFlow and PyTorch. Meanwhile, conventional IT systems use general-purpose tools and software that aren’t optimized for AI workloads.
• Security: AI infrastructure requires robust security measures to protect sensitive data and comply with regulations. On the other hand, conventional IT systems use standard security protocols that cannot keep up with more advanced security compromises.

Implementing AI Infrastructure Solutions in Your Business

Regulations and data privacy are prominent issues for many industrie, so having full control over your AI infrastructure is often a must. That’s why we typically recommend going with the self-managed approach.

We, however, offer clients to choose between the two options themselves; we can deploy your AI applications either on our or your infrastructure. What matters is that you understand the trade-offs with both approaches. 

If you’re interested in learning more about how we can help you implement AI, please book a 30-minute demo with one of our experts.