Signals Fintech

Top 3 Multi-Cloud Solutions for Data-Driven Fintechs

Find out what solutions your Fintech company can use to advance its data efforts, including AI and ML tasks, while operating in the multi-cloud environment.

With cloud providers for businesses, it’s now a question of “which one,” not “if” use any. Moreover, you can keep your app or components in multiple cloud “baskets” for security or efficiency reasons.

After the “eggs” are sorted, the trick is to find the solutions capable of handling this multi-cloud ecosystem. Let’s explore what’s in the market for fintechs tapping the data power.

What multi-cloud strategy is

Multi-cloud strategy is the simultaneous use of two or more cloud computing platforms from different providers (such as AWS, Azure, Google Cloud, etc.) to meet the company’s computing needs. 

For instance, you might want to use MongoDB for your databases and Google Cloud for user management or match Microsoft’s Azure Active Directory (AAD) with Google Cloud for a single source of truth for identity. Or, run your application on one provider’s infrastructure while having it backed up on another.

You can also locate your development and test environments on one cloud and run your prod environment on another.

To fully take advantage of multi-cloud benefits, it is crucial to ensure proper multi-cloud management, which implies managing and monitoring your cloud deployment and costs.

Why go multi-cloud in Fintech

Fintech companies can leverage the services and resources offered by multiple cloud service providers to build and deploy more advanced financial technology solutions.

Multi-cloud strategy can be particularly beneficial if your company is facing one or more of the following challenges:

  • Users are dispersed across regions where data centers are not readily accessible.
  • Compliance regulations, especially in certain nations, impose restrictions regarding data storage (e.g., EU.)
  • Your company is utilizing public clouds alongside on-premises infrastructure in the operational environment.
  • Lack of resilience in cloud-based applications poses challenges for disaster recovery in the event of data center failure.

Pros of multi-cloud strategy

1. Fintech companies can use multiple cloud providers to reduce dependency on any single provider. This mitigates the risk of downtime or service disruptions if a single provider experiences issues. Also, such diversification gives you the freedom to switch providers or negotiate better terms without significant disruption to their operations.

2. A multi-cloud approach provides flexibility to choose the most suitable cloud services from different providers based on specific requirements, such as performance, cost, security, or geographic location. It can help your business to adapt quickly to changing business needs and technological advancements.

3. Infrastructure-agnostic solutions have a competitive edge to get the most out of AI and ML. By distributing AI/ML workloads across multiple cloud infrastructures, you can match each aspect of your application with the optimal environment.

5. Fintech businesses can distribute their infrastructure across different regions and data centers offered by various providers. Such distribution enhances redundancy and improves disaster recovery capabilities, reducing the risk of data loss or service interruption due to natural disasters or regional outages.

6. Your company can leverage the strengths and specialties of different cloud providers to access best-of-breed solutions for various aspects of your technology stack, such as machine learning, analytics, security, or compliance.

Cons of utilizing multiple cloud providers’ services 

1. Managing multiple cloud environments can introduce complexity in terms of integration, orchestration, and monitoring. This responsibility requires specialized expertise and resources.

2. While a multi-cloud strategy offers flexibility, it can also lead to increased costs associated with managing multiple accounts, data transfer fees between cloud providers, and potential duplication of services.

3. Each cloud provider may have different security features, compliance standards, and data residency requirements. Ensuring consistent security controls, compliance with regulations (such as GDPR or PCI DSS), and data governance across multiple clouds can be challenging.

4. Beware of compatibility issues when integrating services or applications across cloud platforms. Fintechs need to invest in interoperability solutions or adopt standardized approaches to ensure seamless communication and data exchange between cloud environments.

5. Effective vendor management processes are necessary when dealing with multiple cloud providers. This includes contract negotiations, service-level agreements (SLAs), and support mechanisms. Coordinating with various vendors adds administrative overhead and may complicate issue resolution.

3 multi-cloud solutions for your data needs
NVIDIA AI Enterprise 

NVIDIA AI Enterprise is a comprehensive, cloud-native software platform designed to revolutionize data science pipelines and streamline the development and deployment in companies running their businesses on AI. 

Built with ease-of-use in mind, this platform offers optimized model performance alongside enterprise-grade security, support, and stability, ensuring a seamless transition from prototype to production for businesses deeply invested in AI and data.

Key features and benefits:

- End-to-end solution: NVIDIA AI Enterprise provides a complete solution, from data science to deployment, accelerating the entire AI development lifecycle.

- Microservices architecture: Easy-to-use microservices, including NVIDIA NIM and CUDA-X, optimize runtime and simplify generative AI development, making it accessible to a wider range of users.

- Security and compliance: Ongoing monitoring for security vulnerabilities ensures the protection of company data and intellectual property, providing peace of mind in AI-driven environments.

- Cloud-agnostic: Certified containerized microservices can be deployed on any certified cloud service provider (CSP), enabling a cost-effective and flexible multi- or hybrid-cloud strategy.

- Predictable production branches: API stability and NVIDIA Enterprise Support help maintain project integrity and ensure smooth operations during development and deployment.

IBM Cloud Pak for Data

IBM Cloud Pak for Data is a comprehensive data platform streamlining data collection, organization, and analysis. It offers an open and flexible multi-cloud architecture.

This solution has a strong focus on seamlessly integrating AI into data architecture. By simplifying data handling and analysis, this platform empowers users to deploy AI models effortlessly and scale insights with ease, making it the ideal choice for organizations seeking to tap the power of AI in their data workflows.

Key features and benefits:

- High personalization: IBM’s Cloud Pak for Data has a customizable dashboard that allows users to tailor their data management experience according to their preferences.

- Robust core: The solution encompasses built-in data virtualization, automated AI lifecycle management, and an open and flexible multi-cloud architecture. 

- Integration capabilities: IBM Cloud Pak for Data seamlessly integrates with a range of IBM services, including Watson Studio, Watson Machine Learning, and Watson Knowledge Catalog, providing users with a comprehensive AI-driven ecosystem.

Databricks

Databricks is a multi-cloud solution for AI and machine learning. Based on Apache Spark, this distributed computing platform accelerates data processing for enterprises of all sizes.

Key features and benefits:

- Multi-cloud management: Databricks seamlessly manages multi-cloud environments, enabling enterprises to deploy clusters across public or private cloud infrastructures — or a combination of both. This flexibility empowers organizations to adapt to evolving requirements while optimizing resource utilization.

- Advanced analytics capabilities: A suite of tools is quite rich, including SQL, Python, R, and Scala, which empowers users to perform advanced data analysis. Integrated with popular machine learning libraries like TensorFlow and PyTorch, Databricks facilitates the development of AI models for predictive insights.

- Collaborative workflows: Databricks' integrated notebooks facilitate collaboration among data scientists, engineers, and analysts, streamlining the AI model development process. With seamless integration of various programming languages and libraries, teams can efficiently create, share, and iterate on models.

-  AI and ML at scale: Leveraging Databricks' distributed computing clusters, enterprises can easily train large-scale AI models. Moreover, robust data pipeline monitoring and management capabilities ensure optimal model performance in production, enabling organizations to deploy, scale, and maintain AI solutions effectively.

Wrapping it up

While a multi-cloud strategy offers several benefits, such as reduced dependency, flexibility, and geographic redundancy, fintech companies must carefully consider the associated complexities, costs, security implications, and management challenges before adopting this approach. The same applies to choosing solutions to support your data activities in the multi-cloud context.

It's essential to weigh the pros and cons against specific business requirements and objectives to select a solution that best fits your organization. Our Fintech Engineering Startup Studio & Innovation Lab can give you a hand with that. Let’s discuss how your business can do more and better with less.

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