Multi-Cloud Deployment: Multi-Cloud Definitions + Tips for Consistent Infrastructure
Moving to the cloud is no small feat, especially for enterprise-scale infrastructure. And just imagine the complications when you need to deploy across more than one cloud. Multi-cloud deployment is sometimes characterized by slow, error-prone workloads, lack of consistency, and inflexibility that holds users back.
In this blog, we’ll provide clear definitions, use cases, benefits, challenges, and factors to consider for multiple-cloud deployment. Then we’ll close by laying out how scalable multi-cloud automation makes it all so much smoother.
What is Multi-Cloud Deployment? Multi-Cloud Definition + Examples
Multi-cloud deployment is when one organization deploys resources like infrastructure, platform, or software across multiple public and private cloud providers, like Amazon AWS, Microsoft Azure, Google Cloud, and others.
There are three basic types of resources provided in the cloud:
- Infrastructure as a Service (IaaS): IaaS includes basic building blocks of infrastructure you’d find at a data center, like virtual machines (VMs), storage, and network function virtualization (NFV).
- Platform as a Service (PaaS): PaaS is when the cloud provides the middleware needed to develop and manage applications. That includes things like database services, content delivery and web services, artificial intelligence and machine learning (AI/ML) services, and inter-application messaging.
- Software as a Service (SaaS): SaaS is the delivery of an entire usable application. Netflix, SalesForce, Concur, and countless other apps are SaaS.
The Benefits of Multi-Cloud Deployment
Organizations do multi-cloud deployment because deploying across clouds provides choice of functions, specific strengths, and cost savings.
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No single cloud provider does everything an organization might need to do in the cloud. If one tool does some of the things you need while another does the rest (or a combination), multi-cloud deployment might be the only way to operate in the cloud. Having to choose between one tool and another might make cloud deployment impossible or unrealistic, especially for larger organizations with more complex or diverse infrastructure (like information technology in government, for example).
Also, some cloud providers may be better known for specific services than others. Amazon Web Services (AWS), for example, is the most established cloud platform on the market, so it has a larger user base and a reputation for reliability. If you need search built into your infrastructure, Google Cloud Platform (GCP) is king – it uses the same infrastructure as Google Search and YouTube.
Another benefit of multiple-cloud deployment is its ability to help control cloud computing costs. AWS, Azure, and Google each have their own pricing structure, which is itself a deciding factor for some deployments. But prices and billing increments for cloud services, like storage, change frequently. Multi-cloud deployment lets you diversify the tools you use so your entire cloud deployment doesn’t get locked into a pricing structure you can’t afford – or just don’t want.
Back to topThe Challenges of Multi-Cloud Deployment
Of course, deploying across cloud platforms doesn’t come without a measure of difficulty. Some of the common problems organizations face when moving to multi-cloud deployment include:
- Teams Lacking Expertise: Each cloud (and each service those clouds provide) requires specialized skills to manage.
- Rewriting for Each Cloud: Just like cloud management is specialized, every cloud has its own APIs and methods of integration.
- Increased Complexity: Managing multiple different environments and dealing with multiple vendors can get pretty complicated.
- Comparing and Managing Costs: Organizations using multiple clouds aren’t looking at one price tag per option. They’re often balancing the cost of individual services (like ingress/egress cost) against the overall cost to use the tool. That can make budgeting and affordability a concern when deploying across clouds.
- Security and Risk Management: Ensuring security across different cloud environments and during transits between clouds is paramount for any organization.
As you might’ve gathered, the main pain point for multiple-cloud deployment is that there’s not much standardization between cloud services. Each cloud does things a bit differently. There may be some similar constructs between certain types of PaaS cloud offerings (like SQL-based databases), but even then, there are dozens of specialized database services that may be better for different use cases.
That lack of standardization really becomes apparent when setting up and maintaining individual clouds. Infrastructure as code (IaC) tools like Terraform can be used to help reduce the complexity of setup, but this speed bump can still prevent organizations from efficiently using multiple cloud providers at once.
Back to topMulti-Cloud Automation Definition, Use Cases + Benefits
Multi-cloud automation is the process of automating deployment, configuration, and management of resources like IaaS, PaaS, and SaaS across multiple cloud platforms.
Almost anything can be automated in a cloud environment. That includes tasks that are fundamental to building and maintaining infrastructure. It also means you can automate application lifecycle management (ALM), from server/service provisioning to security to management to scaling to failure correction to destruction.
Automation in a cloud environment is focused on the timeless issues of Reliability, Availability, and Scalability (RAS). With automation, it’s easier to make sure environments are consistent, keep them in their proper state, and maintain self-healing systems.
☁️ Related webinar: Rein in Cloud Chaos with Puppet >>
There are many use cases for automation in a multi-cloud environment. For example, automation makes it easier to develop microservice architectures like Kubernetes that reduce single points of failure. It also allows you to scale services up and down when needed by rapidly deploying new systems and tearing down unnecessary ones.
How Automation Supports a Multi-Cloud Strategy
Making the decision to deploy across multiple clouds (or to make changes to your current multi-cloud deployment) can have a huge impact on your development and operations. That’s where automation shines: It can actually make it easier to deal with complex multi-cloud environments.
Deploying in multiple environments almost always creates a skill gap. Remember how different each cloud provider can be? When members of a team become subject matter experts in one platform, the techniques and nuances they learn might not transfer to another cloud deployment. Automation can help your team bridge the skills gap by allowing different subject matter experts to develop reusable capabilities for others to leverage.
😨 Did You Know: Gartner estimates that fewer than half of I&O organizations have the skills to meet their cloud adoption goals >>
Automation can also minimize the differences between environments. By providing a consistent abstraction layer that bridges the individual cloud providers, it creates a more even playing field among clouds for your team to utilize. That means users don’t all have to be familiar with the differences between environments to utilize them properly. For example, you could write a set of Terraform configurations that allows users to deploy the same set of services on either AWS or GCP simply by passing a single configuration option.
But multi-cloud automation does more than fix and prevent cloud deployment problems. In addressing the friction points above, it gives architects and engineers new ways to develop environments. That leads to better, more resilient infrastructure across platforms.
Back to topGetting Started with Multi-Cloud Automation
Ultimately, any automation and configuration management tool you use for multi-cloud deployment shouldn’t care where you deploy. Puppet excels at not caring whether your operating systems are physical, virtual, on-premises, in the cloud, or in several clouds at once.
Puppet is one of the pioneers of using abstractions to manage configurations. Reusable components in Puppet are written by experts in individual technologies to be leveraged by users without specialized cloud skills. In other words, Puppet users get expert resources without having to become experts themselves.
Try out Puppet Enterprise for yourself – a free, full-featured instance on up to 10 nodes – and start automating anywhere.
