In 2024, businesses of all sizes are adopting cloud computing and hyperscalers like AWS, Azure and GCP are growing at an insane pace. From an engineering perspective, the cloud is an amazing piece of technology. However, that's not the reason why so many companies are moving their applications into the cloud. The reason is that it helps businesses reach their goals. But how? This is what I'm trying to summarize in this article.
Here are my top 10 reasons why you should consider cloud-hosting your business application.
1. Managed Services relieve you of Maintenance Work 🥱
When you use managed services, you no longer need to worry about low-level infrastructure management. Deploy a container instead of a VM, then you don't need to update your operating system to ensure security. When you use a managed SQL database, you don't need to manage the host machine. You are tired of operating your own Kubernetes cluster? Then use a managed platform, it will save you a lot of exhausting brainwork.
2. Increadibly Easy Scalability 📈
Cloud infrastructure can scale seamlessly to meet your growing needs without requiring additional hardware or major changes. A few clicks or configurations allow you to scale up and down at any time. This ensures your applications are future-proof and can handle increasing loads effortlessly.
3. Empower Teams for End-to-End Ownership 💪
"You build it, you run it" is the motto of platform engineering. Great (cloud) platforms empower their teams to own not only the application code, but also the infrastructure that lives around it. It can reduce friction to align your software with your organization structure, see Conway's Law. This brings you one step further towards a self-sufficient, cross-functional team, that isn't dependent on other business units.
4. Infrastructure Automation and Auto-Healing ❤️🩹
When the infrastructure layer can be controlled via APIs, you can do all sorts of magic with automations, such as auto-healing your deployment whenever the container is unresponsive. Many of these techniques are even built into the cloud offering, so you don't need to create the automation from scratch.
5. Elasticity and Pay-As-You-Go Model 💵
The resources your application demands probably varies from hour to hour, from day to day. Some applications are only used during business hours. Or sometimes a marketing campaign causes so much traffic that your server crashes (We've seen it all). With the elasticity that hyperscalers provide, you can overcome this issue by scaling automatically, depending on the current demand. What's more, you only need to pay for additional resources when you really needed them.
6. Serverless Computing for Infrequent, High-Power Tasks ⚙️
Related to the previous point, many businesses have the need to periodically run resource-intensive tasks. While banks operate massive mainframes to do that, you could also just run some serverless functions. You pay for it while it runs. While your code doesn't run, you scale to zero and spend no money at all. Running your function on insane 256 GB of RAM and 32 cores costs as little as 5€ per hour.
7. Governance of Large IT Landscapes 🔍
Cloud platforms support central oversight and governance of large IT landscapes. Through policies, your platform engineers can ensure that every application in your organization complies with security, privacy, data locality, backup and logging requirements.
8. Ready-to-Use Cloud Services 🌱
Instead of building everything from scratch, you can leverage existing cloud services for data analytics, Internet of Things (IoT) applications, and many more. Take a look at the components your cloud platform provides, to see if you can make use of them to save time and reduce development costs.
9. High Availability and Disaster Recovery 💥
Cloud providers offer multi-region deployments and allow you to seamlessly shift traffic between regions and data centers. It ensures your applications remain available even in the unlikely event that your data center burns down or a major regional outage.
10. Machine Learning and AI Tools Tailored to Your Data 🦾
Last but not least, who doesn't want an AI tools that is tailored to their very own data. Only when your personal and your company's context is incorporated into an LLM does it really show its strengths. Data analytics and machine learning can add valuable insights to the data you have already stored. Unless you have your own in-house data scientists and ML experts, all of this only makes sense in the cloud, as it provides you with the tools you'll need to create your own AI tools.
To sum it up, by leveraging cloud technologies, companies can innovate faster, operate more efficiently, and stay competitive in an increasingly digital world.