Cloud development allows you to write, debug, and run code directly in the cloud infrastructure, rather than working in the local environment and subsequently uploading to the cloud. This streamlines the development process, allowing you to deploy and update applications faster. This article explains what cloud development is, what tools can help you with it, and how you can use cloud development to develop and update your application to meet your customers’ needs.
What Is Cloud Development?
Cloud development is the practice of creating and managing applications that run on remote servers, allowing users to access them over the internet.
Every application is made up of different types of services, such as backend services, frontend services, and monitoring services. Normally, without cloud development, creating a new service or updating an existing service means writing and running your code in the local environment first. After ensuring your service works as expected, you then push your code to the cloud environment, and run it there. Finally, you publish your service and integrate it with the app. This process is time-consuming, and requires sufficiently powerful computing resources to run the service on the local machine.
This is when cloud development proves its value. With cloud development, you write your code directly in the cloud infrastructure. After you have finished writing your code, publishing your service takes just one click.
Useful Cloud Development Tools
To apply cloud development to your projects, several tools are required to build and manage the code efficiently:
- Code editor to help you write code more efficiently
- Version control tool to manage the code changes
- Compiler/interpreter to run your code
- Publisher to allow public use of your application
Let’s learn about each of the tools in more detail.
Code Editor
A code editor is a software tool that supports code highlighting, easy navigation within the codebase, test execution, and debugging capabilities. When you’re working on applications that are hosted in a cloud environment, it’s essential for your code editor to support remote access because the code you’re working on is stored on a remote cloud server.
Remote access support enables you to establish an SSH connection between your local code editor and the remote server. You can then use your code editor to create, view, and modify code files as if they were local.
Popular code editors—like Visual Studio Code and JetBrains IDE—have features that support remote development. For example, with Visual Studio Code, you can install Microsoft’s “Remote – SSH” extension to enable remote code access via SSH. Once the extension is installed, you can connect to the remote server by entering its IP address, username, and password, and work on your cloud-based projects just as easily as you would local ones.
Below is an example of using Visual Studio Code to access code via a remote machine.
Version Control
In software development, it’s common for many people to be working on the same code base. Having a version control tool allows you to review who made the changes, and when, to certain lines of code so that you can trace any problems back to their source. Having a version control tool also allows you to revert your code to the version you want in the instance that new code introduces bugs.
There are several version control tools out there, such as Git, SVN, and Mercurial; Git is currently the most popular. Git is an open-source version control system that allows you to manage the changes in the code. It is distributed software, meaning that you create a local copy of the Git repository on your local machine, and then create new branches, add files, commit, and merge locally. When your code is ready to ship, you then push your code to the Git repository on the server.
Compiler/Interpreter
After tools that help you to write and track changes in the code, the next most important tool required to run code in the cloud is a compiler or interpreter. You need either a compiler or an interpreter to translate your code into machine code, depending on the programming languages or the runtime you are working on. They allow the computer to understand and execute your instructions. Generally speaking, the compiler or interpreter helps to build your code into executable files. Let’s look at each in turn to understand their differences.
Compiler
Before a service actually runs, a compiler translates the high-level code that you have written into low-level code. For example, a Java compiler compiles source code to the bytecode first, then the Java Virtual Machine will interpret and convert the bytecode to a machine-code executable file. As a result, compilers require time to analyze the source code. However, they also show obvious syntax errors, so you don’t need to spend a lot of time debugging your service when it’s running.
The programming languages that use compilers to translate code are Java, C#, and Go.
Interpreter
Unlike a compiler, an interpreter only translates written code into machine code when the service runs. As a result, the interpreter does not take time to compile the code. Instead, the code is executed right away. However, an application using an interpreter is often slower than one using a compiler because the interpreter executes the code line by line.
The programming languages that use interpreters are Python, Javascript, and Ruby.
Publisher
To allow other users to access your service, you need a publisher tool. This manages the following key aspects of your service:
- Configuring the network
- Creating the domain name
- Managing the scalability
Network Configuration
To allow users to access your service, the network configuration is crucial. The method for making your service available online varies based on your technology stack. For instance, if you use the Next.js framework to build your web application, you can choose Vercel to deploy your application code.
You can also customize the behavior of your application with network configuration. Here’s an example of how to use the vercel.json file to redirect requests from one path to another:
{ "redirects": [ { "source": "/book", "destination": "/api/book", "statusCode": 301 } ]}Domain Setting
Every service requires a URL for applications to interact with it. However, using direct IP addresses as URLs can be complex and unwieldy, so it’s advisable to assign a domain name to your service, like www.servicedomain.com. Various platforms, such as GoDaddy or SquareSpace, offer domain registration services for this purpose.
Scalability
To allow your service to handle more requests from your users, you need to define a scalability mechanism for your services. This way, your service will automatically scale according to the workload. Scalability also keeps costs in check; you pay for what you use, rather than wasting money by allocating resources based on peak usage.
Below is an example definition file for applying autoscaling to your service, using Kubernetes HorizontalPodAutoscaler.
apiVersion: autoscaling/v1 kind: HorizontalPodAutoscaler metadata: name: app spec: scaleTargetRef: apiVersion: apps/v1 kind: Deployment name: appdeploy minReplicas: 1 maxReplicas: 10 targetCPUUtilizationPercentage: 70How to Run Code In the Cloud
Now that you are familiar with the tools you need for cloud development, let’s learn about how to run code in the cloud. There are two ways to run code in the cloud: using virtual machines or using containers. We explain the difference in depth in our dedicated article, but let’s review their relevance to cloud development here.
Virtual Machines
A virtual machine (VM) is like a computer that runs within a computer. It mimics a standalone system, with its own virtual hardware and software. Since a VM is separate from its host computer, you can pick the VM operating system that suits your needs without affecting your host’s system. Plus, its isolation offers an extra layer of security: if one VM is compromised, the others remain unaffected.
While VMs offer versatility in terms of OS choices for cloud development, scaling applications on VMs tends to be more challenging and costly compared to using containers. This is because each VM runs a full operating system, leading to higher resource consumption and longer boot-up times. Containers, on the other hand, share the host OS and isolate only the application environment, making them more lightweight and quicker to scale up or down.
Containers
A container is a software unit that contains a set of software packages and other dependencies. Since it uses the host operating system’s kernel and hardware, it doesn’t possess its own dedicated resources as a virtual machine does. As a result, it’s more lightweight and takes less time to start up. For instance, an e-commerce application can have thousands of containers for its backend and frontend services. This allows the application to easily scale out when needed by increasing the number of containers for its services.
Using containers for cloud code enables efficient resource optimization and ease of scaling due to their lightweight nature. However, you have limited flexibility in choosing the operating system, as most containers are Linux-based.
Cloud Development Guide
We’ve addressed cloud development tools and ways to run code in the cloud. In this section, we offer a step-by-step guide to using cloud development for your project.
Check Computing Resources
Before anything else, you’ll need the correct computing resources to power your service. This includes deciding between virtual machines and containers. If your service tends to have a fixed number of user requests everyday, or it needs a specific operating system like Mac or Windows in order to run, go with virtual machines. If you expect your service to experience a wide range in the number of user requests and you want it to be scalable to optimize operational costs, go with containers.
After choosing between virtual machines and containers, you need to allocate computing resources for use. The important resources that you need to consider are CPUs, RAM, disk volumes, and GPUs. The specifications for these resources can vary significantly depending on the service you’re developing. For instance, if you’re building a monitoring service with a one-year data retention plan, you’ll need to allocate disk volumes of approximately 100GB to store all generated logs and metrics. If you’re building a service to apply deep learning models to large datasets, you’ll require not only a powerful CPU and ample RAM, but also a GPU.
Install Software Packages and Dependencies
After preparing the computing resources, you’ll next install the necessary software and dependencies. The installation process varies depending on whether you’re using virtual machines or containers.
As a best practice, you should set up the mechanism to install the required dependencies automatically upon initialization of the virtual machine or container. This ensures that your service has all the necessary dependencies to operate immediately upon deployment. Additionally, it facilitates easy redeployment to a different virtual machine or container, if necessary. For example, if you want to install software packages and dependencies for an Ubuntu virtual machine to host a Node.js service, you can configure cloud-init scripts for the deployed virtual machine as below:
#cloud-config...apt: sources: docker.list: source: deb [signed-by=$KEY_FILE] https://deb.nodesource.com/node_18.x $RELEASE main keyid: 9FD3B784BC1C6FC31A8A0A1C1655A0AB68576280package_update: truepackage_upgrade: truepackages: - apt-transport-https - ca-certificates - gnupg-agent - software-properties-common - gnupg - nodejspower_state: mode: reboot timeout: 30 condition: TrueTo set up a web service on containers using Node.js, you’ll need to install Node along with the required dependencies. Below is a Dockerfile example for doing so:
# Pull the Node.js image version 18 as a base imageFROM node:18# Set the service directoryWORKDIR /usr/src/appCOPY package*.json ./# Install service dependenciesRUN npm installWrite Code
When you’ve installed the necessary software packages and dependencies, you can begin the fun part: writing the code. You can use code editors that support remote access to write the code for your service directly in the cloud. Built-in debugging tools in these editors can help you to identify any issues during this period.
Below is an example of using IntelliJ to debug a Go service for managing the playlists.
Test Your Service
After you finish writing your code, it’s crucial to test your service. As a best practice, start with unit tests to ensure that individual components work, followed by integration tests to see how your service interacts with existing application services, and finally E2E (end-to-end) tests to assess the overall user experience and system behavior.
Below is a test pyramid that gives a structured overview of each test type’s coverage. This will help you allocate your testing efforts efficiently across unit and integration tests for your service.
Configure Network Settings
To make your service available to users, you need to configure its network settings. This might involve configuring the rules for inbound and outbound data, creating a domain name for your service, or setting a static IP address for your service.
Here is an example of using cloud-init configuration to set a static IP for a virtual machine that hosts your service:
#cloud-config...write_files: - content: | network: version: 2 renderer: networkd ethernets: enp3s0: addresses: - 192.170.1.25/24 - 2020:1::1/64 nameservers: addresses: - 8.8.8.8 - 8.8.4.4 path: /etc/netplan/00-add-static-ip.yaml permissions: 0644power_state: mode: reboot timeout: 30 condition: TrueAdd Autoscaling Mechanism
With everything in place, it’s time to add an autoscaling mechanism. This adjusts resources based on demand, which will save costs during quiet times and boost performance during busy periods.
Assuming that you use Kubernetes to manage the containers of your service, the following is an example of using Gcore Managed Kubernetes to set the autoscaling mechanism for your Kubernetes cluster:
Set Up Security Enhancement
Finally, ensure your service is secure. Enhancements can range from setting up robust authentication measures to using tools like API gateways to safeguard your service. You can even set up a mechanism to protect your service from malicious activities such as DDoS attacks.
Below is an example of how to apply security protection to your service by creating a resource for your service URL using Gcore Web Security:
Gcore Cloud Development
Accelerating feature delivery through cloud development can offer a competitive edge. However, the initial setup of tools and environments can be daunting—and mistakes in this phase can undermine the benefits.
Here at Gcore, we recognize these obstacles and offer Gcore Function as a Service (FaaS) as a solution. Gcore FaaS eliminates the complexities of setup, allowing you to dive straight into coding without worrying about configuring code editors, compilers, debuggers, or deployment tools. Ideally suited for straightforward services that require seamless integration with existing applications, Gcore FaaS excels in the following use cases:
- Real-time stream processing
- Third-party service integration
- Monitoring and analytics services
Conclusion
Cloud development allows you to deliver your service to users immediately after you’ve finished the coding and testing phases. You can resolve production issues and implement features faster to better satisfy your customers. However, setting up cloud infrastructure can be time intensive and ideally needs a team of experienced system administrators for building and maintenance.
With Gcore FaaS, you don’t have to take on that challenge yourself. You can focus on writing code, and we’ll handle the rest—from configuring pods and networking to implementing autoscaling. Plus, you are billed only for time your customers actually use your app, ensuring cost effective operation.
Want to try out Gcore FaaS to see how it works? Get started for free.
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The cloud never stops. Neither do the threats.Every shift toward containers, microservices, and hybrid clouds creates new opportunities for innovation…and for attackers. Legacy security, built for static systems, crumbles under the speed, scale, and complexity of modern cloud-native environments.To survive, organizations need a new approach: one that’s dynamic, AI-driven, automated, and rooted in zero trust.In this article, we break down the hidden risks of cloud-native architectures and show how intelligent, automated security can outpace threats, protect distributed workloads, and power secure growth at scale.The challenges of cloud-native environmentsCloud-native architectures are designed for maximum flexibility and speed. Applications run in containers that can scale in seconds. Microservices split large applications into smaller, independent parts. 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GTC Europe 2025: watch Seva Vayner on European AI trends
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Gcore and Orange Business have kicked off a strategic co-innovation program with the mission to deliver a scalable, production-grade AI inference service that is sovereign by design. By combining Orange Business’ secure, trusted cloud infrastructure and Gcore’s AI inference private deployment service, the collaboration empowers European enterprises and public sector organizations to run inference workloads at scale, without compromising on latency, control, or compliance.Gcore’s AI inference private deployment service is already live on Orange Business’ Cloud Avenue infrastructure. Selected enterprises across industries are actively testing it in real-world scenarios. These pilot customers are exploring how fast, secure, and compliant inference can accelerate their AI projects, cut deployment times, and reduce infrastructure overhead.The prototype will be demonstrated at NVIDIA GTC Paris, at the Taiga Cloud booth G26. Stop by any time to see it in action.The inference supercycle is underwayBy 2030, inference will comprise 70% of enterprise AI workloads. Telcos are well positioned to lead this shift due to their dense edge presence, licensed national data infrastructure, and long-standing trust relationships.Gcore’s inference solution provides a sovereign, edge-native inference layer. It enables users to serve real-time, GPU-intensive applications like agentic AI, trusted LLMs, computer vision, and predictive analytics, all while staying compliant with Europe’s evolving data and AI governance frameworks.From complexity to three clicksEnterprise AI doesn’t need to be hard. Deploying inference workloads at scale used to demand Kubernetes fluency, large MLOps teams, and costly trial-and-error.Now? It’s just three clicks:Pick a model: Choose from NVIDIA NIMs, open source, or proprietary libraries.Choose a region: Select one of Orange Business’ accredited EU data centers.Deploy: See your workloads go live in under 10 seconds.Enterprises can launch inference projects faster, test ideas more quickly, and deliver production-ready AI services without spending months on ML plumbing.Explore our blog to watch a demo showing how enterprises can deploy inference workloads in just three clicks and ten seconds.Sovereign by designAll model data, logs, and inference results are stored exclusively within Orange Business’ own data centers in France, Germany, Norway, and Sweden. Cross-border data transfer is opt-in only, helping ensure alignment with GDPR, sector-specific regulations, and the forthcoming EU AI Act.This platform is built for trust, transparency, and sovereignty by default. Customers maintain full control over their data, with governance baked into every layer of the deployment.Performance without trade-offsGcore’s AI inference solution avoids the latency spikes, cold starts, and resource waste common in traditional cloud AI setups. Key design features include:Smart GPU routing: Directs each request to the nearest in-region GPU, delivering real-time performance with sub-50ms latency.Pre-loaded models: Reduces cold start delays and improves response times.Secure multi-tenancy: Isolates customer data while maximizing infrastructure efficiency.The result is a production-ready inference platform optimized for both performance and compliance.Powering the future of AI infrastructureThis partnership marks a step forward for Europe’s sovereign AI capabilities. It highlights how telcos can serve as the backbone of next-generation AI infrastructure, hosting, scaling, and securing workloads at the edge.With hundreds of edge POPs, trusted national networks, and deep ties across vertical industries, Orange Business is uniquely positioned to support a broad range of use cases, including real-time customer service AI, fraud detection, healthcare diagnostics, logistics automation, and public sector digital services.What’s next: validating real-world performanceThis phase of the Gcore and Orange Business program is focused on validating the solution through live customer deployments and performance benchmarks. Orange Business will gather feedback from early access customers to shape its future sovereign inference service offering. These insights will drive refinements and shape the roadmap ahead of a full commercial launch planned for later this year.Gcore and Orange Business are committed to delivering a sovereign inference service that meets Europe’s highest standards for speed, simplicity, and trust. This co-innovation program lays the foundation for that future.Ready to discover how Gcore and Orange Business can deliver sovereign inference as a service for your business?Request a preview
Why on-premises AI is making a comeback
In recent years, cloud AI infrastructure has soared in popularity. With its scalability and ease of deployment, it’s no surprise that organizations rushed to transfer their data to the cloud in a bid to become “cloud-first.”But now, the tide is turning.As AI workloads grow more complex and regulatory pressures increase, many companies are reconsidering their reliance on cloud and turning back toward on-premises AI infrastructure.Rather than doubling down on the cloud, organizations are diversifying—adopting multi-cloud models, sovereign cloud environments, and even hybrid or fully on-prem setups. The era of a single cloud provider handling everything is coming to an end. Why? Control, security, and performance are hard to find in the public cloud.Here’s why more businesses are bringing AI back in-house.#1 Enhanced data security and controlData security remains one of the most urgent concerns driving the return to on-prem infrastructure.For sensitive or high-priority workloads—common in sectors like finance, healthcare, and government—keeping data off the cloud is often non-negotiable. Cloud computing inherently increases risk by exposing data to shared environments, wider attack surfaces, and complex supply chains.Choosing a trusted cloud provider can mitigate some of those risks. But it can’t replace the peace of mind that comes from keeping sensitive data in-house.With on-premises AI, organizations gain fine-grained access control. Encryption keys remain internal and breach exposure shrinks dramatically. It’s also much easier to stay compliant with privacy laws when data never leaves your own secure perimeter.For industries where trust and confidentiality are everything, on-prem solutions offer full visibility into where and how data is stored and processed.#2 Performance enhancement and latency reductionLatency matters—especially in AI.On-premises AI systems excel in environments that require real-time performance and heavy compute loads. Processing data locally avoids the physical delays caused by transferring it across the internet to a cloud data center.By eliminating long-haul network hops, companies get near-instant access to computing resources. They also get to fine-tune their internal networks—using private fiber, low-hop switching, and other low-latency optimizations that cloud customers can’t control.Unlike multi-tenant cloud platforms, on-prem resources aren’t shared. That means consistently low, predictable latency.This is vital for use cases where milliseconds—or even microseconds—make a difference: autonomous vehicles, real-time analytics, robotic control systems, and high-speed trading. Fast feedback loops and localized processing enable better outcomes, tighter control, and faster decision-making at the edge.#3 Regulatory compliance and data sovereigntyAround the world, data privacy regulations are tightening. For most organizations, compliance isn’t optional.On-premises infrastructure helps keep data safely inside the organization’s network. This supports data sovereignty, ensuring that sensitive information remains subject only to local laws—not the policies of another country’s cloud provider.It's also a powerful hedge against geopolitical instability.While hyperscalers operate globally, they’re always headquartered somewhere. That makes their infrastructure vulnerable to political shifts, sanctions, or changes in international data law. Governments may require them to restrict access, share data, or cut off services entirely—especially to organizations in sanctioned or adversarial jurisdictions.Businesses relying on these providers risk disruption when regulations change. On-premises infrastructure, by contrast, offers reliable continuity and greater control—especially in uncertain times.#4 Cost control and operational benefitsCloud pricing may look flexible, but costs can escalate quickly.Data transfers, storage, and compute spikes all add up—fast. In contrast, on-premises infrastructure provides a predictable Total Cost of Ownership (TCO). Although upfront CapEx is higher, OpEx remains more stable over time.Organizations can invest in high-performance hardware tailored to their specific needs and amortize those costs across years. That means no surprise bills, no sudden price hikes, and no dependence on vendor pricing models.Of course, running on-prem infrastructure comes with its own challenges. It demands specialized teams for deployment, maintenance, and support. These experts are costly to recruit and retain—but they’re critical to ensure uptime, security, and performance.Still, for companies with relatively stable compute and storage needs, the long-term savings often outweigh the initial setup effort. On-prem also integrates more smoothly into existing IT workflows, without the need for internet access or additional network setup—another operational bonus.#5 Proactive threat detection and automated responsesOn-premises AI sometimes enables smarter, more customized security.Advanced platforms can continuously analyze live data streams using machine learning to detect anomalies and predict threats. When something suspicious is flagged, the system can respond instantly by quarantining data, blocking traffic, and alerting security teams.That kind of automation is essential for minimizing damage and downtime.With full infrastructure control, organizations can deploy bespoke monitoring systems that align with their threat models. Deep packet inspection, real-time anomaly detection, and behavioral analytics can be easier to configure and maintain on-prem than in shared cloud environments.These systems can also work seamlessly with WAAP and DDoS tools to detect and neutralize threats before they spread. The key is flexibility: whether on-prem or cloud-based, AI-driven security should adapt to your architecture and threat landscape, not the other way around.End-to-end visibility can give security teams a clearer picture and faster response options than generic, one-size-fits-all public cloud security tools.How to combine eon-premises control with cloud scalabilityLet’s be clear: on-premises AI isn’t perfect. It demands upfront investment. It requires skilled personnel to deploy and manage systems. And integrating AI into legacy environments takes thoughtful planning.But today’s tools are helping bridge those gaps. Modern platforms reduce the need for constant manual intervention. They support real-time updates to threat models and detection logic. As a result, security teams can spend more time on strategy and less on maintenance.Meanwhile, the cloud still plays an important role. It offers faster access to new tools, software updates, and next-gen GPU hardware.That’s why many organizations are opting for a hybrid model.Our recommendation: Keep your sensitive, high-priority workloads on-prem. Use the cloud for elastic scale and innovation. Together, they deliver the best of both worlds: performance, control, compliance, and flexibility.Secure your digital infrastructure with Gcore on-premises AI inferenceWhether you’re protecting sensitive data or running high-demand workloads, on-premises AI gives you the control and confidence you need. Securing sensitive data and managing high-demand workloads requires a level of control, performance, and predictability that only on-premises AI infrastructure delivers.Gcore Everywhere Inference Private Deployment makes it easier than ever to bring powerful serverless AI inference capabilities directly into your physical environment. Designed for scalable global performance, Everywhere Inference enables robust and secure multi-tenant AI inference deployments across on-prem and cloud environments, helping you meet data sovereignty requirements, reduce latency, and streamline deployment.Talk to us about your on-prem AI plans
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