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.
Related articles
No capacity = no defense: rethinking DDoS resilience at scale
DDoS attacks are growing so massive they are overwhelming the very infrastructure designed to stop them. Earlier this year, a peak attack exceeding 7 Tbps was recorded, while 1–2 Tbps attacks have become everyday occurrences. Such volumes were unimaginable just a few years ago.Yet many businesses still depend on mitigation systems that were not designed to scale alongside this rapid attack growth. While these systems may have smart detection, that advantage is moot if physical infrastructure cannot handle the load. Today, raw capacity is non-negotiable — intelligent filtering alone isn’t enough; you need vast, globally distributed throughput.Lukasz Karwacki, Gcore’s Security Solution Architect specializing in DDoS, explains why modern DDoS protection requires immense capacity, global distribution, and resilient routing. Scroll down to watch him describe why a globally distributed defense model is now the minimum standard for mitigating devastating DDoS attacks.DDoS is a capacity war, not just a traffic spikeThe central challenge in DDoS mitigation today is the total attack volume versus total available throughput. Attacks do not originate from a single location. Global botnets harness compromised devices across Asia, Africa, Europe, and the Americas. When all this traffic converges on a single data center, it creates a structural mismatch: a single site’s limited capacity pitted against the full bandwidth of the internet.Anycast is non-negotiable for global capacityTo counter today’s attack volumes, mitigation capacity must be distributed globally, and that’s where Anycast routing plays a critical role.Anycast routes incoming traffic to the nearest available scrubbing center. If one region is overwhelmed or offline, traffic is automatically redirected elsewhere. This eliminates single points of failure and enables the absorption of massive attacks without compromising service availability. By contrast, static mitigation pipelines create bottlenecks: all traffic funnels through a single point, making it easy for attackers to overwhelm that location. Centralized mitigation means centralized failure. The more distributed your infrastructure, the harder it is to take down — that’s resilient network design.Why always-on cloud defense outperforms on-demand protectionSome DDoS defenses activate only when an attack is detected. These on-demand models may save costs but introduce a brief delay while traffic is rerouted and protections come online.Even a few seconds of delay can allow a high-speed attack to inflict damage. Gcore’s cloud-native DDoS protection is always-on, continuously monitoring, filtering, and balancing traffic across all scrubbing centers. This means no activation lag and no dependency on manual triggers.Capacity is the new baseline for protectionModern DDoS attacks focus less on sophistication and more on sheer scale. Attackers simply overwhelm infrastructure by flooding it with more traffic than it can handle.True DDoS protection begins with capacity planning — not just signatures or rulesets. You need sufficient bandwidth, processing power, and geographic distribution to absorb attacks before they reach your core systems.At Gcore, we’ve built a globally distributed DDoS mitigation network with over 200 Tbps capacity, 40+ protected data centers, and thousands of peering partners. Using Anycast routing and always-on defense, our infrastructure withstands attacks that other systems simply can’t.Many customers turn to Gcore for DDoS protection after other providers fail to keep up with attack capacity.Find out why Fawkes Games turned to Gcore for DDoS protection
Deploy GPT-OSS-120B privately on Gcore
OpenAI’s release of GPT-OSS-120B is a turning point for LLM developers. It’s a 120B parameter model trained from scratch, licensed for commercial use, and available with open weights. This is a serious asset for serious builders.Gcore now supports private GPT-OSS-120B deployments via our Everywhere Inference platform. That means you can stand up your own endpoint in minutes, run inference at scale, and control the full stack, without API limits, vendor lock-in, or hidden usage fees. Just fast, secure, controlled deployment on your terms. Deploy now in three clicks or read on to learn more.Why GPT-OSS-120B is big news for buildersThis model changes the game for anyone developing AI apps, platforms, or infrastructure. It brings GPT-3-level reasoning to the open-source ecosystem and frees developers from closed APIs.With GPT-OSS-120B, you get:Full access to model weights and architectureSelf-hosting for maximum data control and privacySupport for fine-tuning and model editingOffline deployment for secure or air-gapped useMassive cost savings at scaleYou can deploy in any Gcore region (or leverage Gcore’s three-click serverless inference on your own infrastructure), route traffic through your own stack, and fully control load, latency, and logs. This is LLM deployment for real-world apps, not just playground prompts.How to deploy GPT-OSS-120B with Gcore Everywhere InferenceGcore Everywhere Inference gives you a clean path from open model to production endpoint. You can spin up a dedicated deployment in just three clicks. We offer configuration options to suit your business needs:Choose your location (cloud or on-prem)Integrate via standard APIs (OpenAI-compatible)Control usage, autoscale, and costsDeploying GPT-OSS-120B on Gcore takes just three clicks in the Gcore Customer Portal.There are no shared endpoints. You get dedicated compute, low-latency routing, and full control and observability.You can also bring your own trained variant if you’ve fine-tuned GPT-OSS-120B elsewhere. We’ll help you host it reliably, close to your users.Use cases: where GPT-OSS-120B fits bestCommercial GPTs still outperform OSS models on some general tasks, but GPT-OSS-120B gives you control, portability, and flexibility where it counts. Most importantly, it gives you the ability to build privacy-sensitive applications.Great fits include:Internal dev tools and copilotsRetrieval-augmented generation (RAG) pipelinesSecure, private enterprise assistantsData-sensitive, on-prem AI workloadsModels requiring full customization or fine-tuningIt’s especially relevant for finance, healthcare, government, and legal teams operating under strict compliance rules.Deploy GPT-OSS-120B todayWant to learn more about GPT-OSS-120B and why Gcore is an ideal provider for deployment? Get all the information you need on our dedicated page.And if you’re ready to deploy in just three clicks, head on over to the Gcore Customer Portal. GPT-OSS-120B is waiting for you in the Application Catalog.Learn more about deploying GPT-OSS-120B on Gcore
Announcing new tools, apps, and regions for your real-world AI use cases
Three updates, one shared goal: helping builders move faster with AI. Our latest releases for Gcore Edge AI bring real-world AI deployments within reach, whether you’re a developer integrating genAI into a workflow, an MLOps team scaling inference workloads, or a business that simply needs access to performant GPUs in the UK.MCP: make AI do moreGcore’s MCP server implementation is now live on GitHub. The Model Context Protocol (MCP) is an open standard, originally developed by Anthropic, that turns AI models into agents that can carry out real-world tasks. It allows you to plug genAI models into everyday tools like Slack, email, Jira, and databases, so your genAI can read, write, and reason directly across systems. Think of it as a way to turn “give me a summary” into “send that summary to the right person and log the action.”“AI needs to be useful, not just impressive. MCP is a critical step toward building AI systems that drive desirable business outcomes, like automating workflows, integrating with enterprise tools, and operating reliably at scale. At Gcore, we’re focused on delivering that kind of production-grade AI through developer-friendly services and top-of-the-range infrastructure that make real-world deployment fast and easy.” — Seva Vayner, Product Director of Edge Cloud and AI, GcoreTo get started, clone the repo, explore the toolsets, and test your own automations.Gcore Application Catalog: inference without overheadWe’ve upgraded the Gcore Model Catalog into something even more powerful: an Application Catalog for AI inference. You can still deploy the latest open models with three clicks. But now, you can also tune, share, and scale them like real applications.We’ve re-architected our inference solution so you can:Run prefill and decode stages in parallelShare KV cache across pods (it’s not tied to individual GPUs) from August 2025Toggle WebUI and secure API independently from August 2025These changes cut down on GPU memory usage, make deployments more flexible, and reduce time to first token, especially at scale. And because everything is application-based, you’ll soon be able to optimize for specific business goals like cost, latency, or throughput.Here’s who benefits:ML engineers can deploy high-throughput workloads without worrying about memory overheadBackend developers get a secure API, no infra setup neededProduct teams can launch demos instantly with the WebUI toggleInnovation labs can move from prototype to production without reconfiguringPlatform engineers get centralized caching and predictable scalingThe new Application Catalog is available now through the Gcore Customer Portal.Chester data center: NVIDIA H200 capacity in the UKGcore’s newest AI cloud region is now live in Chester, UK. This marks our first UK location in partnership with Northern Data. Chester offers 2000 NVIDIA H200 GPUs with BlueField-3 DPUs for secure, high-throughput compute on Gcore GPU Cloud, serving your training and inference workloads. You can reserve your H200 GPU immediately via the Gcore Customer Portal.This launch solves a growing problem: UK-based companies building with AI often face regional capacity shortages, long wait times, or poor performance when routing inference to overseas data centers. Chester fixes that with immediate availability on performant GPUs.Whether you’re training LLMs or deploying inference for UK and European users, Chester offers local capacity, low latency, and impressive capacity and availability.Next stepsExplore the MCP server and start building agentic workflowsTry the new Application Catalog via the Gcore Customer PortalDeploy your workloads in Chester for high-performance UK-based computeDeploy your AI workload in three clicks today!
Gcore recognized as a Leader in the 2025 GigaOm Radar for AI Infrastructure
We’re proud to share that Gcore has been named a Leader in the 2025 GigaOm Radar for AI Infrastructure—the only European provider to earn a top-tier spot. GigaOm’s rigorous evaluation highlights our leadership in platform capability and innovation, and our expertise in delivering secure, scalable AI infrastructure.Inside the GigaOm Radar: what’s behind the Leader statusThe GigaOm Radar report is a respected industry analysis that evaluates top vendors in critical technology spaces. In this year’s edition, GigaOm assessed 14 of the world’s leading AI infrastructure providers, measuring their strengths across key technical and business metrics. It ranks providers based on factors such as scalability and performance, deployment flexibility, security and compliance, and interoperability.Alongside the ranking, the report offers valuable insights into the evolving AI infrastructure landscape, including the rise of hybrid AI architectures, advances in accelerated computing, and the increasing adoption of edge deployment to bring AI closer to where data is generated. It also offers strategic takeaways for organizations seeking to build scalable, secure, and sovereign AI capabilities.Why was Gcore named a top provider?The specific areas in which Gcore stood out and earned its Leader status are as follows:A comprehensive AI platform offering Everywhere Inference and GPU Cloud solutions that support scalable AI from model development to productionHigh performance powered by state-of-the-art NVIDIA A100, H100, H200 and GB200 GPUs and a global private network ensuring ultra-low latencyAn extensive model catalogue with flexible deployment options across cloud, on-premises, hybrid, and edge environments, enabling tailored global AI solutionsExtensive capacity of cutting-edge GPUs and technical support in Europe, supporting European sovereign AI initiativesChoosing Gcore AI is a strategic move for organizations prioritizing ultra-low latency, high performance, and flexible deployment options across cloud, on-premises, hybrid, and edge environments. Gcore’s global private network ensures low-latency processing for real-time AI applications, which is a key advantage for businesses with a global footprint.GigaOm Radar, 2025Discover more about the AI infrastructure landscapeAt Gcore, we’re dedicated to driving innovation in AI infrastructure. GPU Cloud and Everywhere Inference empower organizations to deploy AI efficiently and securely, on their terms.If you’re planning your AI infrastructure roadmap or rethinking your current one, this report is a must-read. Explore the report to discover how Gcore can support high-performance AI at scale and help you stay ahead in an AI-driven world.Download the full report
Protecting networks at scale with AI security strategies
Network cyberattacks are no longer isolated incidents. They are a constant, relentless assault on network infrastructure, probing for vulnerabilities in routing, session handling, and authentication flows. With AI at their disposal, threat actors can move faster than ever, shifting tactics mid-attack to bypass static defenses.Legacy systems, designed for simpler threats, cannot keep pace. Modern network security demands a new approach, combining real-time visibility, automated response, AI-driven adaptation, and decentralized protection to secure critical infrastructure without sacrificing speed or availability.At Gcore, we believe security must move as fast as your network does. So, in this article, we explore how L3/L4 network security is evolving to meet new network security challenges and how AI strengthens defenses against today’s most advanced threats.Smarter threat detection across complex network layersModern threats blend into legitimate traffic, using encrypted command-and-control, slow drip API abuse, and DNS tunneling to evade detection. Attackers increasingly embed credential stuffing into regular login activity. Without deep flow analysis, these attempts bypass simple rate limits and avoid triggering alerts until major breaches occur.Effective network defense today means inspection at Layer 3 and Layer 4, looking at:Traffic flow metadata (NetFlow, sFlow)SSL/TLS handshake anomaliesDNS request irregularitiesUnexpected session persistence behaviorsGcore Edge Security applies real-time traffic inspection across multiple layers, correlating flows and behaviors across routers, load balancers, proxies, and cloud edges. Even slight anomalies in NetFlow exports or unexpected east-west traffic inside a VPC can trigger early threat alerts.By combining packet metadata analysis, flow telemetry, and historical modeling, Gcore helps organizations detect stealth attacks long before traditional security controls react.Automated response to contain threats at network speedDetection is only half the battle. Once an anomaly is identified, defenders must act within seconds to prevent damage.Real-world example: DNS amplification attackIf a volumetric DNS amplification attack begins saturating a branch office's upstream link, automated systems can:Apply ACL-based rate limits at the nearest edge routerFilter malicious traffic upstream before WAN degradationAlert teams for manual inspection if thresholds escalateSimilarly, if lateral movement is detected inside a cloud deployment, dynamic firewall policies can isolate affected subnets before attackers pivot deeper.Gcore’s network automation frameworks integrate real-time AI decision-making with response workflows, enabling selective throttling, forced reauthentication, or local isolation—without disrupting legitimate users. Automation means threats are contained quickly, minimizing impact without crippling operations.Hardening DDoS mitigation against evolving attack patternsDDoS attacks have moved beyond basic volumetric floods. Today, attackers combine multiple tactics in coordinated strikes. Common attack vectors in modern DDoS include the following:UDP floods targeting bandwidth exhaustionSSL handshake floods overwhelming load balancersHTTP floods simulating legitimate browser sessionsAdaptive multi-vector shifts changing methods mid-attackReal-world case study: ISP under hybrid DDoS attackIn recent years, ISPs and large enterprises have faced hybrid DDoS attacks blending hundreds of gigabits per second of L3/4 UDP flood traffic with targeted SSL handshake floods. Attackers shift vectors dynamically to bypass static defenses and overwhelm infrastructure at multiple layers simultaneously. Static defenses fail in such cases because attackers change vectors every few minutes.Building resilient networks through self-healing capabilitiesEven the best defenses can be breached. When that happens, resilient networks must recover automatically to maintain uptime.If BGP route flapping is detected on a peering session, self-healing networks can:Suppress unstable prefixesReroute traffic through backup transit providersPrevent packet loss and service degradation without manual interventionSimilarly, if a VPN concentrator faces resource exhaustion from targeted attack traffic, automated scaling can:Spin up additional concentratorsRedistribute tunnel sessions dynamicallyMaintain stable access for remote usersGcore’s infrastructure supports self-healing capabilities by combining telemetry analysis, automated failover, and rapid resource scaling across core and edge networks. This resilience prevents localized incidents from escalating into major outages.Securing the edge against decentralized threatsThe network perimeter is now everywhere. Branches, mobile endpoints, IoT devices, and multi-cloud services all represent potential entry points for attackers.Real-world example: IoT malware infection at the branchMalware-infected IoT devices at a branch office can initiate outbound C2 traffic during low-traffic periods. Without local inspection, this activity can go undetected until aggregated telemetry reaches the central SOC, often too late.Modern edge security platforms deploy the following:Real-time traffic inspection at branch and edge routersBehavioral anomaly detection at local points of presenceAutomated enforcement policies blocking malicious flows immediatelyGcore’s edge nodes analyze flows and detect anomalies in near real time, enabling local containment before threats can propagate deeper into cloud or core systems. Decentralized defense shortens attacker dwell time, minimizes potential damage, and offloads pressure from centralized systems.How Gcore is preparing networks for the next generation of threatsThe threat landscape will only grow more complex. Attackers are investing in automation, AI, and adaptive tactics to stay one step ahead. Defending modern networks demands:Full-stack visibility from core to edgeAdaptive defense that adjusts faster than attackersAutomated recovery from disruption or compromiseDecentralized detection and containment at every entry pointGcore Edge Security delivers these capabilities, combining AI-enhanced traffic analysis, real-time mitigation, resilient failover systems, and edge-to-core defense. In a world where minutes of network downtime can cost millions, you can’t afford static defenses. We enable networks to protect critical infrastructure without sacrificing performance, agility, or resilience.Move faster than attackers. Build AI-powered resilience into your network with Gcore.Check out our docs to see how DDoS Protection protects your network
Introducing Gcore for Startups: created for builders, by builders
Building a startup is tough. Every decision about your infrastructure can make or break your speed to market and burn rate. Your time, team, and budget are stretched thin. That’s why you need a partner that helps you scale without compromise.At Gcore, we get it. We’ve been there ourselves, and we’ve helped thousands of engineering teams scale global applications under pressure.That’s why we created the Gcore Startups Program: to give early-stage founders the infrastructure, support, and pricing they actually need to launch and grow.At Gcore, we launched the Startups Program because we’ve been in their shoes. We know what it means to build under pressure, with limited resources, and big ambitions. We wanted to offer early-stage founders more than just short-term credits and fine print; our goal is to give them robust, long-term infrastructure they can rely on.Dmitry Maslennikov, Head of Gcore for StartupsWhat you get when you joinThe program is open to startups across industries, whether you’re building in fintech, AI, gaming, media, or something entirely new.Here’s what founders receive:Startup-friendly pricing on Gcore’s cloud and edge servicesCloud credits to help you get started without riskWhite-labeled dashboards to track usage across your team or customersPersonalized onboarding and migration supportGo-to-market resources to accelerate your launchYou also get direct access to all Gcore products, including Everywhere Inference, GPU Cloud, Managed Kubernetes, Object Storage, CDN, and security services. They’re available globally via our single, intuitive Gcore Customer Portal, and ready for your production workloads.When startups join the program, they get access to powerful cloud and edge infrastructure at startup-friendly pricing, personal migration support, white-labeled dashboards for tracking usage, and go-to-market resources. Everything we provide is tailored to the specific startup’s unique needs and designed to help them scale faster and smarter.Dmitry MaslennikovWhy startups are choosing GcoreWe understand that performance and flexibility are key for startups. From high-throughput AI inference to real-time media delivery, our infrastructure was designed to support demanding, distributed applications at scale.But what sets us apart is how we work with founders. We don’t force startups into rigid plans or abstract SLAs. We build with you 24/7, because we know your hustle isn’t a 9–5.One recent success story: an AI startup that migrated from a major hyperscaler told us they cut their inference costs by over 40%…and got actual human support for the first time. What truly sets us apart is our flexibility: we’re not a faceless hyperscaler. We tailor offers, support, and infrastructure to each startup’s stage and needs.Dmitry MaslennikovWe’re excited to support startups working on AI, machine learning, video, gaming, and real-time apps. Gcore for Startups is delivering serious value to founders in industries where performance, cost efficiency, and responsiveness make or break product experience.Ready to scale smarter?Apply today and get hands-on support from engineers who’ve been in your shoes. If you’re an early-stage startup with a working product and funding (pre-seed to Series A), we’ll review your application quickly and tailor infrastructure that matches your stage, stack, and goals.To get started, head on over to our Gcore for Startups page and book a demo.Discover Gcore for Startups
Subscribe to our newsletter
Get the latest industry trends, exclusive insights, and Gcore updates delivered straight to your inbox.