In the IT industry, logs provide invaluable insights into system behavior, performance, and security, enabling timely troubleshooting and data-driven decision making. As a result, generating a vast quantity of logs is often considered a valuable goal in itself. However, the indiscriminate logging of every single step of your code can lead to chaos in log storage, failing to deliver the expected benefits of log collection. In this article, we’ll look at best practices for log generation, collection, and analysis to help you get the most from your logs.
Understanding Logs and Log Collection
Logs are short messages that capture significant events within a software system, along with associated metadata. Log collection refers to the generation, aggregation, and storage of the historical data represented by the logs.
Typically, log messages are generated within a software’s source code or by infrastructure components. These messages are either stored locally on disk or sent to a dedicated server; in both locations, the log entries are processed, stored, and analyzed.
The main use cases for log collection include:
- Troubleshooting bugs: Log messages can help to reconstruct the sequence of events leading to a bug and provide useful data that gives context.
- Detecting errors: You may be unaware of a specific bug or failure until an anomaly appears in your log files. Monitoring logs helps detect errors and system malfunctions.
- Investigating security incidents: Unauthorized access attempts, cyberattacks, and other suspicious activity can be revealed by logs, which track unusual events happening in the system.
- Usage analytics: Logs can mark various milestones or steps in users’ interaction with your services and applications, enhancing your understanding of how they use your software.
Server applications often use logs to analyze how their API is used, monitor outages, and measure latency when exchanging data between subsystems, or the system and the user, in order to recognize performance bottlenecks. In mobile apps, it’s common to use logs while investigating crash reports and analyzing A/B testing results.
The entire process of logging can be divided into two categories of activities: log collection and log analysis. The first group encompasses everything that produces log messages, including logs generation and saving them to a file or sending them to a remote storage. The second group relates to activities on the logs consumer side—logs storage, processing, combining, filtering, and, ultimately, their analysis. In some cases, the edge between two groups is blurred, therefore some practices recommended in this article affect the entire process, not just the group they logically relate to.
Log Collection Best Practices
In order to get the most from your logs, it’s important to follow certain best practices. Here are some simple practices to ensure that your logs are useful tools for the efficient maintenance of your app, instead of a pile of unsorted data with no practical value.
#1 Planning
Good results start with a good plan, and logs are no exception. Although logs can be introduced at any stage of the application lifecycle, planning what you want to log before you start writing code optimizes the process. This allows you to integrate logging seamlessly where it’s useful, efficient, and maintainable.
#2 Including All Layers and Subsystems
It’s important to include all system components and code modules in your logging. Otherwise, you may find a bug located in an area that’s only partially logged or not logged at all, and then you’ll either need another way to tackle the issue or you’ll have to add logging retroactively and wait until the problem occurs again. Adding new logs on the go, redeploying the system, and waiting until the elusive bug reoccurs—while your users are expecting a fix—does not inspire trust in your company because it’ll result in subpar user experience.
#3 Structure
Good logs have good structure. Here are four elements that lend structure to logs, and the reasons it’s important to keep these four things in mind while generating log messages:
Categorization
To navigate easily through large amounts of data, log data should be organized systematically. Using different categories for different subsystems enables you to filter logs for the specific part of the application that is relevant to your current analysis.
Log Levels
Virtually all log systems support different groups of messages, which are commonly referred to as log levels. While different log systems may use different names for levels or offer a slightly different number, a number of levels are common across systems.
- Debug logs help software developers with the problem at hand by providing technical and specific information, such as the context necessary to reproduce a bug.
- Info logs are usually bound to certain events related to the software’s business value, such as starting and stopping a service or creating and removing a file. Such logs are useful when gathering statistics and analyzing various usage scenarios.
- Warning logs warn about potentially dangerous events in the system or circumstances that might lead to such events. For instance, they will notify you if a disc has almost run out of space.
- Error logs are exactly what their name suggests: the description and meta-data of the errors that happen in your system. For example, a mobile application can generate an error log when its backend server is not available.
- Fault or fatal-level logs show critical failures of the software that prevent its proper functioning. They usually mean that an intervention from an engineer is required. For instance, a microservice can log a fault when its connected database is down.
Using log levels consistently allows entries to be filtered, limiting the output to the necessary minimum. Together with categorizing, this would make it possible, for example, to display warnings related solely to the database layer.
Tagging
Some log systems allow you to add custom tags to log messages. Similarly to how categories help distinguish log messages produced by different subsystems, tags allow you to group your log messages by custom criteria, for example, “A/B Testing” or “Performance.”
Format
Adhering to a well-known formatting structure, such as JSON or XML, makes processing and storing of the log data more efficient. However, there’s a catch: A fixed format imposes rigid constraints on the log message. Thus, applying a strict schema to each log message might result in partial loss of context, which is less likely with a free-form message.
#4 Consistent Text Formatting
Software developers are sometimes reluctant to write any kind of documentation, including log messages, and generating consistent log messages requires discipline and long-term commitment. However, using established terminology and unified formatting always pays off, because it helps you skim through vast amounts of log messages more easily and reduces the possibility of human error.
Reading logs can be a challenge even when they are properly organized. At the very minimum, using units and date formats consistently is necessary if you don’t want to spend hours reading scattered entries, and editing messages that use different terms for the same concept.
#5 Including Relevant Data and Context
Before adding logging, it’s worth deciding which data will be useful for your use case. Relevant data provides your log messages with context. For example, attaching unique identifiers to user API requests makes it easier to find information about a specific request. Even if a log message is in plain text, consistently including a timestamp will eventually give the message context during log filtering and analysis.
On the other hand, it’s equally beneficial to avoid generating unnecessary log messages. Logging irrelevant data introduces noise, slows down the search for relevant information, and wastes both your and your users’ disk space.
The key to remember when it comes to relevance is that detailed and meaningful messages are the core of your log entries. Even if they are collected, stored, and processed by sophisticated automated systems, ultimately logs are read and interpreted by humans, so they need to convey relevant meaning.
#6 Security and Privacy
When planning which data to incorporate into logs, users’ private information should be excluded or at least encrypted. Some log systems can redact personal details, such as names or credit card numbers. This obfuscates sensitive data but still allows log entries containing a specific encrypted identifier to be collated.
If sending sensitive data to your log server is unavoidable, precautions should be taken. The internet connection must be secure, data should be encrypted, and access to logs should be restricted to a select few individuals whose roles require it.
It’s also advisable to keep your software updated, because new patches often contain fixes to known security breaches. However, this comes with its own pitfalls: New updates sometimes include known issues, so always read release notes and bulletins.
Finally, if your company must comply with certain regulations, such as GDPR for companies operating within the EU, logs require particular attention. Regulations may require that certain data types, including logs, have a finite retention period.
What Is Log Analysis?
Log analysis involves the set of activities related to reading, searching, and interpreting collected logs. While effective log generation and collection are integral parts of the efficient log system, they make up just half of its success. When it comes to actually using the resulting logs, analysis comes to the fore.
IT professionals generally use log analysis in a precisely targeted way, focusing on specific sections of the entire log to answer questions, analyze aspects of performance, or investigate incidents. For instance, the focus might be on logs related to a user session during the time when a particular bug occurred.
Log Analysis Best Practices
Given the challenges of processing large volumes of stored data and network delays in the case of remote log servers, log analysis has its own challenges. In this section, we’ll take a look at best practices that make the process easier.
After logs are generated and collected, they are stored in or sent to a logs storage. This is where IT professionals access generated logs to analyze them.
#1 Accessible Storage
To facilitate efficient analysis, a good storage system for your logs should possess the following qualities:
- Friendly interface: Browsing log messages starts with accessibility; a cryptic API can easily make reading logs torturous. Choose a log system or service that makes your logs easily accessible, preferably one that has an intuitive user interface.
- Security: As log entries can contain sensitive data or business-critical information, access should be restricted. Implement a storage system that allows individual or role-based access control.
- Easy browsing: The storage should allow easy browsing, sorting, and filtering. Centralized storage allows logs to be easily and quickly collated from different subsystems, giving you a holistic view of your logs—crucial, given the growth of distributed systems and cloud-based services.
- Rotation: To comply with regulations and reduce costs, the storage system should support log rotation. This means that when storage limits are exceeded or retention periods expire, old or irrelevant data is automatically deleted.
- Scalability: Scalability will ensure that your logs will not be lost if your service usage grows rapidly. To limit the cost of storing vast amounts of data, a storage service might compress files containing log messages, especially if the data is old or is being stored only because of a retention policy.
- Indexing: Like other kinds of databases, logs benefit from indexing for faster access and more efficient filtering and sorting.
#2 Normalization
Since log messages may come from disparate sources, such as different modules of an application or different microservices of the server, data received by the log system may be in diverse formats. Many software systems use the legacy syslog format, while others have their own, e.g., Apple’s unified logging system, which is used in macOS and iOS applications. Data should be automatically converted to a consistent format when it is received and stored.
Modern log services support various filters and parsers that will format logs to one standard. However, data should follow the standards which are recognized by your log analysis tools.
#3 Correlating
An effective log analysis tool should allow the collation of log messages from different sources. This is crucial for investigation of incidents that occur and are logged in one subsystem, but are actually caused by a failure in another subsystem. Such scenarios require a deep understanding of the context and juxtaposition of all relevant events from all involved software components.
#4 Monitoring
Regular monitoring of incoming log messages allows for quicker identification of unusual activity, prompt reactions to security incidents, and earlier handling of performance drops. As a result, cyberattacks can be averted and the service operates without interruption. This is especially important for services where even a brief downtime can result in significant loss of revenue, like the financial services industry or enterprises.
Nowadays, monitoring often includes machine learning, which adapts to the specifics of your use case and constantly learns to predict events by analyzing more and more data. With the help of machine learning, logging systems can detect patterns in log messages that could, for instance, be a sign of a cyberattack, but aren’t obvious to human interpreters.
#5 Real-Time Updates
In order to receive timely alerts from a managed logging system, log messages should be written into the system as close to real time as possible. While this may seem obvious, it’s not that simple to implement.
Log collection demands computational powers, and sending log messages to a server consumes network bandwidth. However, as log collection is not critical to the immediate user experience, it’s not always a priority. As a result, many systems send log messages using buffers and background queuing.
As often happens with IT systems, actual “real-timeness” is also a matter of a tradeoff: If logs are processed in the background with low priority, they may be analyzed and interpreted later than desired. On the other hand, if logging is given a high priority, the software’s responsiveness may suffer.
Managed Logging
Managed logging systems, also known as logging as a service (LaaS,) are centralized storage systems for log collection and analysis. These services save companies the investment of building and maintaining their own solutions.
Managed logging systems typically offer flexible options for log storage and rotation, and a user-friendly interface for displaying, sorting, and filtering historical data. However, different systems offer different feature sets, so it’s advisable to familiarize yourself with what a service provides before making a final decision; switching between managed logging systems can be both costly and cumbersome.
Gcore Managed Logging
Gcore Managed Logging stores logs collected from different sources and compiles them into a single, intuitive system that can be browsed using OpenSearch Dashboards. For better reliability, we use Kafka servers as an intermediary buffer and retain received log messages, ensuring they remain available even if the log source is currently down.
Conclusion
Log collection and analysis ultimately lead to a better user experience delivered by your services and applications. The best practices discussed in this article can help make working with the collected logs easier and more efficient.
Managed logging services, such as Gcore Managed Logging, help you to get the most from your logs. You get a centralized persistent storage that accumulates logs from all your services and displays them on a dashboard, where you can easily collate, filter and monitor your log messages.
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Cloud security is the discipline of protecting cloud-based infrastructure, applications, and data from internal and external threats, ensuring confidentiality, integrity, and availability of cloud resources. This protection model has become important as organizations increasingly move their operations to cloud environments.Cloud security operates under a shared responsibility model where providers secure the infrastructure while customers secure their deployed applications, data, and access policies. This responsibility distribution varies by service model, with Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS) each requiring different levels of customer involvement.The model creates clear boundaries between provider and customer security obligations.Cloud security protects resources and data individually rather than relying on a traditional perimeter defense approach. 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Cloud computing: types, deployment models, benefits, and how it works
Cloud computing is a model for enabling on-demand network access to a shared pool of configurable computing resources, such as networks, servers, storage, applications, and services that can be rapidly provisioned and released with minimal management effort or service provider interaction. According to research by Gartner (2024), the global cloud computing market size is projected to reach $1.25 trillion by 2025, reflecting the rapid growth and widespread adoption of these services.The National Institute of Standards and Technology (NIST) defines five core characteristics that distinguish cloud computing from traditional IT infrastructure. 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Public cloud services are offered over the internet to anyone, private clouds are proprietary networks serving limited users, hybrid clouds combine public and private cloud features, and community clouds serve specific groups with shared concerns. Each model provides different levels of control, security, and cost structures.Over 90% of enterprises use some form of cloud services as of 2024, according to Forrester Research (2024), making cloud computing knowledge important for modern business operations. This widespread adoption reflects how cloud computing has become a cornerstone of digital change and competitive advantage across industries.What is cloud computing?Cloud computing is a model that delivers computing resources like servers, storage, databases, and software over the internet on demand, allowing users to access and use these resources without owning or managing the physical infrastructure. Instead of buying and maintaining your own servers, you can rent computing power from cloud providers and scale resources up or down based on your needs.Over 90% of enterprises now use some form of cloud services, with providers typically guaranteeing 99.9% or higher uptime in their service agreements.The three main service models offer different levels of control and management. Infrastructure as a Service (IaaS) provides basic computing resources like virtual machines and storage. Platform as a Service (PaaS) adds development tools and runtime environments, and Software as a Service (SaaS) delivers complete applications that are ready to use. Each model handles different aspects of the technology stack, so you only manage what you need while the provider handles the rest.Cloud use models vary by ownership and access control. Public clouds serve multiple customers over the internet, private clouds operate exclusively for one organization, and hybrid clouds combine both approaches for flexibility. This variety lets organizations choose the right balance of cost, control, and security for their specific needs while maintaining the core benefits of cloud computing's flexible, elastic infrastructure.What are the main types of cloud computing services?The main types of cloud computing services refer to the different service models that provide computing resources over the internet with varying levels of management and control. The main types of cloud computing services are listed below.Infrastructure as a service (IaaS): This model provides basic computing infrastructure, including virtual machines, storage, and networking resources over the internet. Users can install and manage their own operating systems, applications, and development frameworks while the provider handles the physical hardware.Platform as a service (PaaS): This service offers a complete development and use environment in the cloud, including operating systems, programming languages, databases, and web servers. Developers can build, test, and use applications without managing the underlying infrastructure complexity.Software as a service (SaaS): This model delivers fully functional software applications over the internet through a web browser or mobile app. Users access the software on a subscription basis without needing to install, maintain, or update the applications locally.Function as a service (FaaS): Also known as serverless computing, this model allows developers to run individual functions or pieces of code in response to events. The cloud provider automatically manages server provisioning, scaling, and maintenance while charging only for actual compute time used.Database as a service (DBaaS): This service provides managed database solutions in the cloud, handling database administration tasks like backups, updates, and scaling. Organizations can access database functionality without maintaining physical database servers or hiring specialized database administrators.Storage as a service (STaaS): This model offers flexible cloud storage solutions for data backup, archiving, and file sharing needs. Users can store and retrieve data from anywhere with internet access while paying only for the storage space they actually use.What are the different cloud deployment models?Cloud use models refer to the different ways organizations can access and manage cloud computing resources based on ownership, location, and access control. The cloud use models are listed below.Public cloud: Services are delivered over the internet and shared among multiple organizations by third-party providers. Anyone can purchase and use these services on a pay-as-you-go basis, making them cost-effective for businesses without large upfront investments.Private cloud: Computing resources are dedicated to a single organization and can be hosted on-premises or by a third party. This model offers greater control, security, and customization options but requires higher costs and more management overhead.Hybrid cloud: Organizations combine public and private cloud environments, allowing data and applications to move between them as needed. This approach provides flexibility to keep sensitive data in private clouds while using public clouds for less critical workloads.Community cloud: Multiple organizations with similar requirements share cloud infrastructure and costs. Government agencies, healthcare organizations, or financial institutions often use this model to meet specific compliance and security standards.Multi-cloud: Organizations use services from multiple cloud providers to avoid vendor lock-in and improve redundancy. This plan allows businesses to choose the best services from different providers while reducing dependency on any single vendor.How does cloud computing work?Cloud computing works by delivering computing resources like servers, storage, databases, and software over the internet on an on-demand basis. Instead of owning physical hardware, users access these resources through web browsers or applications, while cloud providers manage the underlying infrastructure in data centers worldwide.The system operates through a front-end and back-end architecture. The front end includes your device, web browser, and network connection that you use to access cloud services.The back end consists of servers, storage systems, databases, and applications housed in the provider's data centers. When you request a service, the cloud infrastructure automatically allocates the necessary resources from its shared pool.The technology achieves its flexibility through virtualization, which creates multiple virtual instances from single physical servers. Resource pooling allows providers to serve multiple customers from the same infrastructure, while rapid elasticity automatically scales resources up or down based on demand.This elastic scaling can reduce resource costs by up to 30% compared to fixed infrastructure, according to McKinsey (2024), making cloud computing both flexible and cost-effective for businesses of all sizes.What are the key benefits of cloud computing?The key benefits of cloud computing refer to the advantages organizations and individuals gain from using internet-based computing services instead of traditional on-premises infrastructure. The key benefits of cloud computing are listed below.Cost reduction: Organizations eliminate upfront hardware investments and reduce ongoing maintenance expenses by paying only for resources they actually use. Cloud providers handle infrastructure management, reducing IT staffing costs and operational overhead.Flexibility and elasticity: Computing resources can expand or contract automatically based on demand, ensuring best performance during traffic spikes. This flexibility prevents over-provisioning during quiet periods and under-provisioning during peak usage.Improved accessibility: Users can access applications and data from any device with an internet connection, enabling remote work and global collaboration. This mobility supports modern work patterns and increases productivity across distributed teams.Enhanced reliability: Cloud providers maintain multiple data centers with redundant systems and backup infrastructure to ensure continuous service availability.Automatic updates and maintenance: Software updates, security patches, and system maintenance happen automatically without user intervention. This automation reduces downtime and ensures systems stay current with the latest features and security protections.Disaster recovery: Cloud services include built-in backup and recovery capabilities that protect against data loss from hardware failures or natural disasters. Recovery times are typically faster than traditional backup methods since data exists across multiple locations.Environmental effectiveness: Shared cloud infrastructure uses resources more effectively than individual company data centers, reducing overall energy consumption. Large cloud providers can achieve better energy effectiveness through economies of scale and advanced cooling technologies.What are the drawbacks and challenges of cloud computing?The drawbacks and challenges of cloud computing refer to the potential problems and limitations organizations may face when adopting cloud-based services. They are listed below.Security concerns: Organizations lose direct control over their data when it's stored on third-party servers. Data breaches, unauthorized access, and compliance issues become shared responsibilities between the provider and customer. Sensitive information may be vulnerable to cyber attacks targeting cloud infrastructure.Internet dependency: Cloud services require stable internet connections to function properly. Poor connectivity or outages can completely disrupt business operations and prevent access to critical applications. Remote locations with limited bandwidth face particular challenges accessing cloud resources.Vendor lock-in: Switching between cloud providers can be difficult and expensive due to proprietary technologies and data formats. Organizations may become dependent on specific platforms, limiting their flexibility to negotiate pricing or change services. Migration costs and technical complexity often discourage switching providers.Limited customization: Cloud services offer standardized solutions that may not meet specific business requirements. Organizations can't modify underlying infrastructure or install custom software configurations. This restriction can force businesses to adapt their processes to fit the cloud platform's limitations.Ongoing costs: Monthly subscription fees can accumulate to exceed traditional on-premise infrastructure costs over time. Unexpected usage spikes or data transfer charges can lead to budget overruns. Organizations lose the asset value that comes with owning physical hardware.Performance variability: Shared cloud resources can experience slower performance during peak usage periods. Network latency affects applications requiring real-time processing or frequent data transfers. Organizations can't guarantee consistent performance levels for mission-critical applications.Compliance complexity: Meeting regulatory requirements becomes more challenging when data is stored across multiple locations. Organizations must verify that cloud providers meet industry-specific compliance standards. Audit trails and data governance become shared responsibilities that require careful coordination.Gcore Edge CloudWhen building AI applications that require serious computational power, the infrastructure you choose can make or break your project's success. Whether you're training large language models, running complex inference workloads, or tackling high-performance computing challenges, having access to the latest GPU technology without performance bottlenecks becomes critical.Gcore's AI GPU Cloud Infrastructure addresses these demanding requirements with bare metal NVIDIA H200. H100. A100. L40S, and GB200 GPUs, delivering zero virtualization overhead for maximum performance. The platform's ultra-fast InfiniBand networking and multi-GPU cluster support make it particularly well-suited for distributed training and large-scale AI workloads, starting from just €1.25/hour. Multi-instance GPU (MIG) support also allows you to improve resource allocation and costs for smaller inference tasks.Discover how Gcore's bare metal GPU performance can accelerate your AI training and inference workloads at https://gcore.com/gpu-cloud.Frequently asked questionsPeople often have questions about cloud computing basics, costs, and how it fits their specific needs. These answers cover the key service models, use options, and practical considerations that help clarify what cloud computing can do for your organization.What's the difference between cloud computing and traditional hosting?Cloud computing delivers resources over the internet on demand, while traditional hosting provides fixed server resources at dedicated locations. Cloud offers elastic growth and pay-as-you-go pricing, whereas traditional hosting requires upfront capacity planning and fixed costs regardless of actual usage.What is cloud computing security?Cloud computing security protects data, applications, and infrastructure in cloud environments through shared responsibility models between providers and users. Cloud providers secure the underlying infrastructure while users protect their data, applications, and access controls.What is virtualization in cloud computing?Virtualization in cloud computing creates multiple virtual machines (VMs) on a single physical server using hypervisor software that separates computing resources. This technology allows cloud providers to increase hardware effectiveness and offer flexible, isolated environments to multiple users simultaneously.Is cloud computing secure for business data?Yes, cloud computing is secure for business data when proper security measures are in place, with major providers offering encryption, access controls, and compliance certifications that often exceed what most businesses can achieve on-premises. Cloud service providers typically guarantee 99.9% or higher uptime in service level agreements while maintaining enterprise-grade security standards.How much does cloud computing cost compared to on-premises infrastructure?Cloud computing typically costs 20-40% less than on-premises infrastructure due to shared resources, reduced hardware purchases, and lower maintenance expenses, according to IDC (2024). However, costs vary primarily based on usage patterns, with predictable workloads sometimes being cheaper on-premises while variable workloads benefit more from cloud's pay-as-you-go model.How do I choose between IaaS, PaaS, and SaaS?Choose based on your control needs. IaaS gives you full infrastructure control, PaaS handles infrastructure so you focus on development, and SaaS provides ready-to-use applications with no technical management required.
Pre-configure your dev environment with Gcore VM init scripts
Provisioning new cloud instances can be repetitive and time-consuming if you’re doing everything manually: installing packages, configuring environments, copying SSH keys, and more. With cloud-init, you can automate these tasks and launch development-ready instances from the start.Gcore Edge Cloud VMs support cloud-init out of the box. With a simple YAML script, you can automatically set up a development-ready instance at boot, whether you’re launching a single machine or spinning up a fleet.In this guide, we’ll walk through how to use cloud-init on Gcore Edge Cloud to:Set a passwordInstall packages and system updatesAdd users and SSH keysMount disks and write filesRegister services or install tooling like Docker or Node.jsLet’s get started.What is cloud-init?cloud-init is a widely used tool for customizing cloud instances during the first boot. It reads user-provided configuration data—usually YAML—and uses it to run commands, install packages, and configure the system. In this article, we will focus on Linux-based virtual machines.How to use cloud-init on GcoreFor Gcore Cloud VMs, cloud-init scripts are added during instance creation using the User data field in the UI or API.Step 1: Create a basic scriptStart with a simple YAML script. Here’s one that updates packages and installs htop:#cloud-config package_update: true packages: - htop Step 2: Launch a new VM with your scriptGo to the Gcore Customer Portal, navigate to VMs, and start creating a new instance (or just click here). When you reach the Additional options section, enable the User data option. Then, paste in your YAML cloud-init script.Once the VM boots, it will automatically run the script. This works the same way for all supported Linux distributions available through Gcore.3 real-world examplesLet’s look at three examples of how you can use this.Example 1: Add a password for a specific userThe below script sets the for the default user of the selected operating system:#cloud-config password: <password> chpasswd: {expire: False} ssh_pwauth: True Example 2: Dev environment with Docker and GitThe following script does the following:Installs Docker and GitAdds a new user devuser with sudo privilegesAuthorizes an SSH keyStarts Docker at boot#cloud-config package_update: true packages: - docker.io - git users: - default - name: devuser sudo: ALL=(ALL) NOPASSWD:ALL groups: docker shell: /bin/bash ssh-authorized-keys: - ssh-rsa AAAAB3Nza...your-key-here runcmd: - systemctl enable docker - systemctl start docker Example 3: Install Node.js and clone a repoThis script installs Node.js and clones a GitHub repo to your Gcore VM at launch:#cloud-config packages: - curl runcmd: - curl -fsSL https://deb.nodesource.com/setup_18.x | bash - - apt-get install -y nodejs - git clone https://github.com/example-user/dev-project.git /home/devuser/project Reusing and versioning your scriptsTo avoid reinventing the wheel, keep your cloud-init scripts:In version control (e.g., Git)Templated for different environments (e.g., dev vs staging)Modular so you can reuse base blocks across projectsYou can also use tools like Ansible or Terraform with cloud-init blocks to standardize provisioning across your team or multiple Gcore VM environments.Debugging cloud-initIf your script doesn’t behave as expected, SSH into the instance and check the cloud-init logs:sudo cat /var/log/cloud-init-output.log This file shows each command as it ran and any errors that occurred.Other helpful logs:/var/log/cloud-init.log /var/lib/cloud/instance/user-data.txt Pro tip: Echo commands or write log files in your script to help debug tricky setups—especially useful if you’re automating multi-node workflows across Gcore Cloud.Tips and best practicesIndentation matters! YAML is picky. Use spaces, not tabs.Always start the file with #cloud-config.runcmd is for commands that run at the end of boot.Use write_files to write configs, env variables, or secrets.Cloud-init scripts only run on the first boot. To re-run, you’ll need to manually trigger cloud-init or re-create the VM.Automate it all with GcoreIf you're provisioning manually, you're doing it wrong. Cloud-init lets you treat your VM setup as code: portable, repeatable, and testable. Whether you’re spinning up ephemeral dev boxes or preparing staging environments, Gcore’s support for cloud-init means you can automate it all.For more on managing virtual machines with Gcore, check out our product documentation.Explore Gcore VM product docs
How to cut egress costs and speed up delivery using Gcore CDN and Object Storage
If you’re serving static assets (images, videos, scripts, downloads) from object storage, you’re probably paying more than you need to, and your users may be waiting longer than they should.In this guide, we explain how to front your bucket with Gcore CDN to cache static assets, cut egress bandwidth costs, and get faster TTFB globally. We’ll walk through setup (public or private buckets), signed URL support, cache control best practices, debugging tips, and automation with the Gcore API or Terraform.Why bother?Serving directly from object storage hits your origin for every request and racks up egress charges. With a CDN in front, cached files are served from edge—faster for users, and cheaper for you.Lower TTFB, better UXWhen content is cached at the edge, it doesn’t have to travel across the planet to get to your user. Gcore CDN caches your assets at PoPs close to end users, so requests don’t hit origin unless necessary. Once cached, assets are delivered in a few milliseconds.Lower billsMost object storage providers charge $80–$120 per TB in egress fees. By fronting your storage with a CDN, you only pay egress once per edge location—then it’s all cache hits after that. If you’re using Gcore Storage and Gcore CDN, there’s zero egress fee between the two.Caching isn’t the only way you save. Gcore CDN can also compress eligible file types (like HTML, CSS, JavaScript, and JSON) on the fly, further shrinking bandwidth usage and speeding up file delivery—all without any changes to your storage setup.Less origin traffic and less data to transfer means smaller bills. And your storage bucket doesn’t get slammed under load during traffic spikes.Simple scaling, globallyThe CDN takes the hit, not your bucket. That means fewer rate-limit issues, smoother traffic spikes, and more reliable performance globally. Gcore CDN spans the globe, so you’re good whether your users are in Tokyo, Toronto, or Tel Aviv.Setup guide: Gcore CDN + Gcore Object StorageLet’s walk through configuring Gcore CDN to cache content from a storage bucket. This works with Gcore Object Storage and other S3-compatible services.Step 1: Prep your bucketPublic? Check files are publicly readable (via ACL or bucket policy).Private? Use Gcore’s AWS Signature V4 support—have your access key, secret, region, and bucket name ready.Gcore Object Storage URL format: https://<bucket-name>.<region>.cloud.gcore.lu/<object> Step 2: Create CDN resource (UI or API)In the Gcore Customer Portal:Go to CDN > Create CDN ResourceChoose "Accelerate and protect static assets"Set a CNAME (e.g. cdn.yoursite.com) if you want to use your domainConfigure origin:Public bucket: Choose None for authPrivate bucket: Choose AWS Signature V4, and enter credentialsChoose HTTPS as the origin protocolGcore will assign a *.gcdn.co domain. If you’re using a custom domain, add a CNAME: cdn.yoursite.com CNAME .gcdn.co Here’s how it works via Terraform: resource "gcore_cdn_resource" "cdn" { cname = "cdn.yoursite.com" origin_group_id = gcore_cdn_origingroup.origin.id origin_protocol = "HTTPS" } resource "gcore_cdn_origingroup" "origin" { name = "my-origin-group" origin { source = "mybucket.eu-west.cloud.gcore.lu" enabled = true } } Step 3: Set caching behaviorSet Cache-Control headers in your object metadata: Cache-Control: public, max-age=2592000 Too messy to handle in storage? Override cache logic in Gcore:Force TTLs by path or extensionIgnore or forward query strings in cache keyStrip cookies (if unnecessary for cache decisions)Pro tip: Use versioned file paths (/img/logo.v3.png) to bust cache safely.Secure access with signed URLsWant your assets to be private, but still edge-cacheable? Use Gcore’s Secure Token feature:Enable Secure Token in CDN settingsSet a secret keyGenerate time-limited tokens in your appPython example: import base64, hashlib, time secret = 'your_secret' path = '/videos/demo.mp4' expires = int(time.time()) + 3600 string = f"{expires}{path} {secret}" token = base64.urlsafe_b64encode(hashlib.md5(string.encode()).digest()).decode().strip('=') url = f"https://cdn.yoursite.com{path}?md5={token}&expires={expires}" Signed URLs are verified at the CDN edge. Invalid or expired? Blocked before origin is touched.Optional: Bind the token to an IP to prevent link sharing.Debug and cache tuneUse curl or browser devtools: curl -I https://cdn.yoursite.com/img/logo.png Look for:Cache: HIT or MISSCache-ControlX-Cached-SinceCache not working? Check for the following errors:Origin doesn’t return Cache-ControlCDN override TTL not appliedCache key includes query strings unintentionallyYou can trigger purges from the Gcore Customer Portal or automate them via the API using POST /cdn/purge. Choose one of three ways:Purge all: Clear the entire domain’s cache at once.Purge by URL: Target a specific full path (e.g., /images/logo.png).Purge by pattern: Target a set of files using a wildcard at the end of the pattern (e.g., /videos/*).Monitor and optimize at scaleAfter rollout:Watch origin bandwidth dropCheck hit ratio (aim for >90%)Audit latency (TTFB on HIT vs MISS)Consider logging using Gcore’s CDN logs uploader to analyze cache behavior, top requested paths, or cache churn rates.For maximum savings, combine Gcore Object Storage with Gcore CDN: egress traffic between them is 100% free. That means you can serve cached assets globally without paying a cent in bandwidth fees.Using external storage? You’ll still slash egress costs by caching at the edge and cutting direct origin traffic—but you’ll unlock the biggest savings when you stay inside the Gcore ecosystem.Save money and boost performance with GcoreStill serving assets direct from storage? You’re probably wasting money and compromising performance on the table. Front your bucket with Gcore CDN. Set smart cache headers or use overrides. Enable signed URLs if you need control. Monitor cache HITs and purge when needed. Automate the setup with Terraform. Done.Next steps:Create your CDN resourceUse private object storage with Signature V4Secure your CDN with signed URLsCreate a free CDN resource now
Bare metal vs. virtual machines: performance, cost, and use case comparison
Choosing the right type of server infrastructure is critical to how your application performs, scales, and fits your budget. For most workloads, the decision comes down to two core options: bare metal servers and cloud virtual machines (VMs). Both can be deployed in the cloud, but they differ significantly in terms of performance, control, scalability, and cost.In this article, we break down the core differences between bare metal and virtual servers, highlight when to choose each, and explain how Gcore can help you deploy the right infrastructure for your needs. If you want to learn about either BM or VMs in detail, we’ve got articles for those: here’s the one for bare metal, and here’s a deep dive into virtual machines.Bare metal vs. virtual machines at a glanceWhen evaluating whether bare metal or virtual machines are right for your company, consider your specific workload requirements, performance priorities, and business objectives. Here’s a quick breakdown to help you decide what works best for you.FactorBare metal serversVirtual machinesPerformanceDedicated resources; ideal for high-performance workloadsShared resources; suitable for moderate or variable workloadsScalabilityOften requires manual scaling; less flexibleHighly elastic; easy to scale up or downCustomizationFull control over hardware, OS, and configurationLimited by hypervisor and provider’s environmentSecurityIsolated by default; no hypervisor layerShared environment with strong isolation protocolsCostHigher upfront cost; dedicated hardwarePay-as-you-go pricing; cost-effective for flexible workloadsBest forHPC, AI/ML, compliance-heavy workloadsStartups, dev/test, fast-scaling applicationsAll about bare metal serversA bare metal server is a single-tenant physical server rented from a cloud provider. Unlike virtual servers, the hardware is not shared with other users, giving you full access to all resources and deeper control over configurations. You get exclusive access and control over the hardware via the cloud provider, which offers the stability and security needed for high-demand applications.The benefits of bare metal serversHere are some of the business advantages of opting for a bare metal server:Maximized performance: Because they are dedicated resources, bare metal servers provide top-tier performance without sharing processing power, memory, or storage with other users. This makes them ideal for resource-intensive applications like high-performance computing (HPC), big data processing, and game hosting.Greater control: Since you have direct access to the hardware, you can customize the server to meet your specific requirements. This is especially important for businesses with complex, specialized needs that require fine-tuned configurations.High security: Bare metal servers offer a higher level of security than their alternatives due to the absence of virtualization. With no shared resources or hypervisor layer, there’s less risk of vulnerabilities that come with multi-tenant environments.Dedicated resources: Because you aren’t sharing the server with other users, all server resources are dedicated to your application so that you consistently get the performance you need.Who should use bare metal servers?Here are examples of instances where bare metal servers are the best option for a business:High-performance computing (HPC)Big data processing and analyticsResource-intensive applications, such as AI/ML workloadsGame and video streaming serversBusinesses requiring enhanced security and complianceAll about virtual machinesA virtual server (or virtual machine) runs on top of a physical server that’s been partitioned by a cloud provider using a hypervisor. This allows multiple VMs to share the same hardware while remaining isolated from each other.Unlike bare metal servers, virtual machines share the underlying hardware with other cloud provider customers. That means you’re using (and paying for) part of one server, providing cost efficiency and flexibility.The benefits of virtual machinesHere are some advantages of using a shared virtual machine:Scalability: Virtual machines are ideal for businesses that need to scale quickly and are starting at a small scale. With cloud-based virtualization, you can adjust your server resources (CPU, memory, storage) on demand to match changing workloads.Cost efficiency: You pay only for the resources you use with VMs, making them cost-effective for companies with fluctuating resource needs, as there is no need to pay for unused capacity.Faster deployment: VMs can be provisioned quickly and easily, which makes them ideal for anyone who wants to deploy new services or applications fast.Who should use virtual machines?VMs are a great fit for the following:Web hosting and application hostingDevelopment and testing environmentsRunning multiple apps with varying demandsStartups and growing businesses requiring scalabilityBusinesses seeking cost-effective, flexible solutionsWhich should you choose?There’s no one-size-fits-all answer. Your choice should depend on the needs of your workload:Choose bare metal if you need dedicated performance, low-latency access to hardware, or tighter control over security and compliance.Choose virtual servers if your priority is flexible scaling, faster deployment, and optimized cost.If your application uses GPU-based inference or AI training, check out our dedicated guide to VM vs. BM for AI workloads.Get started with Gcore BM or VMs todayAt Gcore, we provide both bare metal and virtual machine solutions, offering flexibility, performance, and reliability to meet your business needs. Gcore Bare Metal has the power and reliability needed for demanding workloads, while online virtual machines offers customizable configurations, free egress traffic, and flexibility.Compare Gcore BM and VM pricing now
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