What Is a Docker Container and How Is It Hosted?

In mid-2025, Google introduced the third version of its proprietary video generator: Veo. The new model not only creates high-quality visuals but also provides realistic audio tracks, including environmental sounds and character dialogues. In a sense, Google has created something entirely new—something revolutionary—a technology capable of making a quantum leap in video generation. Thanks to this, distinguishing real videos from AI-generated ones will soon become much more difficult. That’s why it’s important to understand what the new Veo 3 neural network is and which special tools Google provides for working with it. Let’s explore this in detail. What Is Google Veo 3 Google Veo is a generative model for creating videos, developed and released by Google in mid-2024. Its main innovation is the native ability to generate audio: sound effects, background music, and dialogues synchronized with lip movements. A frame from one of the official videos generated using Google Veo 3 The audio track of generated videos automatically adapts to the context of the scene, adding appropriate effects as needed: natural sounds, urban ambiance, musical accompaniment, and even human speech with dialects and accents specific to the characters. Thus, the Veo 3 artificial intelligence combines high-quality visuals, realistic physics, and synchronized audio. Features of Veo 3 The updated Veo 3 model has a number of features that distinguish it from other AI video generation services: Longer duration. The duration of generated videos can exceed the standard five seconds common for many AI video generators. The maximum video length is eight seconds. Synchronized audio support. Video is accompanied by environmental sounds, music, and speech, all realistically synchronized with the visuals. Physical accuracy. Hyper-realistic motion of objects, materials, characters, and light throughout the video. This combination of exceptional characteristics makes Google Veo 3 an ideal tool for generating cinematic, animated, or any other videos with high visual dynamics and deep storylines. Thanks to these features, Veo 3 can already be used in professional settings: for UGC content (for example, YouTube), short ads, or even full-length films. Another frame from one of the official videos generated using Google Veo 3 For instance, filmmaker Dave Clark has already used Veo 2 and Veo 3 in several of his short films. Another director, June Lau, also places great hopes on Google’s cutting-edge model, using Veo 3 to create a short film titled Dear Strangers. Filmmaker Yonatan Dor created his own short film, The History of Influencers, using Veo 3, featuring fictional influencers from different eras. In general, the number of directors and artists integrating Google’s AI tools into their content creation process is growing rapidly. However, it’s worth noting that Veo 3 is still not enough to create a full-fledged movie; it serves best as an auxiliary tool. Capabilities of Veo 3 The new version of Veo includes several ways to generate video using different types of input data: Text-to-video. The primary method of video generation in Veo 3 is based on a detailed (preferably very detailed) text description. Image-to-video. Veo 3 can generate videos based on text or images. Moreover, any image used as input can be enhanced with a textual description that clarifies the scene’s behavior. Video-to-video. Using additional tools (Flow), users can upload existing videos and apply modifications with Veo 3: adding or removing objects, changing visual styles, adjusting camera behavior, editing object movement, and their accompanying sounds. As previously noted, Veo 3 videos integrate all attributes of traditional, non-computer-generated footage. The standard output resolution is 720p, but the upscaling feature allows increasing it up to 4K. Veo 3 Tools It’s important to note that Veo 3 cannot be used “as is”—additional Google tools are required. Flow Google offers a special tool that combines Veo (video), Imagen (images), and Gemini (text) models in a single director-style interface called Flow. Essentially, it’s Google’s central content creation platform. With Flow, users can precisely edit videos: extend frames, add new details, animate specific elements, adjust camera movement, store styles, and more. This editor is ideal for solo and manual work as it allows quick creation of short clips with instant preview and fine-tuning. Everything happens in a single window. At the same time, Flow requires minimal technical setup: no cloud account, billing, or SDK is needed; video generation happens directly within the visual interface. Demonstration of the Flow graphical interface at the Google I/O 2025 presentation (Kerry Wan/ZDNET) Gemini With the Gemini LLM neural network, users can generate precise prompts for video generation via Flow. In simple terms, Gemini serves as a converter that transforms more human-style text descriptions into more machine-readable ones, though both are still in natural language and easy to understand. For example, you can find an image online or generate one using another AI tool (e.g., Midjourney), attach it to a message in the Gemini chatbot (or any other LLM), and provide an additional description: “I need precise prompt is needed for Google Veo 3 to generate a short video from this image, where three men are pushing a banana-shaped car with a driver at the wheel, and as the car gains speed, it gradually turns yellow.” Gemini will then generate a complete prompt for video generation and include explanatory comments, for example: “A vintage car, half-peeled banana, driven by a man in a hat, is being pushed by three other men from behind. The car is initially in black and white, but as it gains momentum and the men push harder, the banana part of the car gradually becomes fully ripe yellow. The background shows a field with trees in the distance, also in black and white. Dynamic camera movement, tracking the car as it accelerates.” This way, you can generate a video based on a reference image by following a simple sequence of steps: Generate a prompt for image generation using an LLM (based on a description). Generate the image (based on the prompt). Generate a prompt for video generation (based on the description and image). Generate the video (based on the prompt). Alternatively, you can use a ready-made reference image from the Internet: Generate a prompt for video generation (based on the description and image). Generate the video (based on the prompt). In a simplified version, you can also generate a video without using any reference images: Generate a prompt for video generation (based on the description). Generate the video (based on the prompt). Or, you can manually write the prompt for video generation from scratch :) Nevertheless, Gemini (naturally, in paid tiers) also allows generating videos using Veo 3. However, in most cases, Flow is used for video creation as it’s more convenient and visually intuitive. After all, Gemini is primarily designed for working with text rather than video. Vertex AI The Vertex AI platform represents an enterprise solution for large-scale cloud-based content generation and asset storage, that is, various media files needed for creating images and videos. In essence, it’s a fully managed platform for developing, training, deploying, and maintaining AI models. It brings together all the tools needed for every stage of the machine learning cycle, from data preparation to model performance monitoring. Thus: Flow provides a convenient and visual approach. Gemini delivers accurate and relevant prompts. Vertex AI ensures a reliable and scalable infrastructure. Together, they turn Veo 3 from an experimental service into a professional tool capable of solving real-world challenges across a wide variety of projects. How to Use Veo 3: Step-by-Step Guide After understanding the main tools, we can now look at how to generate a video using Veo 3. First of all, it’s important to note that to use Google Veo 3, you must have one of Google AI’s paid subscriptions: Google AI Pro. Expands the basic functionality of Google’s AI tools. Starting at $19 per month. Google AI Ultra. Offers maximum, virtually unlimited content-generation capabilities. Starting at $249 per month. There’s no other official way to use Veo 3 within the Google ecosystem. A paid subscription is required. The only exceptions are third-party intermediary services or Telegram bots that provide Veo 3 video generation on a pay-per-video basis. Another important detail: the Flow editor is only available in English. Moreover, prompts for Veo 3 must be written in English. The only exception is dialogue lines: they can be written in any other language, and Veo 3 will perfectly reproduce the described characters’ dialects. Such a level of synchronization between sound and video, with extraordinary precision, amazes (and sometimes even frightens) people well-acquainted with modern technology. Working with such a powerful generative model usually requires additional tools for convenient use. Therefore, Google offers several ways to interact with Veo 3, differing in their complexity. Using Flow Flow allows you to create scenes, control camera movement, manage assets, and edit clips, all without third-party tools. Essentially, it’s an intuitive visual editor for creating videos with Veo 3. Using it is simple: Sign in. On the Flow homepage, log in with your Google account. Create a project. Click the New project button. A page will open where you can enter a text prompt describing the desired video and its audio track. Choose input type. On the prompt input page, select the source type for your video: Text to Video, Frames to Video, or Ingredients to Video. Choosing the latter two enables extra settings for camera behavior and frame composition. Configure settings. On the same page, you can set generation parameters: the number of variants per prompt (1–4) and the model used (Veo 2 Fast, Veo 2 Quality, Veo 3 Highest Quality). Depending on the settings, each generation consumes 10–100 Flow credits. Enter the prompt. Type your text prompt in the input field. Generate. After entering the prompt, click the arrow button and wait 2–7 minutes. The generated videos and prompts will appear in the request history above the input field. This is Flow’s basic functionality. In many ways, it resembles LLM chatbots, only instead of text, it produces video. Naturally, Flow also includes advanced tools for composing and editing video clips. Using Gemini To generate a video directly in the Gemini chatbot, follow these simple steps: Sign in. Log in to Gemini with your Google account. After successful sign-in, the chat interface opens. Activate video mode. Click the Video button next to the message input field to switch to video generation mode. This button is only available to users with a paid plan. Enter the prompt. In the input field, describe the desired video in detail: environment, characters, lighting, camera behavior, style, and other details. Generate. Click the arrow button or press Enter. The generation process takes 2–7 minutes, and the finished video will appear directly in the chat window. Thus, Gemini unifies the generation of text (Gemini), images (Imagen), and video (Veo) in a single interface, which is quite convenient. Of course, Gemini alone isn’t enough for professional video work—you’ll also need Flow and dedicated video-editing software. However, for presentations or idea visualization, Gemini is more than sufficient. Using Vertex AI Another way to use the Veo 3 model is through Vertex AI. Unlike Flow, which is built for creative work, Vertex AI is designed for professional, large-scale, and automated content creation. Here’s a short sequence for generating videos with Vertex AI: Sign in. Log in to Google Cloud Console with your Google account, then navigate to the Vertex AI section. Open Media Studio. From the left sidebar, select Media Studio, and the page for choosing media generation models will open. Choose Veo. Enter the prompt. On the next page, enter the text description of your video and configure the main parameters. Generate. Click Generate and wait a few minutes for the video to appear in the interface. Vertex AI provides distributed computing, cost monitoring, asset storage, and ML-process management, all centralized in Google Cloud. Thanks to the REST API, the platform also allows programmatically launching hundreds of video generations, integrating Veo 3 into third-party applications. Pros and Cons of Veo 3 Google Veo 3 opens new horizons for automated video production, combining advanced audio generation with high-quality visualization. Understanding its strengths and weaknesses helps identify optimal use cases. Advantages: Visual and physical realism. Beyond realistic lighting, shadows, textures, and details, the model simulates accurate physical behavior of objects, substances, and characters. Audio-video synchronization. Native audio generation (sound effects, music, dialogues) is tightly synchronized with the visuals. Advanced prompt interpretation. Deep understanding of complex queries: mood, style, camera perspective (panning, zoom). Extensive creative control enables frame-to-frame consistency, maintaining stable characters and environments across angles. Extended toolset. Integration with tools like Flow, Vertex AI, and Gemini provides a unified environment for generation, editing, and scene management. Disadvantages: Limited duration. The maximum video length (8 seconds at 24 fps) is independent of resolution. This is still short for production-scale work. Synchronization artifacts. While lip-sync accuracy is high, minor artifacts can appear, especially with background characters (unnatural lip movement or blurring). Small body parts like hands, elbows, or feet may occasionally deform. Prompt interpretation errors. The model sometimes overlooks details, misreads subtle emotions, or ignores secondary characters. High cost. Subscription plans are expensive, mostly suitable for professional studios but less accessible for students, freelancers, or solo creators. AI watermarking. Every video carries an invisible SynthID marker that can be detected via a special app. Misinformation risks. The exceptional realism of Veo 3 could enable convincing deepfakes or spread fake news, raising ethical concerns. Although Veo 3’s strengths outweigh its drawbacks, it can’t yet fully replace traditional video production. Still, it can easily serve as a powerful supplementary tool alongside classic video and graphics software. Conclusion It’s safe to say that Google Veo 3 is an innovative model that elevates AI-driven video generation to an astonishing new level. It combines realistic graphics, precise audio synchronization, and a robust physics engine. The generated videos are so realistic and coherent that untrained viewers may not notice they’re artificial—and sometimes can’t tell at all. The new version is perfect for those who need fast, high-quality short clips, from marketers and content creators to artists and filmmakers.

A hybrid cloud is an infrastructure model that combines private and public cloud services. Private clouds are owned by the company, while public clouds rely on provider resources, such as Amazon Web Services (AWS), Microsoft Azure, or Hostman. Hybrid Cloud Architecture The architecture of a hybrid cloud consists of the company’s own data center, external resources, and private hosting. These components are connected through a unified management process. The key feature of the hybrid approach is the ability to connect systems that handle business-critical data, which cannot be placed on public infrastructure, while still leveraging the advantages of external hosting, such as on-demand scaling. Hybrid Cloud Advantages Hybrid cloud addresses the limitations of both public and private cloud services. It is a compromise solution with several important benefits: Reduced computing costs compared to relying solely on in-house hardware. Flexible management: critical data can remain on private infrastructure, while less sensitive workloads can be handled by the provider. Easy scalability by using resources offered by cloud providers. Disadvantages Some drawbacks of hybrid cloud include: Integration complexity: establishing a reliable connection between private and public environments can be challenging. Risk of failure: if resources are poorly distributed or one segment fails, the entire system may be affected. Oversubscription: some providers may allocate the same resources to multiple clients. Such issues can be avoided by carefully selecting a provider. For instance, when configuring a hybrid cloud on Hostman, you can rely on expert support and guaranteed access to the resources you pay for. Use Cases Here are several examples of situations where hybrid cloud infrastructure is particularly useful: Rapid Project Scaling Suppose you run an online store. During high-traffic events like Black Friday, website traffic spikes dramatically. Cloud architecture reduces the risk of server crashes during peak loads. Additional resources can be deployed in the cloud as needed and removed once demand decreases, preventing unnecessary costs. Scalability is also crucial for big data processing. Using cloud resources is more cost-effective than maintaining a large in-house data center. Data Segregation Confidential client information can be stored in a private cloud, while corporate applications run on public cloud infrastructure. Public hosting is also suitable for storing backup copies, ensuring business continuity if the primary system encounters problems. Development and Testing External cloud resources can be used for deployment and testing, allowing teams to simulate workloads and identify bugs not visible in local environments. After validation, the new version can be deployed to the main infrastructure. Conclusion Hybrid cloud is a practical approach for companies that value flexibility and aim for rapid growth. It combines the advantages of private and public hosting, enabling multiple use cases, from quickly deploying additional resources to securely storing sensitive data and testing new products.

A hypervisor is a process that helps separate the operating system and running applications from the hardware component. This typically refers to specialized software. However, embedded hypervisors also exist. These are available from the start, rather than being launched after system deployment. The hypervisor is what enables the development of the virtualization concept. Hardware virtualization is the ability to manage multiple virtual machines (VMs) on a single device. They become guest systems. An example of virtualization in use is renting a virtual server from a hosting provider. Multiple isolated spaces are located on one device. Different software can be installed on them. This increases resource utilization efficiency. Memory, computing power, and bandwidth are distributed among virtual servers rather than sitting idle waiting for load. Virtualization is not limited to servers. Storage hypervisors use it for data storage. They run on physical hardware as VMs, within the system, or in another storage network. Hypervisors also help virtualize desktops and individual applications. History of the Hypervisor Virtualization began being used in the 1960s. For the most part, the virtualization environment was applied to IBM mainframes. Developers used it to test ideas and to study and refine hardware concepts. This made it possible to deploy systems and fix errors without threats to the stability of the primary equipment. At the beginning of the new millennium, virtualization received a powerful boost thanks to widespread adoption in Unix family operating systems. There were several reasons for mass distribution: Server hardware capabilities improved. Architecture refinement led to increased reliability and security. Developers began implementing hardware virtualization on processors based on x86 architecture. This led to mass adoption. Since then, virtualization systems have been used not only for solving complex engineering tasks, but also for simple resource sharing and even home entertainment. In recent years, virtualization has expanded beyond x86 to ARM-based processors, with solutions like Apple's Virtualization framework and AWS Graviton instances becoming increasingly common. Advantages of Hypervisors Although virtual machines run on a single device, logical boundaries are built between them. This isolation protects against threats. If one virtual machine fails, others continue to operate. Another huge advantage is mobility. VMs are independent of hardware. Want to migrate an environment to another server? No problem. Need to deploy a VM on a local computer? Also a simple task. Less connection to hardware means fewer dependencies. Finally, resource savings. A hosting provider manages equipment more rationally by providing one physical server to multiple clients. Machines don't sit idle, but bring benefit with all their capabilities. Clients don't overpay for physical equipment while simultaneously gaining the ability to scale quickly and conveniently if such a need arises. Types of Hypervisors There are two types of hypervisors, concisely named Type 1 and Type 2. TYPE 1: bare-metal hypervisors. They run on the computer's hardware. From there, they manage the equipment and guest systems. This type of virtualization is offered by Xen, Microsoft Hyper-V, Oracle VM Server, and VMware ESXi. Modern cloud providers also use specialized Type 1 hypervisors like AWS Nitro and KVM-based solutions. TYPE 2: hosted hypervisors. They operate within the system as regular programs. Virtual systems in this case appear in the main system as processes. Examples include VirtualBox, VMware Workstation, VMware Player, and Parallels Desktop. To increase the stability, security, and performance of hypervisors, developers combine features of both types, creating hybrid solutions. They work both on "bare metal" and using the host's main system. Examples include recent versions of Xen and Hyper-V. The boundaries between bare-metal and hosted hypervisors are gradually blurring. However, it's still possible to determine the type. Though there's usually no practical need for this. Hypervisor Comparison Virtualization types are not the only difference. Hypervisors solve different tasks, have different hardware requirements, and have licensing peculiarities. Hyper-V A free hypervisor for servers running Windows OS. Its features: No graphical interface; configuration and debugging must be done in the console. Licenses must be purchased for all VMs running Windows. No technical support, although updates are released regularly. Hyper-V uses encryption to protect virtual machines and also allows reducing and expanding disk space. Among the disadvantages: there's no USB Redirection needed for connecting USB devices to virtual hosts. Instead, Discrete Device Assignment is used, which is not a complete replacement. VMware VMware is a virtualization technology created by the American company of the same name. It's used to organize virtual server operations. In 2024, Broadcom acquired VMware and introduced significant changes to licensing models and product portfolios, shifting focus toward larger enterprise customers. Many know about ESXi, a hardware hypervisor built on a lightweight Linux kernel called VMkernel. It contains all the necessary virtualization tools. A license must be purchased for each physical processor to operate. The amount of RAM and how many virtual machines you plan to run on your equipment doesn't matter. Note that under Broadcom's ownership, licensing models have evolved, with many standalone products being bundled into subscription packages. VMware has free virtualization tools. However, their capabilities are insufficient for professional use. For example, the API works in read-only mode, and the number of vCPUs must not exceed eight. Additionally, there are no backup management tools.  VMware Workstation The VMware Workstation hypervisor was created in 1999. Now it's a virtualization tool for x86-64 computers with Windows and Linux. The hypervisor supports over two hundred guest operating systems. VMware Hypervisor has a free version with reduced functionality, typically used for familiarization and testing. In 2024, Broadcom made VMware Workstation Pro free for personal use, making it more accessible to individual users and developers. KVM An open-source tool designed for Linux/x86-based servers. Intel-VT and AMD-V extensions are also supported, and ARM virtualization extensions are increasingly common. The KVM hypervisor is quite popular. It's used in many network projects: financial services, transportation systems, and even in the government sector. KVM is integrated into the Linux kernel, so it runs quickly. Major cloud providers use KVM as the foundation for their virtualization infrastructure. However, some disadvantages remain. Built-in services are not comparable in functionality to other hypervisors' solutions. To add capabilities, third-party solutions must be used, such as SolusVM or more modern management platforms like Proxmox VE. KVM is being refined by a community of independent developers, so gradually there are fewer shortcomings in its operation. The quality of the hypervisor is confirmed by hosting providers who choose it for virtualization on their equipment. Xen Xen is a cross-platform hypervisor solution that supports hardware virtualization and paravirtualization. It features minimal code volume. Modules are used to expand functionality. Open source code allows any specialist to modify Xen for their needs. Oracle VM VirtualBox Oracle VM VirtualBox is a cross-platform hypervisor for Windows, Linux, macOS, and other systems.  It is one of the most popular hypervisors, especially in the mass market segment. This is partly because VM VirtualBox has open source code. The program is distributed under the GNU GPL license. A distinctive feature: VirtualBox offers broad compatibility across different host and guest operating system combinations, making it ideal for development and testing environments. Hypervisors vs. Containers Hypervisors are often contrasted with containers. They allow deploying a greater number of applications on a single device. You already know what a hypervisor is and how it works. The problem is that VMs consume many resources. To operate, you need to make a copy of the operating system, plus a complete copy of the equipment for this system to function. If you allocate a nominal 4 GB of RAM to a VM, then the main device will have 4 GB less RAM. Unlike VMs, a container only uses the operating system's resources. It also needs power to run a specific application. But much less is required than to run an entire OS. However, containers cannot completely replace VMs. This is partly due to the increased risk of losing all data. Containers are located inside the operating system. If the host is attacked, all containers can be damaged or lost. A virtualization server creates multiple virtual machines. They don't interact with each other; there are clear boundaries between them. If one machine is attacked, the others remain safe. Along with all their contents. In modern infrastructure, containers and VMs are often used together. Container orchestration platforms like Kubernetes typically run on virtual machines, combining the isolation benefits of VMs with the efficiency of containers. This hybrid approach has become the standard for cloud-native applications. Security Issues Hypervisors are more secure than containers. However, they still have problems. Theoretically, it's possible to create a rootkit and malicious application that installs itself disguised as a hypervisor. Such a hack is called hyperjacking. It's difficult to detect. Protection doesn't trigger because the malicious software is already installed and intercepts system actions. The system continues to work, and the user doesn't even suspect there are problems. To protect the system from rootkits, specialists are developing various approaches that protect it without negatively affecting performance. Modern processors include hardware-based security features like Intel TXT and AMD Secure Encrypted Virtualization to help prevent hypervisor-level attacks. How to Choose a Hypervisor The choice is vast: VMware or VirtualBox, Hyper-V or KVM. There's one universal recommendation: focus on the tasks. If you need to test an operating system in a virtual machine on a home computer, VirtualBox will suffice. If you're looking for a solution to organize a corporate-level server network, then the focus shifts toward VMware tools (keeping in mind recent licensing changes), KVM-based solutions like Proxmox, or cloud-native options. For cloud deployments, consider managed hypervisor solutions from providers like Hostman, AWS, Azure, or Google Cloud, which abstract away much of the complexity while providing enterprise-grade performance and security.