VDS vs Shared vs Dedicated vs Cloud. What is The Best Option for You?

The year 2025 has truly become the year of flourishing AI agents, and this trend continues to gain momentum. Not long ago, many were only discussing the concept, but today we can see real-world applications of AI agents actively being integrated into development processes. Of particular interest are open-source AI agents, which allow teams not only to use but also to adapt the technology to their own needs. In this article, we will look at how these AI tools have evolved and how they help solve complex software engineering tasks. We’ll start with an overview of the early but important players, such as Devstral, and move on to more up-to-date AI agent applications available now. Overview of the Open-Source AI Agent Landscape for Coding The first noticeable steps toward open agents for development were made with models such as Devstral. Developed in collaboration between Mistral AI and All Hands AI, Devstral became a breakthrough solution. Thanks to its lightweight architecture (only 24 billion parameters), it could run on a single Nvidia RTX 4090, making it accessible for local use. With a large context window of 128k tokens and an advanced tokenizer, it handled multi-step tasks in large codebases very well. However, the AI world doesn’t stand still. Today, many new, more productive and functional agents have appeared. Among them, the following stand out: OpenHands: One of the most popular open-source frameworks today. It provides a flexible platform for creating and deploying agents, allowing developers to easily integrate different LLMs for task execution. Moatless Tools: A set of tools that expand agent capabilities, allowing them to interact with various services and APIs, making them especially effective for automating complex workflows. Refact.ai: A full-fledged open-source AI assistant focusing on refactoring, code analysis, and test writing. It offers a wide range of functions to boost developer productivity. SWE-agent and its mini version mini: Tools developed by researchers from Princeton and Stanford. SWE-agent allows LLMs, such as GPT-4o, to autonomously solve tasks in real GitHub repositories, demonstrating high efficiency. The compact mini version (just 100 lines of code) can solve 65% of tasks from the SWE-bench benchmark, making it a great choice for researchers and developers who need a simple yet powerful agent-building tool. Each of these projects contributes to the development of agent-based coding, providing developers with powerful and flexible tools. SWE-Bench: The Standard for Evaluating Agent Coding To understand how effectively these agents work, a reliable evaluation system is necessary. This role is played by SWE-Bench, which has become the de facto standard for measuring LLM capabilities in software engineering. The benchmark consists of 2,294 real GitHub issues taken from 12 popular Python repositories. To improve evaluation accuracy, SWE-Bench Verified was created—a carefully curated subset of 500 tasks. These tasks were analyzed by professional developers and divided by complexity: 196 “easy” (less than 15 minutes to fix) and 45 “hard” (over an hour). A task is considered solved if the changes proposed by the model pass all unit tests successfully. Originally, Devstral was among the leaders on SWE-Bench Verified among open-source models. For example, in May 2025, the OpenHands + Devstral Small 2505 combo successfully solved 46.8% of tasks. But the AI-agent world is evolving incredibly fast. Just three months later, in August 2025, these results don’t even make the top ten anymore. The current leader, Trae.ai, shows an impressive 75.20% of solved tasks—a clear demonstration of how quickly these technologies are progressing. Not Just Benchmarks, But Real Work At first glance, it might seem that the only important metric for an AI agent is its performance on benchmarks like SWE-Bench. And of course, impressive numbers like those of Trae.ai speak volumes. But in practice, when solving real tasks, functionality and workflow integration matter much more than raw percentages. Modern AI agents are not just code-generating models. They’ve become true multi-tool assistants, capable of: interacting with Git, running tests, analyzing logs, even creating pull requests. Still, they differ, and each has its strengths: Devstral is great for multi-step tasks in large codebases. Its lightweight design and large context window make it valuable for local workflows. OpenHands is less of an agent itself and more of a flexible platform for building and deploying agents tailored to specific needs, easily integrating different language models. Refact.ai is a full-fledged assistant focusing on analysis, refactoring, and test writing, helping developers maintain high code quality. And let’s not forget SaaS solutions that have been breaking revenue records since the start of the year: Replit, Bolt, Lovable, and others. Ultimately, the choice of an agent depends on the specific task: do you need a tool for complex multi-step changes, a flexible platform to build your own solution, or an assistant to help with refactoring? In the end, their main advantage is not just the ability to write code but their seamless integration into workflows, taking over routine and complex tasks. Launching Your Own Agent Let’s look at how to deploy one of the modern agents, OpenHands. We’ll use the Devstral model, since it remains one of the open-source models that can run on your own hardware. Preparing the GPU Server First, you will need a server. Choose a suitable configuration with a GPU (for example, NVIDIA A100) to ensure the necessary performance. After creating the server, connect to it via SSH. Installing Dependencies Update packages and install Docker, which will be used to run OpenHands. Example for Ubuntu: sudo apt update && sudo apt install docker.io -y Setting Up and Running OpenHands We’ll use a prebuilt Docker image of OpenHands to simplify deployment: docker pull docker.all-hands.dev/all-hands-ai/runtime:0.51-nikolaik docker run -it --rm --pull=always \ -e SANDBOX_RUNTIME_CONTAINER_IMAGE=docker.all-hands.dev/all-hands-ai/runtime:0.51-nikolaik \ -e LOG_ALL_EVENTS=true \ -v /var/run/docker.sock:/var/run/docker.sock \ -v ~/.openhands:/.openhands \ -p 0.0.0.0:3000:3000 \ --add-host host.docker.internal:host-gateway \ --name openhands-app \ docker.all-hands.dev/all-hands-ai/openhands:0.51 This command will launch OpenHands in a Docker container, accessible via your server’s address at port 3000. During startup, you’ll get a URL for the OpenHands web interface. The option -p 0.0.0.0:3000:3000 means OpenHands will be accessible externally. By default, the web interface does not require login or password, so use caution. Connecting to the Agent Open in your browser: https://SERVER-IP:3000 You’ll see this screen: Installing the Language Model (LLM) To function, the agent needs an LLM. OpenHands supports APIs from various providers such as OpenAI (GPT family), Anthropic (Claude family), Google Gemini, and others. But since we’re using a GPU server, the model can be run locally. The OpenHands + Devstral Small combo is still a top open-source performer on SWE-Bench, so we’ll use that model. First, install the model locally. The method depends on the service you’ll use to run it. The simplest option is via Hugging Face: huggingface-cli download mistralai/Devstral-Small-2505 --local-dir mistralai/Devstral-Small-2505 You can run the model with Ollama, vLLM, or other popular solutions. In our case, we used vLLM: vllm serve mistralai/Devstral-Small-2505 \     --host 127.0.0.1 --port 8000 \     --api-key local-llm \     --tensor-parallel-size 2 \     --served-model-name Devstral-Small-2505 \     --enable-prefix-caching Adding the Model to OpenHands In the LLM settings of OpenHands, go to “see advanced settings” and fill in: Custom model: mistralai/Devstral-Small-2505 Base URL: http://127.0.0.1:8000/v1 (depends on your service setup) API Key: local-llm (may vary by setup) The Future of Agent-Based Coding: More Than Just Autocompletion The evolution from Devstral to platforms like OpenHands shows that we are moving from simple models to full-fledged autonomous tools. LLM agents are no longer just “improved autocompletes”; they are real development assistants, capable of taking on routine and complex tasks. They can: Implement features requiring changes across dozens of files. Automatically create and run tests for new or existing code. Perform refactoring and optimization at the project-wide level. Interact with Git, automatically creating branches and pull requests. Agents like Refact.ai are already integrating into IDEs, while OpenHands enables building a full AI-driven CI/CD pipeline. The future points to a world where developers act more as architects and overseers, while routine work is automated with AI agents.

Computer performance in any segment often comes down to the speed of reading and writing data from storage devices. This is one of the main reasons for the widespread transition to SSD drives, which offer speeds at least 3 to 5 times higher than HDDs. Partly because of this, devices such as RAID arrays began to appear. They allowed building relatively fast systems, even using outdated hard drives. And this is not the only advantage of RAID technology. Its second key function is increasing the reliability of the data storage subsystem, including the ability to preserve information even in the event of a hardware failure of one of the drives. In practice, these capabilities are often combined. Consumer systems usually represent a "simple combining" of a pair of drives into a single cluster to increase speed or consolidate their capacity. What Is a RAID Array? The term RAID stands for Redundant Array of Independent Disks. The technology allows combining several storage devices into a single logical unit. Depending on the type of RAID array, the user gets improved fault tolerance, increased performance, or both. Its configuration in technical environments is called the RAID level. There are four common types (marked by numbers): RAID 0 — involves striping data across disks during reading and writing, resulting in nearly double the speed compared to a single drive. Fault tolerance does not increase; this is only about improved performance. RAID 1 — mirrors disks, doubling fault tolerance. However, it does not affect data transfer speeds. In case of a disk failure, the system remains operational, and after replacing the disk, the mirror is restored. RAID 5 — a combined option with striping for reading/writing and parity data for fault tolerance. Requires at least 3 drives. It offers higher read speeds and safety, but slightly slower write speeds. RAID 10 — a combination of RAID 0 and RAID 1. It includes a number of disks divisible by 4. The first pair of drives is striped and mirrored onto the second pair, creating a single array with high performance and fault tolerance. RAID arrays are created from either SSDs or HDDs. It is preferable to use identical models from the same manufacturer, though formally, there are no strict restrictions. For data centers or large server enterprises, these recommendations are usually followed because it is more cost-effective to buy bulk identical drives for equipping computers and creating a spare pool for technical failures. When upgrading, often the entire block is replaced to reset its lifecycle. There are two ways to create a RAID array. The first involves installing special drivers (software). In this case, it is managed by the operating system. The second involves installing a dedicated hardware controller card. Such chips have long been integrated into motherboards, even for home use, but the CPU still controls them. The optimal choice is to use external controllers that handle most functions in hardware. Types of RAID Controllers Typically, a modular RAID controller connects to a free PCI-E slot. It includes its own cache memory used for temporarily storing data being read or written. It operates under its own microcontroller, and the cards may include backup power sources (BBU, Battery Backup Unit) or flash memory with supercapacitors. Linux Software RAID On Linux, the mdadm utility can create and manage software RAID arrays of most common levels. Requires permanently connected drives (internal or always-attached). Consumes some CPU cycles, but modern CPUs handle this overhead easily for most workloads. Status and configuration are accessible via /proc/mdstat and mdadm commands. Example creation of a RAID 1 array: mdadm --create --verbose /dev/md0 --level=1 --raid-devices=2 /dev/nvme1n1 /dev/nvme2n1 The result is a single block device /dev/md0 that abstracts the underlying drives. Intel Virtual RAID on CPU (VROC) Intel VROC is a firmware-assisted NVMe RAID solution built into Intel Xeon Scalable platforms. It requires a VROC license key to unlock RAID functionality beyond RAID 0 and works with Intel Volume Management Device (VMD) technology for NVMe hot-swap capability. As of 2025, supported OS/platforms include: Windows 11, Windows Server 2022, Windows Server 2025 RHEL 7.3–9.x, SLES 12 SP3–15 SP6, Ubuntu 18.04–24.04 LTS VMware ESXi 7.0 U3 and 8.x (ESXi 9.0 planned) The RAID levels are 0, 1, 10 with standard license, while premium license also adds RAID 5 (RAID 6 not supported). Notes: Supported drive count varies by platform (commonly up to 32+ drives on modern Xeons). Arrays are created in UEFI BIOS Setup Utility under VROC/VMD menus. Linux mdadm can manage but not create VROC arrays—initial setup must be done in BIOS. Windows uses the Intel VROC GUI or CLI tools. Broadcom/LSI MegaRAID 9460-8i Now let's look at a fully hardware NVMe RAID controller—a PCI-Express x8 card supporting up to 8 drives with SAS/SATA ports.  We should note that while the 9460-8i is still supported it is considered legacy. The Broadcom MegaRAID 9600 series with PCIe 4.0 is the recommended choice for new high-performance NVMe deployments. Features of the NVMe MegaRAID 9460-8i controller: NVMe support is limited to drives connected through SAS-based U.2/U.3 backplanes or tri-mode expanders—not direct PCIe lanes like VROC. Presents RAID volumes to the OS as single logical devices (member drives are hidden). Typically configured once during initial server provisioning using the UEFI RAID BIOS, Broadcom MegaRAID Storage Manager (MSM), or storcli CLI. NVMe RAID Performance Metrics The use of NVMe (Non-Volatile Memory Express) technology is justified by the increased bandwidth provided by the standard PCIe interface. It leverages all the advantages of solid-state drives, since RAID arrays are increasingly built from SSDs. For example, the data transfer protocol operates similarly to high-performance processor architectures (parallel paths, low latency, etc.). NVMe supports up to 64,000 queues, each with a depth of 64,000 entries, whereas the outdated AHCI technology can only send 32 commands per queue. Previous-generation controllers’ drivers used long cycles with a 6-microsecond latency. NVMe uses short cycles with only 2.8 microseconds latency—a significant factor in performance improvement. The following metrics are commonly compared: IOPS (Input/Output Operations Per Second)—the number of input/output operations per second. Average and maximum latency—the host response time to operation requests. System throughput—the speed of sequential read/write. These metrics are “synthetic” because in real-world use, they rarely appear in pure form. However, they serve well for testing and comparing different controllers by running specialized programs. It is best to evaluate equipment built on similar technology since RAID 0 on SSDs is always faster than on HDDs, even without NVMe, due to hardware differences alone. Conclusion Choosing between software and hardware platforms usually boils down to some recommendations. For a RAID array made from two drives, the first option (software RAID) is sufficient. More complex systems should definitely be built on external controllers. For large arrays or mission-critical workloads, use dedicated hardware RAID or firmware-assisted RAID like Intel VROC for better performance and resilience. For new enterprise NVMe deployments, look into modern PCIe 4.0/5.0 hardware RAID controllers or direct CPU-attached solutions with VMD/VROC, avoiding older legacy cards unless required for compatibility.

After OpenAI's successful debut in 2022, AI tools rapidly entered everyday life.  When we talk about text generation, ChatGPT comes to mind. When it comes to image generation, we think of Midjourney. Then there are Gemini, DALL-E, Claude, Stable Diffusion, and many other leading models in the field. But what comes to mind when it comes to video generation? Or at least, what should come to mind? That’s exactly what we’ll discuss in this article. 1. Kling Kling is a Chinese AI video generation tool developed by Kuaishou in 2024. It is one of the best video generation AI tools on the market, ideal for marketers, bloggers, and large teams who need to produce high-quality videos quickly. Kling's standout feature is its balanced blend of cinematic aesthetics and flexible settings—you can get hyper-realistic or stylized clips. The model processes both text prompts and static images, turning them into dynamic, high-quality videos—up to 10 seconds long, FullHD resolution (1080p), and 30 FPS. Naturally, the best features are available only on paid plans. The service supports complex camera behavior for expressive angles: panning, tilting, and zooming. You can also set keyframes (start and end) to generate video in between them. There's also an "extension" function to prolong an already generated video up to 3 minutes. Additionally, the model supports lip-syncing—synchronizing mouth movement with speech. The interface is intuitive, though slightly overloaded. It’s easy to get the hang of but can occasionally be confusing.   Free Plan Paid Plans (from $3/month) Resolution up to 720p up to 1080p Duration up to 5 sec up to 10 sec Generations up to 6 per day from 18 per month Faster Generation no yes Watermarks yes no Upscaling no no Extension no up to 3 minutes Extra Features no yes Note: On the free plan, Kling allows about 10x more generations per month than the paid plan. However, those videos are shorter and lower quality. The free quota is added on top of the paid quota. 2. Hailuo AI Hailuo AI is a Chinese AI video generator developed by MiniMax in 2024. It offers a simple and flexible toolkit for creating content on the go, from marketing clips to social media stories. In just minutes, it can turn a text or static image into a high-quality, albeit short, video, significantly cutting down the time and resources needed for traditional video production. Hailuo AI focuses on quickly generating short videos (up to 6 seconds at 25 FPS) based on text descriptions or static images. The resolution maxes out at 720p. While these limitations are acceptable for fast marketing tasks, they can be a dealbreaker for serious projects. You can combine text and image inputs for more control over the video story. In addition to full camera control (angle, zoom, pan), Hailuo AI reduces random motion noise and maintains character appearance across scenes. The interface is both simple and flexible, allowing cinematic effects without a steep learning curve. It also offers an API for integration into external apps. Ideal for quick short-form videos like animated teasers and promo clips. For longer, more complex videos, you’ll need something else.   Free Plan Paid Plans (from $14/month) Resolution up to 720p up to 720p Duration up to 6 sec up to 6 sec Generations up to 90/month from 130/month Faster Generation no yes Watermarks yes no Upscaling no no Extension no up to 2 minutes Extra Features no yes Note: There’s also progressive pricing based on generation volume. From $1 for 70 credits, enough for a couple of generations. 3. Fliki Fliki is an American AI video generator created by Fliki in 2021. It’s an all-in-one platform combining various AI modules for generating presentations, audio, and video. Fliki specializes in automatically turning any text format (article, script, website URL, PDF/PPT) into a video with realistic voiceovers (2,000+ voices, 100+ dialects) and animated avatars (70+ characters). You can even clone your voice and dub videos in 80+ languages. Fliki also gives access to millions of stock images, video clips, stickers, and music for rapid video creation. Unlike services that render each frame from scratch, Fliki assembles clips, slideshows, presets, and transitions into a cohesive video. Final length can be up to 30 minutes. Runs in-browser with no downloads needed. Just enter your text, select a voice, add media, and you’ll get a professional video with voiceover and subtitles in minutes. Its broad feature set in a simple package makes it suitable for small teams and large enterprises alike. Paired with classic editing tools, Fliki’s potential is immense.   Free Plan Paid Plans (from $28/month) Resolution up to 720p up to 1080p Duration up to 5 min (8 sec scenes) up to 30 min (8 sec scenes) Generations up to 5 min/month from 180 min/month Faster Generation no yes Watermarks yes no Upscaling no no Extension no no Extra Features no yes Paid plans also unlock thousands of voices and dialects, millions of premium images, videos, sounds, and access to Fliki’s API. 4. Dream Machine Dream Machine is an American AI video generator created by Luma AI in 2024. It specializes in generating short videos from text prompts or static images, making it easy to produce dynamic clips with natural movement and cinematic composition—no editing expertise needed. Users can describe or show what they want, and Dream Machine generates fluid, natural videos. Default output is 5–10 seconds at 1080p and 24 FPS. You can adjust aspect ratio, animation style, motion intensity, and transition smoothness. Dream Machine supports keyframe-based generation (start and end image), has an intuitive minimalist interface, and offers an API for integration. It’s not suitable for long, complex videos. But for fast marketing and ad content, it’s a top pick.   Free Plan Paid Plans (from $9/month) Resolution up to 720p up to 1080p Duration up to 10 sec up to 10 sec Generations up to 30/month from 120/month Faster Generation no yes Watermarks yes no Upscaling no up to 4K Extension no up to 30 sec Extra Features no yes 5. Runway Runway is an American AI video platform developed by Runway AI in 2018. It's a full-fledged cloud platform for generating and storing high-quality cinematic media. Runway is both powerful and easy to use. It excels at quickly creating short clips, experimenting with visual styles, and automating parts of the creative process. It can generate videos with outstanding photorealism and character motion consistency. It's one of the most advanced commercial tools for video generation. You can create clips from text or images, restyle existing footage, or edit content. By default, videos are 720p, 24 FPS, and 5 or 10 seconds long. However, you can upscale to 4K and extend to 40 seconds. Runway offers several models: Gen-2, Gen-3 Alpha, Gen-3 Alpha Turbo, Gen-4. The latest (Gen-4) allows for deep control over generation: aspect ratio, camera behavior, style prompts, and more.   Free Plan Paid Plans (from $9/month) Resolution up to 720p up to 720p (4K upscale) Duration 5 or 10 sec 5 or 10 sec Generations up to 5/month from 25/month Faster Generation no yes Watermarks yes no Upscaling no up to 4K Extension no up to 20 sec Extra Features no yes Note: Paid plans include up to 100 GB of cloud storage, while free users get only 5 GB. 6. PixVerse PixVerse is a Chinese AI video generation model developed by AISphere in 2023. Thanks to a wide range of tools, PixVerse can transform text descriptions, images, and video clips into short but vivid videos — from anime and comics to 3D animation and hyperrealism. PixVerse wraps numerous generation parameters in an extremely user-friendly interface: source photos and videos, aspect ratio, camera movement, styling, transitions, sound effects, voiceover, and more. The output videos are 5 to 8 seconds long, with resolutions up to 1080p at 20 frames per second. Naturally, videos can be upscaled and extended. You can also upload an already finished video and additionally stylize it using the neural network — add visual effects, voiceover, or extend the duration. As expected in such a powerful service, an API is also available—any external app can perform automatic video generation. On the PixVerse homepage, you’ll find numerous examples of generated videos along with their original prompts. Anyone can use them as a base for their own projects or simply see the model’s capabilities in action.   Free Plan Paid Plans (from $10/month) Resolution up to 540p up to 720p Duration 5 or 8 seconds 5 or 8 seconds Generations up to 20 per month from 40 per month Faster Generation no yes Watermarks yes no Upscaling up to 4K up to 4K Extension no no Extra Features no yes 7. Genmo Genmo is another AI model for video, launched in 2022. In essence, Genmo is the simplest possible service for turning text descriptions into short video clips with minimal configuration options. As simple as you can imagine—which is both good and bad. On one hand, Genmo’s entry barrier is extremely low—even someone with no experience can create a video. On the other hand, the service is hardly suitable for complex projects due to the lack of control over generation. The neural network is based on the open-source Mochi model and has many limitations: it only uses text descriptions, and video resolution is capped at 480p with a fixed duration of 5 seconds at 30 fps. Although generated videos contain visual artifacts (flickering or shifting geometry and colors) that reveal the use of AI, they still look coherent and interesting — good enough for visualizing ideas and concepts. The user interface is extremely minimalistic—a prompt input field on the homepage followed by the best generations from the past day with their corresponding prompts. It's important to understand that AI models that don't use images or video as input require more specificity in prompts—clear descriptions of visuals, environments, and details.   Free Plan Paid Plans (from $10/month) Resolution up to 480p up to 480p Duration 5 seconds 5 seconds Generations up to 30 per month from 80 per month Faster Generation up to 2 per day from 8 per day Watermarks yes no Upscaling no no Extension no up to 12 seconds Extra Features no yes 8. Sora Sora is a neural network created by OpenAI in 2024. Based on detailed text descriptions, Sora can generate images and videos with the highest level of detail. It’s a model whose output can easily be mistaken for real photos or videos. It’s significant that Sora was developed by OpenAI, a global leader in generative AI and the company behind ChatGPT and DALL·E. Sora’s interface follows the design system used across OpenAI products—sleek black theme and minimal elements. A small sidebar is on the left, a grid of popular user-generated content in the center, and a prompt field with configuration options at the bottom. Sora-generated videos have photo-realistic detail, whether hyperrealistic or animated, almost nothing gives away the AI origin. The quality and imagination in the visuals are astounding. The videos can be up to 20 seconds long, 1080p resolution, and 30 fps—significantly more than most competitors. Sora unifies all video configuration into the prompt itself—the real power of the model lies in the quality of your description. The better the prompt, the better the result. Thus, generating video with Sora becomes a constant game of tweaking prompts, words, and phrasing. Sora can definitely be considered one of the most advanced AI models for generating images and video.   Free Plan Paid Plans (from $20/month) Resolution – up to 1080p Duration – up to 20 seconds Generations – from 50 per month Faster Generation – yes Watermarks – no Upscaling – no Extension – no Extra Features – yes The free plan in Sora does not allow video generation at all—only image generation, limited to 3 per day. 9. Pika Pika is another AI-powered video creation service, launched in 2023. The platform is easy to use and designed for everyday users who are not experts in video editing or neural networks. Its primary use case is modifying existing video footage: adding transitions, virtual characters, changing a person’s appearance, and more. Still, Pika can also generate videos from scratch. Pika’s features are standard for AI video services: generation from text, from images, or between two frames (start and end). Maximum resolution is 1080p. Frame rate is 24 fps. Video duration is up to 10 seconds. Styles can vary—from cartoony to cinematic. In short, Pika is a simple and convenient tool for quickly creating videos from text or images without powerful hardware. It’s especially useful for prototyping, social media, marketing, and advertising.   Free Plan Paid Plans (from $10/month) Resolution up to 1080p up to 1080p Duration up to 10 seconds up to 10 seconds Generations up to 16 per month from 70 per month Faster Generation no yes Watermarks yes no Upscaling no no Extension no no Extra Features no yes Pika’s free plan has generation limits—you can create videos, but in small quantities. The standard paid plan increases your generation limits and unlocks newer model versions, but does not remove watermarks. The professional plan removes all limitations, provides access to advanced tools, speeds up generation, and removes watermarks from final videos. 10. Veo Veo is a video generation model developed in 2024 by DeepMind, a Google-owned company. There are several ways to access the model: Via Google Labs tools — VideoFX or VertexAI Through Google AI Studio Veo can be considered a full-fledged tool for creating high-quality, hyperrealistic clips indistinguishable from real footage. Of course, it also supports animation. Veo generates videos at 720p resolution, 24 fps, and up to 8 seconds long. In private developer previews, 1080p resolution and 4K upscaling are available—but not yet public. It accepts both text prompts and still images as input. For the latter, the neural network preserves the original composition and color palette. Most importantly, Veo supports various cinematic effects: time-lapse, panorama, slow-mo, and many more—with flexible parameter control. Veo ensures excellent consistency, stability, and smooth motion. Every video generated includes a SynthID digital watermark, invisible to the human eye or ear—a tool developed by Google to help detect AI-generated media. Thus, any image, video, or audio can be scanned using SynthID to verify AI generation. Veo also pays attention to small details—hair movement, fabric fluttering, atmospheric behavior, and more. As they say, the devil is in the details.   Free Plan Paid Plans Resolution up to 720p up to 720p Duration up to 8 seconds up to 8 seconds Generations up to 30 per month from 50 per month Faster Generation no yes Watermarks yes no Upscaling no no Extension no no Extra Features no yes Like most Google cloud services, Veo uses pay-as-you-go pricing—$0.50 per second or $30 per minute of generated video. So, a standard 10-second clip will cost $5—cheap for professionals, pricey for casual users. 11. Vidu Vidu is a Chinese AI model developed in 2024 by ShengShu AI in collaboration with Tsinghua University.  Vidu generates smooth, dynamic, and cohesive video clips, both realistic and animated. It can also add AI-generated audio tracks to videos. Vidu can accurately simulate the physical world, creating videos with developed characters, seamless transitions, and logical event chronology. The platform offers three main tools: generation from text, from images, and from videos. Additional tools include an AI voiceover generator and a collection of templates. Maximum video resolution is 1080p. Max duration is 8 seconds. Frame rate is up to 24 fps. The model is based on a "Universal Vision Transformer" (U-ViT) architecture, which processes text, image, and video inputs simultaneously to create coherent video sequences. This ensures object consistency throughout the video. For professionals and studios, Vidu is a powerful tool with great potential; for beginners, it’s an easy gateway into generative video.   Free Plan Paid Plans (from $8/month) Resolution up to 1080p up to 1080p Duration up to 8 seconds up to 8 seconds Generations up to 40 per month unlimited Faster Generation no yes Watermarks yes no Upscaling no no Extension no up to 16 seconds Extra Features no yes Which AI to choose? The vast majority of AI video generation services have similar video parameters: resolution from 720p to 1080p, durations of 5 to 10 seconds, and frame rates around 24 fps. Almost all can generate video based on text prompts, images, or video inputs. Differences in output results are usually minor—video styles and presence of visual artifacts revealing the AI.  The choice largely depends on your input and goals: text descriptions, images, or existing video. Some AI models offer higher detail than others. Always check the sample videos shown on service homepages. And keep in mind: video is a much more complex data format than text. Unlike LLMs, completely free AI video generation tools don’t exist as training the models and powering generation requires significant resources. That said, most services offer a low-tier paid plan that removes major limitations. Name Max Duration Max Resolution Max FPS Starting Price Kling 10 seconds 1080p 30 fps $3/month Hailuo AI 6 seconds 720p 25 fps $14/month Fliki 30 minutes 1080p 30 fps $28/month Dream Machine 10 seconds 1080p 24 fps $9/month Runway 10 seconds 720p 24 fps $15/month PixVerse 8 seconds 1080p 20 fps $10/month Genmo 5 seconds 480p 30 fps $10/month Sora 20 seconds 1080p 30 fps $20/month Pika 10 seconds 1080p 24 fps $10/month Veo 8 seconds 720p 24 fps $0.50/sec Vidu 8 seconds 1080p 24 fps $8/month