# AI Visualizations vs. Classic Rendering: Differences, Time, Costs and Drawbacks > Rendering really calculates physics, AI predicts it. See how AI visualizations differ from V-Ray and what their advantages and drawbacks are. Source: https://www.raya.design/en/articles/ai-visualizations-vs-classic-rendering-differences-time-costs-and-drawbacks Published: 2026-10-24 Updated: 2026-10-24 Author: Piotr Kurpiewski, Architekt, Grafik Category: Tutorial Language: en ![AI visualization of a modern single-family house with a gable roof](https://pub-55300e22613247cdaa083cb8965745af.r2.dev/articles/cbfff6f1-0469-4b48-b750-3777b184b134/wizualizacje-ai-a-klasyczny-rendering-roznice-czas-koszty-i-wady00.jpg) --- **Classic rendering in V-Ray or Corona calculates light according to the laws of physics**, so the result depends on the settings, and a single image usually takes 15-30 minutes. **An AI visualization calculates nothing**: an artificial intelligence model predicts what a photo of your scene would look like, based on millions of images it saw during training. A 2K render is ready in 20-40 seconds and needs no settings, but every generation comes out a little different. **Key takeaways** - **Classic rendering calculates light, AI models predict it** — V-Ray and Corona simulate physics according to your settings, while an AI model recreates photorealism from knowledge gained on millions of photos. - **An AI visualization is ready in well under a minute - no waiting for many minutes or hours** — A classic render usually takes 15-30 minutes plus hours of scene setup, while a 2K AI visualization is ready in 20-40 seconds. - **The same AI generation comes out different every time** — Probabilism is a feature of this technology. You keep a series consistent with the “Match style” feature and fix individual mistakes in the “Edit” tab. - **Control in AI is always control through choice** — Instead of two hours of V-Ray settings, you make three generations in five minutes and pick the best one. - **In many projects, AI is good enough** — Print-ready 4K and a full commercial license on every plan. Rendering still wins where every parameter has to be exact. ## How does an AI visualization differ from classic rendering? The difference starts with how the image is created. **Classic rendering computes the image from physics**: the engine traces the path of light from lamps and the sun, bounces it off the walls and checks how much of it reaches the camera. **An AI visualization predicts the image based on probability**: the model looks at the view from your 3D program and answers the question of what a photo of this interior would most likely look like. Everything else follows from this one difference: time and cost, but also the drawbacks I write about later in this article. You don't need to know either technology from the inside. It's enough to understand what to expect from it. The quickest way to see this difference is on your own project. A new Raya account gets **20 free credits**, with no card required, so your first test costs you nothing. [Try Raya for free](https://www.raya.design/en/login) ### How rendering works in V-Ray and Corona: simulating the physics of light V-Ray and Corona are **rendering engines**, programs that turn a 3D model into an image by calculating how light behaves. Before they calculate anything, someone has to set up the light sources, an HDRI map (a panoramic photo of the sky that lights the scene), PBR materials (a set of textures describing how a surface reflects light) and the parameters of the render itself. The engine has no idea what a beautiful interior should look like. It does exactly what it's told, with mathematical precision. A lamp that's too weak gives you a dark room, and poorly chosen reflections turn an oak floor into a uniform plastic surface. In return you get something very valuable: **the same settings always produce the same image**. The price is time. You have to know how to set every parameter, and a single render (one frame) usually takes 15-30 minutes, while large, complex scenes can take even several hours. ### How AI visualization works: probability instead of physics An AI image generator works in a completely different way. During training, the model saw huge collections of photos and learned what light coming through a window usually looks like, how a lacquered countertop shines and how a shadow falls under a sofa. When it gets a raw view from SketchUp, **it fills in the missing features of photorealism**: it builds the lighting, recreates the texture of the materials and conveys the depth of the space. ![Design of a modern barn-style single-family house in SketchUp](https://pub-55300e22613247cdaa083cb8965745af.r2.dev/articles/cbfff6f1-0469-4b48-b750-3777b184b134/wizualizacje-ai-a-klasyczny-rendering-roznice-czas-koszty-i-wady01.jpg) *A simple SketchUp scene (no additional V-Ray settings)* *The image on the left produced the image on the right* ![AI visualization of a modern barn-style single-family house](https://pub-55300e22613247cdaa083cb8965745af.r2.dev/articles/cbfff6f1-0469-4b48-b750-3777b184b134/wizualizacje-ai-a-klasyczny-rendering-roznice-czas-koszty-i-wady02.jpg) *AI adds photorealistic features to a plain 3D view* The simplest comparison is the work of two people. Rendering is like an engineer in a lab who measures every ray of light and records the result in a table. AI is like a hyperrealist painter who spent years looking at thousands of interiors and paints from memory. The painter doesn't know the formula for light reflecting off an oak floor, but knows what such a floor looks like at sunset. He paints it convincingly, just a little differently every time. Technically, most modern models start from an image filled with random noise and, step by step, turn it into an image that matches the input. In diffusion models this resembles gradual denoising, while newer models based on so-called **flow matching** take a shorter, more direct path from noise to image. From a designer's point of view, one conclusion matters: **since the starting point is random, every AI generation comes out a little different**. ### How does AI know what a photorealistic photo looks like? I've been asking myself this since my very first tests. Google and OpenAI keep the details of their training strictly secret, so what follows is my opinion, not a confirmed fact. I don't think anyone taught these models photorealism directly, with rules like “this is how light reflects off lacquer”. In my view, this ability emerged on its own, as a side effect of looking at millions of photos. The model “understood” how a photograph differs from a drawing, and intuitively moves toward a photograph whenever it gets the chance. That's why a simple view from SketchUp is enough for reflections, soft shadow transitions and depth to appear in the visualization, even though none of them existed in the model. This same intuition has a flip side, though. A model that fills in missing features sometimes fills in too much, and that's how hallucinations happen. The history of these tools shows it best. ## The history of AI visualization: from DALL-E to Nano Banana Pro Hallucinations are a much smaller problem today than just a few years ago. The first image generator the wider world heard about was **DALL-E from OpenAI**, long before ChatGPT launched. It was impressive as a curiosity, and I remember exploring its potential for creating textures together with my students, but it was no use at all for architectural visualization. ### Is Stable Diffusion suitable for making visualizations? In August 2022 came **Stable Diffusion**, an open-weights model you could run on your own computer. That was the moment architects started testing AI on their projects en masse. Me too. It quickly turned out that the model struggled with what matters most in architecture: straight lines, right angles and proportions. Instead of furniture, it produced something more like a dreamlike idea of furniture. Even so, there were plenty of uses where it worked brilliantly! ![A SketchUp view and control layers (so-called ControlNet), which were essential for Stable Diffusion to understand the concept of space](https://pub-55300e22613247cdaa083cb8965745af.r2.dev/articles/cbfff6f1-0469-4b48-b750-3777b184b134/wizualizacje-ai-a-klasyczny-rendering-roznice-czas-koszty-i-wady03.jpg) To make the result resemble the design at all, you had to reach for **ControlNet**, additional control layers that pinned the generation to the geometry. For the kitchen below, I prepared two such layers: a segmentation map, where each material has its own color, and a drawing of the edges alone. This set produced a visualization that looks like a photo only at first glance. Up close, you can see that Stable Diffusion covered the entire ceiling with brick, turned the black oven into a wooden cabinet with a TV, and the floor panels into tiles. The round table with chairs disappeared, and an armchair with a footstool took its place. It looks like a frame from a dream. ![Kitchenette and living room scene in SketchUp](https://pub-55300e22613247cdaa083cb8965745af.r2.dev/articles/cbfff6f1-0469-4b48-b750-3777b184b134/wizualizacje-ai-a-klasyczny-rendering-roznice-czas-koszty-i-wady04.png?v=1792793524431) ![Living room with kitchen generated in Raya from a SketchUp view](https://pub-55300e22613247cdaa083cb8965745af.r2.dev/articles/cbfff6f1-0469-4b48-b750-3777b184b134/wizualizacje-ai-a-klasyczny-rendering-roznice-czas-koszty-i-wady06.jpg) *Good example — AI visualization made in Raya - note how closely it matches the original* ![Kitchenette and living room scene in SketchUp](https://pub-55300e22613247cdaa083cb8965745af.r2.dev/articles/cbfff6f1-0469-4b48-b750-3777b184b134/wizualizacje-ai-a-klasyczny-rendering-roznice-czas-koszty-i-wady04.png?v=1792793490591) ![Living room with kitchen generated with Stable Diffusion from a SketchUp view](https://pub-55300e22613247cdaa083cb8965745af.r2.dev/articles/cbfff6f1-0469-4b48-b750-3777b184b134/wizualizacje-ai-a-klasyczny-rendering-roznice-czas-koszty-i-wady05.jpg?v=1792793445787) *Bad example — AI visualization made with Stable Diffusion - a hallucination far from the original* The same scene generated in Raya today sticks to the design. The distinctive block-and-beam ceiling and steel beams stayed, along with the track lights with spotlights, the arched window lintels, the sofa, the round table and the black oven with three knobs. Instead, AI added what the model was missing: tenement houses outside the window, warm light under the shelf and plaster texture on the walls. No control layers and no manual map preparation. ### When did AI visualizations become fit for commercial work? The breakthrough came with the **FLUX** model family from Black Forest Labs, based on so-called flow matching. Especially **FLUX.1 Kontext**, which learned to edit an image in context: to understand what's in the input and change only what you ask for. For the first time, a view from a 3D program could be turned into a visualization that held the geometry well enough to show to a client. Then came Google's **Nano Banana** models and GPT Image from OpenAI. Still, I consider the real beginning of the AI visualization era to be the launch of **Nano Banana Pro** in November 2025. From that point on, models began to faithfully follow the input geometry, and small details come out more and more reliably. Raya is the next stage of this revolution. We use our own solutions, and we built the interface with designers in mind. Instead of writing instructions, you choose the scene type, a lighting preset and the quality. You don't have to choose any models, let alone follow every release from Google or other industry giants. ## V-Ray or AI? Comparing visualizations of the same interior History shows the direction, but a comparison on a single project says the most. Below is the same SketchUp view of a living room: rendered in V-Ray and generated in Raya. ![View of a living room in SketchUp](https://pub-55300e22613247cdaa083cb8965745af.r2.dev/articles/cbfff6f1-0469-4b48-b750-3777b184b134/wizualizacje-ai-a-klasyczny-rendering-roznice-czas-koszty-i-wady07.png?v=1792793599562) ![Classic visualization made with V-Ray and SketchUp](https://pub-55300e22613247cdaa083cb8965745af.r2.dev/articles/cbfff6f1-0469-4b48-b750-3777b184b134/wizualizacje-ai-a-klasyczny-rendering-roznice-czas-koszty-i-wady08.png) *This is a classic visualization made in SketchUp with the V-Ray rendering engine* ![View of a living room in SketchUp](https://pub-55300e22613247cdaa083cb8965745af.r2.dev/articles/cbfff6f1-0469-4b48-b750-3777b184b134/wizualizacje-ai-a-klasyczny-rendering-roznice-czas-koszty-i-wady07.png?v=1792793589771) ![AI visualization generated in Raya from a SketchUp view](https://pub-55300e22613247cdaa083cb8965745af.r2.dev/articles/cbfff6f1-0469-4b48-b750-3777b184b134/wizualizacje-ai-a-klasyczny-rendering-roznice-czas-koszty-i-wady09.jpg) *This is an AI visualization generated in Raya from a simple SketchUp view (in 30 seconds)* V-Ray rendered the scene exactly as I set it up: soft, even light, correct materials and a clean, catalog-style image. Behind that result, however, are configured lights, materials and render parameters. And, in the end, a long wait for the result. Raya got a raw view and added what wasn't in the model: streaks of sunlight on the rug, the warm glow of a lamp on the walnut paneling and reflections on the lacquered burl veneer. The geometry stayed in place, including the Wassily chair and the marble fireplace. Which image is better? That's a matter of taste. A client can comfortably make a decision based on either of them. What differs is the path to get there: in V-Ray, it's setting up scenes and waiting for the render; in Raya, **a minute or two for the 4K version**. ## How long does rendering take compared to AI visualization? A table of differences Comparing one interior is just an example, while the table below sums up the differences systematically. **A single classic render usually takes 15-30 minutes**, and large scenes can mean hours of waiting, while **a 2K AI visualization is created in Raya in about 20-40 seconds**. | | Classic rendering (V-Ray, Corona) | AI visualization (Raya) | | --- | --- | --- | | How it works | simulating the physics of light | predicting the image based on training data | | Waiting for the image | 15-30 minutes, over an hour in large scenes | 2K approx. 20-40 s, 4K approx. 1-2 min | | Scene preparation | lights, HDRI, PBR materials, render parameters | a ready-made lighting preset and a short description of the materials | | Hardware | a powerful, expensive computer | a browser, it even works on a phone | | Tool cost | a V-Ray license costs about $500 net per year | plans from as little as $5.99 per month | | Repeatability | the same settings produce the same image | every generation comes out slightly different | | Control | every parameter set and fully controlled | contextual editing, fixes to selected areas | | Barrier to entry | months or even years of learning | your first visualization in a few minutes, with no experience | In practice, you'll feel the difference most in scene preparation, not in the calculation itself. You can leave a V-Ray render running overnight, but nobody will set up the lights for you. There's also the hardware: classic rendering ties up your computer while it calculates. Raya's AI visualizations are calculated on Raya's servers, so you can work on another project in the meantime, and the system will notify you when the result is ready. ## What are the drawbacks of AI visualization? The table looks one-sided, so it's time for the other side of the coin. AI visualizations have four real drawbacks: **lack of repeatability, geometry hallucinations, no precise parameter control and generative compression**. Each of them can be addressed, though. ### Why does the same AI generation give a different result every time? In V-Ray, the same file with the same settings gives the same image even after a hundred renders. With AI, it's not that simple. The same operation repeated ten times gives ten different results, and roughly one generation in ten may misunderstand the guidelines. It's a feature of the whole technology, not of one tool. Below I show it using Google Flow as an example. ![View of a single-family house in SketchUp](https://pub-55300e22613247cdaa083cb8965745af.r2.dev/articles/cbfff6f1-0469-4b48-b750-3777b184b134/wizualizacje-ai-a-klasyczny-rendering-roznice-czas-koszty-i-wady10a.jpg) *The base SketchUp view used to generate the AI visualizations* *The image on the left produced the image on the right* ![AI visualizations of the same single-family house made in the Google Flow interface](https://pub-55300e22613247cdaa083cb8965745af.r2.dev/articles/cbfff6f1-0469-4b48-b750-3777b184b134/wizualizacje-ai-a-klasyczny-rendering-roznice-czas-koszty-i-wady10b.jpg) *4 AI visualizations using the same settings and the same instruction give slightly different results* I generated the same SketchUp view of the house four times. In the first version, the paths were paved with cobblestones and the pergola has gray, weathered wood. In the second, hills and a spreading tree in the corner of the frame appeared behind the house. In the third, the cobblestones turned into concrete slabs set in the grass. In the fourth, garden furniture that wasn't in the model appeared on the terrace, and the shrubs came out as flat as cutouts, because the AI took SketchUp's two-dimensional plants literally. With a single image, that's a minor issue. When you present several rooms of one apartment, lack of repeatability is a problem, because each view may come out in a different mood. That's why Raya's “Generate” tab has **the “Match style” feature**. You generate a base frame, set it as the reference, and the following views get the same light, materials and atmosphere. It's best to always use the same reference rather than a chain in which each new result becomes the new reference. ### Geometry hallucinations: when AI changes your design The second problem is more serious. A model that fills in missing features can also fill in geometry. General-purpose tools like ChatGPT can change proportions and move windows, and the kitchen from your design may come out a meter wider. I show what this looks like in practice in a [comparison of free AI visualization tools](https://www.raya.design/en/articles/free-ai-visualizations-catch). Raya sticks to the uploaded geometry, but it's still subject to the rules of probabilism. You'll eliminate most mistakes before you even generate, because **the quality of the input is about 80% of the quality of the result**. Before you send a view, check [how to prepare a model in SketchUp for AI visualizations](https://www.raya.design/en/articles/how-to-prepare-a-model-in-sketchup-for-ai-visualizations-framing-edges-shadows-and-export). When something on a finished visualization doesn't match, you don't have to generate everything from scratch. In Raya's “Edit” tab, **the “Select area” feature** limits the change to the area you mark, and **the “Reference objects” feature** lets you show the AI a photo of a specific piece of furniture instead of describing it. I've laid it out step by step in a guide on [how to edit AI visualizations](https://www.raya.design/en/articles/how-to-edit-ai-visualizations-see-how-to-fix-a-render-with-artificial-intelligence). [Try editing for free](https://www.raya.design/en/edit) ### Can you set the lighting precisely in an AI visualization? Not in the sense you can in V-Ray. In AI you won't enter a color temperature of 3000K or a lamp's output in lumens, and you won't move the sun by two degrees (although you can try). You steer the image with presets and short descriptions, and the model interprets them its own way. Sounds like a big minus, but let's do the math. If control matters to you, in Raya you can make **three different generations in five minutes** and pick the one that best captures your idea. The alternative is two hours of setting up lights in V-Ray before you see the first decent result. In practice, control through choice turns out to be faster than control through parameters! In Simple mode, you choose one of Raya's lighting presets: “Sunny”, “Cloudy”, “Golden hour”, “Dawn”, “Night” or “No windows”. In Advanced mode, you describe the light in your own words, for example “soft warm light from the left”, and you can specify a material with an uploaded texture and the spot where it should go. ### Generative compression: how many edits your visualization can take The last drawback only shows up when you make edits. **Each further pass of the same image through AI lowers its quality a little.** I like to compare it to a meat grinder: the more times you run an image through it, the less of its original structure remains. ![AI visualization of a dining room before editing](https://pub-55300e22613247cdaa083cb8965745af.r2.dev/articles/cbfff6f1-0469-4b48-b750-3777b184b134/wizualizacje-ai-a-klasyczny-rendering-roznice-czas-koszty-i-wady11.jpg) *Visualization before editing* *The image on the left produced the image on the right* ![The same AI visualization after multiple edits](https://pub-55300e22613247cdaa083cb8965745af.r2.dev/articles/cbfff6f1-0469-4b48-b750-3777b184b134/wizualizacje-ai-a-klasyczny-rendering-roznice-czas-koszty-i-wady12.jpg) *Repeated editing inevitably compresses the image* The practical takeaway: gather the client's comments and apply them in one edit with three changes, rather than in three separate ones. I show how many edits in a row are safe and what an image looks like after too many fixes in the [guide to editing AI visualizations](https://www.raya.design/en/articles/how-to-edit-ai-visualizations-see-how-to-fix-a-render-with-artificial-intelligence). ## Will AI visualizations replace classic rendering? Knowing the drawbacks, it's easier to answer the question I hear most often. **In many commercial projects, AI visualization is already enough today, not just for the concept but for the final images too.** Raya generates images in 4K, ready for presentation and print, and every one of them comes with a full commercial license, on every plan, including free credits. The concept stage is AI's natural habitat. The client wants to see three color variants of the kitchen? In a classic workflow, that's three renders and several hours. In Raya, it's 6 credits and a few minutes. Incidentally, for projects under NDA it also matters that Raya doesn't train AI models on uploaded materials or on generated visualizations. ![A 3ds Max render made with the Corona render engine, requiring hours of setting up lighting, materials and rendering.](https://pub-55300e22613247cdaa083cb8965745af.r2.dev/articles/cbfff6f1-0469-4b48-b750-3777b184b134/wizualizacje-ai-a-klasyczny-rendering-roznice-czas-koszty-i-wady-20.jpg) *A 3ds Max render made with the Corona render engine, requiring hours of setting up lighting, materials and rendering.* ![AI visualization made with Raya.design in 30 seconds from a 3ds Max model.](https://pub-55300e22613247cdaa083cb8965745af.r2.dev/articles/cbfff6f1-0469-4b48-b750-3777b184b134/wizualizacje-ai-a-klasyczny-rendering-roznice-czas-koszty-i-wady-21.jpg?v=1793039451892) *Visualization made with Raya.design in 30 seconds.* Classic rendering isn't going away. It stays where every parameter has to match exactly: when simulating specific light fixtures or in long series of shots that must have identical lighting. V-Ray and Corona skills are still valuable. People who have them will be the first to appreciate how much time AI saves at the stage where precision isn't that critical. For someone just starting out, the conclusion is simple: you don't need months of learning to render to show a client a photorealistic design. ## How to start creating AI visualizations without knowing rendering? Since AI visualization doesn't require knowledge of light and PBR materials, getting started comes down to a few steps. You'll make your first visualization in Raya in your browser, with no software to install and no powerful computer: ![Step 1: exporting a SketchUp view of a house to a JPG or PNG file up to 10 MB](https://pub-55300e22613247cdaa083cb8965745af.r2.dev/articles/cbfff6f1-0469-4b48-b750-3777b184b134/wizualizacje-ai-a-klasyczny-rendering-roznice-czas-koszty-i-wady-22.jpg) *Export a view from your 3D program, e.g. SketchUp, Revit, ArchiCAD or Blender, as a JPG or PNG up to 10 MB.* *Next step* ![Step 2: uploading the view to Raya, choosing the scene type, a lighting preset and 2K quality](https://pub-55300e22613247cdaa083cb8965745af.r2.dev/articles/cbfff6f1-0469-4b48-b750-3777b184b134/wizualizacje-ai-a-klasyczny-rendering-roznice-czas-koszty-i-wady-23.jpg?v=1793040321512) *Upload it in the “Generate” tab and choose the scene type (Interior or Architecture), a lighting preset and 2K quality.* ![Step 3: fixing a selected area of an AI visualization in the Edit tab](https://pub-55300e22613247cdaa083cb8965745af.r2.dev/articles/cbfff6f1-0469-4b48-b750-3777b184b134/wizualizacje-ai-a-klasyczny-rendering-roznice-czas-koszty-i-wady-24.jpg?v=1793040321512) *Fix selected areas in the “Edit” tab.* *Next step* ![Step 4: upscaling an AI visualization from 2K to 4K for 4 credits](https://pub-55300e22613247cdaa083cb8965745af.r2.dev/articles/cbfff6f1-0469-4b48-b750-3777b184b134/wizualizacje-ai-a-klasyczny-rendering-roznice-czas-koszty-i-wady-25.jpg?v=1793040321512) *Upscale the approved version to 4K for 4 credits.* If you work in ArchiCAD, Revit, 3ds Max, pCon.planner, Rhino or Blender, you can skip the manual export. [The free Raya plugin](https://www.raya.design/en/plugins) sends the current view straight to the generator, without leaving the program. While preparing this article, I went back to my old Stable Diffusion tests. Only a few years separate a ceiling made entirely of brick and “dreamlike” furniture from a visualization I'd show a client without hesitation. If you want to see how AI handles your project, upload a view you know well and compare the result with your render. A new account gets **20 free credits valid for 14 days**, with no card required, which is about 10 2K visualizations. That's more than enough to find out whether AI will really speed up your work. [Generate your first visualization for free](https://www.raya.design/en/generate)