RunwayML

Overview

RunwayML is an AI media generation platform established in 2018, primarily focused on tools for video creation, editing, and visual effects. The platform provides a suite of generative AI models designed for artists, designers, and content creators to produce and manipulate visual media. Its core offerings include models like Gen-1 and Gen-2, which enable users to generate video content from various inputs, including text prompts, reference images, or existing video footage. RunwayML positions itself as a comprehensive solution for creative professionals seeking to integrate AI into their video production workflows.

The platform is designed with a web-based interface, emphasizing accessibility for users without extensive technical or coding backgrounds. This approach aims to streamline complex generative AI processes, making them manageable through a graphical user interface. Key applications for RunwayML include generating stylistic transfers on video (Gen-1), creating entirely new video clips from text or images (Gen-2), and performing advanced video editing tasks such as object removal or inpainting. For instance, Gen-2 can translate text descriptions like "a robot walking in a futuristic city" directly into video, or apply the style of a reference image to an existing video clip. This enables rapid prototyping of visual concepts and the creation of unique effects.

RunwayML's feature set extends beyond video generation to include AI-powered image editing and generation capabilities. Tools such as Text to Image and Image to Image allow users to create still images from prompts or transform existing images. The Erase and Replace feature leverages AI to remove unwanted objects from video frames and intelligently fill the void, simplifying post-production tasks that traditionally require manual rotoscoping and compositing. The platform also offers Custom AI Training, which allows users to fine-tune generative models on their own datasets, enabling the creation of highly specific visual styles or subjects.

Target users for RunwayML include video editors, animators, graphic designers, filmmakers, and marketing professionals. The platform's utility spans creative content production, experimental visual effects design, and rapid prototyping of visual concepts. While it focuses on a user-friendly creative workflow through its web interface, direct API access for developers is not the primary offering. This focus on a graphical user experience distinguishes it from platforms that are primarily API-driven. For developers interested in integrating similar generative video capabilities programmatically, alternatives like Stability AI's Stable Video Diffusion offer models that can be deployed and controlled via code.

Key features

• Gen-1: Video to Video – Transforms existing video content by applying the style of an image or text prompt. This allows for stylistic transfers, making a video look like a painting or adopting a specific aesthetic based on textual descriptions.
• Gen-2: Text to Video & Image to Video – Generates new video clips from text descriptions, reference images, or a combination of both. Users can describe a scene and have Gen-2 create a corresponding video, or provide an image to animate into a short sequence.
• Text to Image – Creates still images from textual prompts, enabling users to visualize concepts before moving to video.
• Image to Image – Transforms an existing image into a new image based on a text prompt or another image's style, providing tools for visual concept development.
• Erase and Replace – An AI-powered tool that allows users to remove objects from videos and intelligently fill the background. This feature automates tasks such as removing unwanted elements or actors from a scene.
• Custom AI Training – Enables users to train generative models on their own datasets, allowing for the creation of unique characters, objects, or styles tailored to specific projects. This is particularly useful for maintaining brand consistency or developing proprietary visual assets.
• Frame Interpolation – Increases the frame rate of video footage by generating intermediate frames, resulting in smoother motion.
• Inpainting/Outpainting – AI-driven tools for filling in missing parts of an image or video frame (inpainting) or extending an image/video beyond its original borders (outpainting).
• Motion Tracking – Automatically tracks the movement of objects or subjects within a video, useful for applying effects or text that follow specific elements.
• Green Screen / Chroma Key – Provides AI-assisted tools for isolating subjects from their backgrounds, simplifying traditional chroma keying processes.

Pricing

RunwayML offers a free plan with limited credits and features, suitable for evaluation. Paid plans provide increased credits, higher resolution outputs, and access to more advanced features. Pricing is subject to change; the table below reflects information as of May 2026. For the most current details, refer to the RunwayML pricing page.

Common integrations

RunwayML operates primarily as a standalone web application, providing a self-contained environment for creative workflows. While it does not offer a public-facing API for direct programmatic integrations, its output can be easily integrated into standard video editing and compositing software. Users typically export generated video and image files for further processing in professional tools.

• Video Editing Software: Exported video clips from RunwayML can be imported into non-linear editing systems (NLEs) such as Adobe Premiere Pro, DaVinci Resolve, or Final Cut Pro for further assembly, sound design, and color grading.
• Compositing Software: Generated visual effects or isolated elements can be brought into compositing applications like Adobe After Effects or Nuke for advanced layering, motion graphics, and visual effects pipelines.
• Image Editing Software: Images generated or modified within RunwayML can be refined in applications like Adobe Photoshop or GIMP for detailed touch-ups or graphic design elements.

Alternatives

• Pika Labs: Offers text-to-video and image-to-video generation, often cited for its creative output and active community.
• Stability AI (Stable Video Diffusion): Provides open-source models for video generation, offering developers greater control and customization for local or custom deployments.
• HeyGen: Specializes in AI video generation for corporate and marketing content, focusing on AI avatars and voiceovers for business communication.
• Midjourney: Primarily an image generation tool, but can be used to create image sequences that can then be animated into video using other tools.
• DeepMind's Phenaki: While not a commercial product, academic research like Google DeepMind's work on Phenaki demonstrates alternative approaches to generating long, coherent videos from text prompts.

Getting started

RunwayML is primarily a web-based platform with a graphical user interface, so there is no direct API or code-based SDK for developers to use for generative tasks. Getting started involves creating an account and using the web interface. This example outlines the typical workflow for generating a video using Gen-2 from a text prompt within the RunwayML application.

Step 1: Create an Account and Access the Web Application

Navigate to the RunwayML homepage and sign up for a free account or log in. Once logged in, you will be directed to the main workspace.

Step 2: Start a New Gen-2 Project

From the main dashboard, locate and click on the "Gen-2" option. This will open the Gen-2 interface where you can input your desired parameters for video generation.

Step 3: Input Your Text Prompt

In the Gen-2 interface, you will find a text input field labeled "Describe what you want to generate." Enter a descriptive prompt for your video. For example:

A futuristic cityscape at sunset with flying cars, highly detailed, cinematic, 8k.

Step 4: Configure Generation Settings (Optional)

RunwayML provides various optional settings to refine your video generation, such as:

• Seed: A numeric value that influences the randomness and reproducibility of the generation. Keeping the same seed can help generate similar results across multiple attempts.
• Motion: Adjust the amount of motion the AI should introduce into the generated video.
• Style Reference: Upload an image to guide the visual style of the output video.
• Negative Prompt: Describe what you *don't* want to see in the video to prevent undesirable elements.

Step 5: Generate the Video

Once your prompt is entered and settings are configured, click the "Generate" button. The platform will then process your request using the Gen-2 model. Generation time can vary based on the complexity of the prompt and current server load.

Step 6: Review and Export

After the video is generated, it will appear in your workspace. You can preview the video, and if satisfied, download it in various formats (e.g., MP4). If the result isn't exactly what you intended, you can modify your prompt or settings and generate again, consuming additional credits.

This process demonstrates the user-centric design of RunwayML, aiming to provide creative control through an intuitive visual interface rather than requiring direct code interaction.