Haiper

Haiper is a generative video platform that creates short video clips from text prompts or from still images, and provides editing tools to refine the result. It was founded in London by researchers who had worked on perceptual and generative models at major AI laboratories, and it launched publicly in early 2024 during the period when short-form generative video moved from research demos to usable products. The audience is marketers, social media teams, independent creators and product developers who need video assets quickly and cannot justify a shoot, a motion graphics studio or a licensing budget for every clip.

The core capability is text-to-video. You describe a scene, a subject, a camera movement and a style, and the model generates a clip of a few seconds. Prompt quality matters more than in image generation, because temporal coherence is harder: the model has to keep an object consistent across frames while it moves, and has to interpret camera direction such as a slow push-in, an orbit or a handheld pan. Haiper exposes controls for duration, aspect ratio and generation quality, and it iterates on the model regularly, so output quality at a given date is a moving target rather than a fixed property. Buyers should test the current model on their own material before forming an opinion, since reviews written months apart describe meaningfully different products.

Image-to-video is the second mode and, for commercial work, often the more useful one. You supply a still image, typically a product shot, an illustration, a generated image or a frame from existing footage, and the model animates it. This is a much more controllable starting point than pure text, because composition, subject identity and brand colours are already fixed; the model only has to invent motion. It is the mode behind most practical use cases: animating a product hero image for an advertisement, bringing an editorial illustration to life, adding subtle motion to a thumbnail or a banner, creating background plates for a longer edit.

Keyframe control extends that idea. Instead of a single starting image, you can supply a start and an end frame and let the model interpolate between them, which gives direction to the motion and makes the result far more predictable. This is the closest generative video currently gets to traditional animation workflow, and it is the feature that separates a toy from a tool for people who actually ship video.

Editing capabilities sit around generation. Repaint lets you select a region of a generated or uploaded clip and change it with a new prompt, which is the video equivalent of inpainting and is useful for removing an unwanted object, changing a colour or fixing a detail the model got wrong. Upscaling increases resolution for delivery requirements. Background removal and simple trimming round out the toolset. None of these replace a non-linear editor, and the product is not trying to; the assumption is that generated clips get assembled elsewhere.

An API is available for developers who want generation inside their own product, whether that is a marketing platform, an ecommerce tool that animates product images at scale, or an internal content pipeline. Programmatic access is where generative video creates the most commercial value, because it turns a manual creative step into an automated one, but it also concentrates the cost and moderation questions.

Against competitors, Haiper sits in a crowded and fast-moving field. Runway is the most established professional option, with a deeper toolset covering generation, editing, motion brush, camera control and video-to-video, and a strong position in film and advertising workflows. Pika is a direct rival in short-form social generation with a large consumer community and fast iteration. Kling, from Kuaishou, and Luma Dream Machine both produce longer and often more physically coherent clips and attracted significant attention for quality. OpenAI's Sora demonstrated a step change in duration and coherence, and Google's Veo is embedded in a broader suite of creative tools. Stability AI focused more on image and audio. On the open-source side, models such as Wan and Mochi can be self-hosted, which matters for cost and control. Haiper differentiates on ease of use, credit-based accessibility, and its research pedigree, but it does not have Runway's professional tooling depth or the distribution of the largest laboratories. Anyone evaluating generative video in this period should assume the ranking changes every few months.

Pricing is credit-based freemium. A free tier provides a limited number of credits, generally refreshed daily or monthly, with output watermarked and capped in resolution and duration, which is enough to evaluate the model but not to produce deliverables. Paid subscriptions are monthly or annual tiers that increase the credit allowance, remove watermarks, unlock higher resolution and longer clips, grant commercial usage rights and add faster generation queues. Higher tiers and enterprise agreements add API volume, team seats and priority support. Because credits are consumed per generation and each generation is a gamble on quality, real cost per usable clip is several times the nominal credit cost; budgeting should be based on usable outputs, not on generations attempted. Current figures are published on the vendor site and change frequently.

The limitations are substantial and should be stated honestly. Clips are short, typically a few seconds, which means anything longer requires stitching, and coherence across stitched clips is not solved. Fine motor detail is unreliable: hands, faces in motion, text, logos and small objects distort, which rules out most work involving readable typography or precise product detail without cleanup. Prompt adherence is inconsistent, and the same prompt can produce very different results, so workflows need iteration budget. Character consistency across clips is difficult, which is a serious constraint for narrative or campaign work. Physics still breaks in ways that are obvious to a viewer. Generation time is not instant, and queue times vary. Content moderation restricts certain subjects, and the legal position on training data across the whole generative video category is not settled, so commercial use carries residual risk that varies by jurisdiction and by vendor.

Haiper fits social media teams producing high volumes of short-form video, marketers who need motion assets for campaigns and paid social, ecommerce sellers animating product imagery, creators experimenting with generative aesthetics, and developers building video generation into a product through an API. It does not fit narrative filmmakers who need consistent characters across minutes of footage, brands whose work depends on legible text and precise product detail, agencies with clients who cannot accept any ambiguity about training data provenance, or anyone expecting to replace an editing suite.

In the closing comparison, choose Haiper when you want quick, accessible short clips with a low barrier to entry and you are comfortable iterating. Choose Runway when you need professional-grade control and a full editing toolset for client work. Choose Kling or Luma when maximum visual quality and longer coherent clips matter most. Choose Pika when community and rapid consumer iteration suit your workflow. Choose a self-hosted open model when cost per clip at volume or data control dominates. The category moves quickly enough that the right answer is to keep two tools and re-evaluate quarterly rather than standardise on one.