DEV Community: Sora2 Hub Team

v0 Alternatives for Full-Stack Next.js Apps

Sora2 Hub Team — Fri, 09 Oct 2026 13:06:26 +0000

v0 is a strong Next.js tool. If you want a different setup for backend, hosting, billing or workflow, here are the alternatives worth testing and what each one is built around.

Quick answer: If you need a full-stack Next.js app and want something other than v0, look at four options. Massvai generates a Next.js 15 + TypeScript app with Supabase and Stripe in the stack, syncs to GitHub, deploys to your Vercel project, and exports every file. Bolt supports many JavaScript frameworks including Next.js, with hosting and databases on Bolt Cloud. Replit Agent works with any framework and hosts the app on Replit with a built-in PostgreSQL database. Lovable is a capable full-stack builder, but it doesn't generate Next.js. New apps use TanStack Start, so it only fits if Next.js isn't a hard requirement. Pick based on where you want the database and hosting to live.

First: what v0 already does well

It's worth being precise, because many "alternatives" lists understate v0. According to Vercel's docs, v0 builds with Next.js, React, TypeScript, Tailwind CSS and shadcn/ui. It can import existing GitHub repos, Vercel projects or ZIP files, syncs with GitHub in both directions, and publishes through pull requests that respect required checks. Since early 2026, previews run in Vercel Sandbox, so server code, API routes and database connections work in the preview. Databases and services come from Vercel Marketplace integrations such as Neon, Supabase and Upstash, and Stripe is available as an integration.

So people usually look elsewhere for workflow reasons, not missing features:

They want a fixed, opinionated SaaS stack (auth + database + billing) generated from the first prompt, rather than assembled from integrations.
They want the builder itself to host everything, including the database, outside Vercel.
They prefer a different pricing model. v0 combines a plan with usage credits, priced per user on team plans.
They want frameworks beyond Next.js in the same tool.

Comparison table

From each vendor's official site and documentation, checked October 2026. Plans change, so confirm details before you buy.

	v0 (reference)	Massvai	Bolt	Replit Agent	Lovable
Next.js	Default	Default (Next.js 15)	One of many JS frameworks	Any framework	Not offered (TanStack Start for new apps)
Database	Marketplace integrations (Neon, Supabase, Upstash…)	Supabase	Bolt Cloud database or Supabase	Built-in managed PostgreSQL (separate dev and production DBs)	Lovable Cloud or Supabase
Payments	Stripe integration	Stripe in the stack	Stripe via Bolt Cloud	Ask the agent to integrate	Built-in payments or Stripe
Hosting	Vercel	Your Vercel project	Bolt Cloud, Netlify, others via GitHub	Replit (Autoscale or Reserved VM)	Lovable hosting; self-host possible
GitHub	Two-way sync, PR-based publish	GitHub sync	Sync, branches, import repos	Check Replit's docs	Git sync (new repos only)
Code export	Yes	Every file	Via GitHub or download	Check Replit's docs	ZIP on paid plans; Git sync on all
Pricing unit	Plan + usage credits	Credits per agent generation	Tokens	Check Replit's pricing page	Credits per workspace

Massvai: an opinionated Next.js + Supabase + Stripe starting point

Massvai is an AI coding agent built around a single stack: Next.js 15, TypeScript and Tailwind, with Supabase for auth and data and Stripe for payments. You describe the product, optionally with a screenshot or reference file. The agent plans before it builds, then writes the code while you watch a live preview. When you're done, you sync the repo to GitHub and deploy to Vercel through a guided flow.

How it differs from v0: both produce Next.js and deploy to Vercel. v0 is open-ended and assembles services through Marketplace integrations. Massvai starts from a full SaaS shape (auth, database, billing-ready UI) built on Supabase. Every file is exportable, so the result is a standard Next.js + Supabase repo you can keep developing elsewhere.

Pricing: credits per agent generation. The Free plan includes 100 welcome credits with live preview and code export. Builder is $25/month (1,500 credits). Pro is $49/month (4,000 credits) and adds Supabase setup help, version history and priority support.

Good fit if: you're a founder or small team building a SaaS MVP or internal tool and want Supabase specifically.

Consider something else if: you need a non-Supabase database, a framework other than Next.js, or deep work inside a large existing repo. v0 and Bolt both import existing repositories.

Bolt: framework flexibility with built-in hosting

Bolt's docs describe support for JavaScript web technologies, with Node.js on the backend and a choice of JavaScript frontend frameworks. You can ask for Next.js, but it isn't the only path. New projects host on Bolt Cloud, which Bolt says is powered by Netlify and Supabase and covers hosting, domains, databases, auth, storage and server functions. Bolt can also generate Expo mobile apps when you ask for a mobile app in your first prompt. It syncs with GitHub, supports branches and imports existing repos.

Pricing: token-based. Bolt notes that larger projects use more tokens per message because project files are sent to the AI. Its pricing page lists a free tier, Pro from $25/month, and Teams per member.

Good fit if: you want one tool for several frameworks or a mobile companion app, and you're happy with hosting on Bolt Cloud. Bolt's docs say it doesn't support Python or PHP backends.

Replit Agent: any framework, hosted on Replit

Replit's docs describe its General Agent as working with any framework or language. It can do research and file generation, connect to services like BigQuery, Linear and Slack, and build full-stack apps. Every Replit App comes with a managed PostgreSQL database, and published apps get a separate production database. Full-stack apps built with Agent publish to Autoscale (the default) or Reserved VM deployments, not static hosting.

Good fit if: you want the build environment, database and hosting all in one place, or your project mixes Next.js with other languages.

Consider something else if: you specifically want deployments in your own Vercel account or a Supabase backend.

Lovable: a full-stack builder if Next.js is negotiable

Lovable is often listed as a v0 alternative, and it's a strong product, but its own FAQ says you can't choose Next.js. New apps (since May 13, 2026) use TanStack Start, and older apps use React + Vite. It includes Lovable Cloud (managed PostgreSQL, auth, storage), built-in payments on paid plans, hosting on lovable.app, and Git sync on any plan. Pricing uses workspace credits rather than per-seat fees.

Good fit if: you care more about getting a hosted app quickly than about the framework.

Not a fit if: your team, hiring plan or existing code requires Next.js.

How to choose

Is Next.js a hard requirement? If yes, your shortlist is v0, Massvai, and Bolt or Replit (both possible, but not their default).
Where should the database live? Vercel Marketplace (v0), Supabase (Massvai, also an option in Bolt and Lovable), Bolt Cloud, or Replit's built-in Postgres.
Where should the app run? Your own Vercel account (v0, Massvai), the builder's cloud (Bolt, Replit, Lovable), or anywhere via GitHub.
New project or existing repo? For existing repos, v0 and Bolt both document repo import.
How will you pay? Credits, tokens and per-seat plans aren't directly comparable. Estimate based on how many iterations your project needs.

A simple test

Write one product brief, for example "a team task tracker with auth, a Postgres-backed board, and a paid Pro plan", and give it to your top two picks. Compare four things: does it run, is the auth and database setup secure (check RLS or equivalent access rules), can you push it to your GitHub, and would you be comfortable maintaining the code six months from now? That last question usually decides it.

Sources: v0 docs, FAQ and pricing page; Massvai homepage; Bolt pricing page and support docs (supported technologies, Bolt Cloud, GitHub); Replit docs (General Agent, deployment types, SQL database, development and production databases); Lovable docs FAQ and pricing page. Checked October 2026.

Best AI Video Generators in 2026: Which Model for Which Job

Sora2 Hub Team — Fri, 09 Oct 2026 13:00:32 +0000

Best AI Video Generators in 2026: Which Model for Which Job

An honest look at the leading AI video models, what each one does well, where it falls short, and who it suits.

Quick answer: No single AI video model wins at everything in 2026, so pick by job. Google Veo 3.1 suits cinematic clips with native audio. Kling 3.0 suits multi-shot scenes up to 15 seconds. ByteDance Seedance 2.0 suits heavy reference-driven work. MiniMax Hailuo 2.3 suits affordable, motion-heavy clips. Alibaba Wan 2.7 suits prompt-based video editing. For stills and thumbnails, Nano Banana Pro and GPT Image 2 are the main options.

How to choose a model

Decide what you actually need first: native audio or not, one shot or several, consistent references (product, logo, character), output resolution and aspect ratio, and cost per usable clip. A cheap generation isn't cheap if you rerun it five times.

All the specs below come from each vendor's own documentation or announcement. Vendors update their models often, so check them again before you commit a budget.

The video models

Google Veo 3.1: cinematic shots with native audio

Strengths. Google describes Veo 3.1 as generating video with natively generated audio, including dialogue, sound effects and ambient sound. The Gemini API docs list 720p, 1080p and 4K output at 24 fps, in 16:9 or 9:16. Veo 3.1 supports text-to-video, image-to-video, first-and-last-frame transitions and reference images. Scene extension lets you continue a Veo clip to build longer sequences.

Limits. Base clips are 4, 6 or 8 seconds, so longer pieces depend on extension. In the Gemini API, extension works only on 720p Veo-generated video. Google's docs also note that higher resolutions take longer and that 4K costs more.

Best for: brand films, cinematic B-roll, and short ads where synced sound matters and you want to direct the camera.

Kling 3.0 (Kuaishou): multi-shot storytelling up to 15 seconds

Strengths. Kuaishou launched the Kling AI 3.0 series in February 2026: Video 3.0, Video 3.0 Omni, Image 3.0 and Image 3.0 Omni. According to Kling's guides, Video 3.0 adds native audio in several languages, dialects and accents, multi-shot storyboarding, element references for keeping characters consistent, and flexible clip lengths from 3 to 15 seconds. Kuaishou also highlights better preservation of text in the frame, such as signage and logos, and gives e-commerce ads as an example.

Limits. The Video 3.0 Omni guide lists 1080p and 720p modes, and credit cost depends on your inputs and clip length. Plan for 1080p delivery rather than native 4K video.

Best for: short narrative content, dialogue scenes, and product ads where a logo or character must stay readable and consistent across shots.

Seedance 2.0 (ByteDance Seed): the reference-heavy option

Strengths. Seedance 2.0 is built on what ByteDance calls a unified multimodal audio-video joint generation architecture. It accepts four kinds of input: text, image, audio and video. ByteDance says one generation can combine up to 9 images, 3 video clips and 3 audio clips with natural-language instructions. The model can borrow composition, camera movement, motion and sound from those references. ByteDance's launch post also mentions multi-shot audio-video output up to 15 seconds, stereo audio, and video extension and editing.

Limits. Setting up a prompt with many inputs takes more work, and it's at its best when you have reference assets. ByteDance's performance claims come from its own internal benchmark, so test it on your own content.

Best for: music-synced edits, outfit-change and product-showcase videos, and anyone who wants to copy a specific camera move or motion style from an existing clip.

Hailuo 2.3 (MiniMax): motion and value

Strengths. MiniMax built Hailuo 2.3 on Hailuo 02, with better body movement, physical realism, facial micro-expressions and response to motion instructions. It also improves support for anime, illustration, ink-wash and game-CG styles. A Hailuo 2.3 Fast variant generates faster at a lower price, and MiniMax publishes per-clip pay-as-you-go prices on its API pricing page.

Limits. MiniMax's API docs list 768P clips at 6 or 10 seconds and 1080P clips at 6 seconds only. That's shorter and lower-resolution than some competitors. Check whether the version you use generates audio, and plan to add sound in editing if it doesn't.

Best for: e-commerce sellers and social teams making many short clips, stylized and anime content, and dance or action scenes where motion quality matters most.

Wan 2.7 (Alibaba): editing video with prompts

Strengths. Alibaba launched Wan2.7-Video in April 2026 as four models: text-to-video, image-to-video, reference-to-video and video editing. It generates 2 to 15 seconds at 720p or 1080p. Its standout feature is editing with plain-language instructions: you can change actions, dialogue, appearance, scenes, style or camera work in an existing clip. Alibaba says it keeps up to five characters consistent across videos.

Limits. Wan 2.7 is offered as a hosted service through Alibaba Cloud Model Studio and the Wan website. If you chose Wan because earlier versions had openly downloadable weights, check which versions are actually open before you plan a self-hosted pipeline.

Best for: creators who iterate on existing footage, and teams that want to change one element of a clip without regenerating everything.

The image models (for thumbnails, key art and product shots)

Most video projects also need stills, for thumbnails, key art and first frames for image-to-video. Two image models cover most of that work.

Nano Banana Pro (Gemini 3 Pro Image)

Google positions Nano Banana Pro as its studio-quality image generation and editing model. Google highlights accurate, legible text inside images in multiple languages, controls for localized edits, camera angle, focus, lighting and color, and output at 1K, 2K or 4K. Images include SynthID watermarking.

Best for: posters, thumbnails, infographics and localized ad creatives where the text has to be right, plus high-resolution first frames for image-to-video.

GPT Image 2 (OpenAI)

OpenAI describes GPT Image 2 (gpt-image-2) as its state-of-the-art model for fast, high-quality image generation and editing, with flexible image sizes, high-fidelity image inputs and inpainting.

Best for: creators who liked OpenAI's style of prompt understanding and want quick concept art, product mockups and edits to existing images.

Quick comparison

Model	Type	What it's good at	Main limit to plan around	Good fit for
Veo 3.1	Video	Native audio, up to 4K, frame control	4–8 s base clips; extension at 720p	Cinematic ads, B-roll
Kling 3.0	Video	Multi-shot, 3–15 s, multilingual audio	1080p/720p modes	Narrative shorts, product ads
Seedance 2.0	Video	Mixed image/video/audio references	Prompt setup takes more work	Music-synced, reference-driven edits
Hailuo 2.3	Video	Motion, expressions, styles, Fast tier	6–10 s; 1080P only at 6 s	High-volume social and e-commerce
Wan 2.7	Video	Prompt-based video editing, 2–15 s	Hosted; check which versions have open weights	Iterating on existing clips
Nano Banana Pro	Image	Legible text, up to 4K	Image only	Thumbnails, posters, first frames
GPT Image 2	Image	Fast generation and editing, inpainting	Image only	Concepts, mockups, edits

Who should use what

Solo creators and YouTubers: Start with Veo 3.1 or Kling 3.0 for hero shots, and use Nano Banana Pro for thumbnails.
Marketers and agencies: Use Kling 3.0 or Seedance 2.0 when you need brand consistency across several shots. Veo 3.1 is a good choice when the client wants 4K.
E-commerce sellers: Hailuo 2.3 Fast and Kling 3.0 are worth testing for product videos at volume. GPT Image 2 or Nano Banana Pro can handle the product stills.
Editors iterating on footage: Wan 2.7's prompt-based editing can save full regenerations.

Most teams end up with two or three models, and the hidden cost is switching between them: separate accounts, separate billing, and different prompt habits.

Testing several models without juggling subscriptions

The most reliable way to choose is to run the same prompt and the same reference image through a few models, then compare usable clips per dollar rather than looks alone.

If you'd rather not open several separate accounts, Sora2 Hub is an AI image and video generator that offers many models, including Veo 3.1, Kling, Seedance 2.0, Hailuo, Wan, Nano Banana Pro and GPT Image 2, on one credit balance. That makes side-by-side tests and mixed workflows easier, such as a Nano Banana Pro first frame animated with Kling or Veo.

Bottom line

There's no single best AI video generator. Veo 3.1, Kling 3.0, Seedance 2.0, Hailuo 2.3 and Wan 2.7 each lead on a different job. Test two or three of them on your own prompts and reference images, and build your workflow around the results.

Sources: Google Gemini API Veo documentation; Kuaishou/Kling AI 3.0 announcement and model guides; ByteDance Seed Seedance 2.0 launch post; MiniMax Hailuo 2.3 announcement and API docs; Alibaba Cloud Wan2.7-Video announcement; Google DeepMind Nano Banana Pro page; OpenAI GPT-Image-2 model page. Specs were checked in October 2026 and may change.

Seedance 2.0 vs Kling 3.0 vs Veo 3.1: Which AI Video Model for Which Job

Sora2 Hub Team — Fri, 09 Oct 2026 12:54:37 +0000

Three leading video models compared on what their makers document: inputs, length, resolution, audio and control. Then matched to real jobs rather than ranked.

Quick answer: None of the three wins at everything. Veo 3.1 (Google) is the strongest choice for short, high-resolution single shots: clips are 4 to 8 seconds with native audio, up to 4K, with first/last-frame control and up to three reference images. Kling 3.0 (Kuaishou) is built for short stories: multi-shot clips from 3 to 15 seconds, native audio in several languages, and reusable "elements" that keep a character or product consistent. Seedance 2.0 (ByteDance) is the reference-heavy option. It takes text, images, video and audio together (up to 9 images, 3 videos and 3 audio clips, ByteDance says) and outputs multi-shot clips up to 15 seconds. Pick by job, then test on your own material.

Why "which is best?" is the wrong question

All three vendors publish impressive demos, and ByteDance and Google both describe strong results on their own evaluations. Those are vendor claims, measured by the vendors. No independent benchmark covers all three on the same prompts. What you can compare reliably is what each model accepts and produces, because that's documented. Those specs decide which jobs a model can do at all. Quality on your content is something you have to test.

Side-by-side specs

From each vendor's official documentation, checked October 2026. Plans and API tiers may expose only some of these options.

	Veo 3.1	Kling 3.0	Seedance 2.0
Maker	Google DeepMind	Kuaishou (Kling AI)	ByteDance Seed
Inputs	Text, image, first + last frame, up to 3 reference images, Veo video for extension	Text, image, start + end frames, element references (images; video in 3.0 / 3.0 Omni)	Text, image, video and audio together: up to 9 images, 3 videos, 3 audio clips
Clip length	4, 6 or 8 s (8 s required at 1080p/4K or with references); extension adds 7 s per step	3–15 s, flexible	Up to 15 s
Multi-shot in one generation	No (build sequences via extension or editing)	Yes: automatic or custom multi-shot	Yes
Resolution	720p, 1080p, 4K (extension at 720p only)	720P, 1080P, 4K listed for 3.0 and 3.0 Omni in Kling's API capability map	Not stated in the launch post; check your provider
Native audio	Always on (dialogue, effects, ambience)	Yes, multilingual, with dialects and accents	Yes, dual-channel stereo
Editing / extension	Scene extension of Veo clips	Separate tools for multi-element editing and lip sync	Video extension and targeted editing
Aspect ratios	16:9, 9:16	Check your plan or provider	Check your provider

Veo 3.1: the single hero shot

What it does well. Google's Gemini API docs describe Veo 3.1 as generating 8-second videos at 720p, 1080p or 4K with natively generated audio, at 24 fps. You get three kinds of control. Image-to-video animates a starting image. First-and-last-frame generation interpolates between two images you supply. Up to three reference images preserve the look of a person, character or product.

What to plan around. Base clips top out at 8 seconds. Longer pieces come from extension, which in the Gemini API works only on Veo-generated 720p video, adds 7 seconds per step, and requires the source video to have been created or referenced in the last two days. Google notes that higher resolutions mean longer waits and that 4K costs more.

Google's Gemini API docs now also recommend Gemini Omni Flash as the default video model for many workflows, and point to Veo 3.1 for scene extension, last-frame control and existing pipelines. If you build on Google's API, check both.

Best jobs: cinematic B-roll, hero product shots, short ads where one beautiful, well-lit 8-second shot with synced sound is the deliverable, and anything that needs 4K.

Kling 3.0: short stories with consistent characters

What it does well. Kling's model guide describes Video 3.0 as combining native audio, element consistency and multi-shot storytelling. You can let the model plan shots automatically, or use Custom Multi-Shot to set each shot's duration, framing, angle and camera movement. Clips run from 3 to 15 seconds. Native audio supports Chinese, English, Japanese, Korean and Spanish, plus dialects and accents.

The Element Library is the key feature for brand work. An element is a reusable asset built from 2–4 reference images. In 3.0 Omni, it can also be built from a short character video and carry a bound voice, so the same character or product looks and sounds consistent across generations. Kuaishou also points to better preservation of on-screen text such as signage and logos.

What to plan around. Multi-shot prompts take more planning than one-line prompts. Kling lists several variants (3.0 Turbo, 3.0, 3.0 Omni) with different capabilities. Turbo, for example, doesn't support element control in the capability map, so check which variant your plan or provider uses.

Best jobs: narrative social content, dialogue scenes, recurring characters or mascots, and multi-angle product ads in a single generation.

Seedance 2.0: when you have references to follow

What it does well. ByteDance describes Seedance 2.0 as a unified multimodal audio-video model that accepts four input types at once. Its launch post says you can combine up to 9 images, 3 video clips and 3 audio clips with a text instruction. The model can take composition, camera movement, motion rhythm, visual effects and sound from those references, and even follow a storyboard image. It outputs multi-shot clips up to 15 seconds with stereo audio, and supports video extension and targeted editing of clips, characters and actions.

What to plan around. It works best when you bring assets. A text-only prompt doesn't use its main strength. ByteDance's own launch post lists remaining weaknesses: detail stability, multi-subject consistency, text rendering accuracy and occasional audio distortion. ByteDance also notes that using real people's portraits as references requires identity verification or authorization.

Best jobs: music-synced edits, recreating a camera move or choreography from a reference clip, storyboard-to-video, and outfit or product showcases built from several angles.

Which model for which job

Job	First choice	Also test
8-second hero shot for a brand film, 4K delivery	Veo 3.1	Kling 3.0
15-second social ad with three different shots	Kling 3.0	Seedance 2.0
Recurring mascot or presenter across a series	Kling 3.0 (elements)	Veo 3.1 (reference images)
Copy the camera move from a reference video	Seedance 2.0	—
Edit cut to an existing music track	Seedance 2.0	Kling 3.0
Product photo → simple animated packshot	Veo 3.1 or Kling 3.0	Seedance 2.0
Dialogue in Japanese, Korean or Spanish	Kling 3.0	Veo 3.1
Turn a storyboard image into a sequence	Seedance 2.0	Kling 3.0 (custom multi-shot)

How to run a fair test

Fix the inputs. Use the same reference image, the same prompt (adjusted only for syntax), and the same aspect ratio and duration where possible.
Generate several takes per model. One sample tells you very little.
Score usable clips. Count a clip as usable only if it needs no regeneration: correct product, stable faces, believable physics, clean audio.
Track cost per usable clip. Prices differ by model, resolution and duration, so the cheapest generation isn't always the cheapest result.
Repeat in a month. All three vendors ship updates frequently.

Running a test like this is easier from one account. Sora2 Hub is a credit-based multi-model studio with Seedance 2.0, Kling 3.0 and Veo 3.1 (plus Hailuo, Wan and image models such as Nano Banana Pro and GPT Image 2) on a single credit balance. You can send the same prompt to all three and compare results without three subscriptions.

Bottom line

Choose Veo 3.1 for polished single shots and 4K, Kling 3.0 for multi-shot stories and consistent characters, and Seedance 2.0 when your references (images, video, music) should drive the result. Many teams keep two of the three and switch per brief.

Sources: Google Gemini API documentation (Veo 3.1 and video generation overview); Kling VIDEO 3.0 model guide, Kling Element Library guide and Kling API video capability map; ByteDance Seed "Seedance 2.0 Official Launch" (Feb 12, 2026). Checked October 2026. Capabilities vary by plan and provider, so confirm before production.

How to Build a SaaS MVP with Next.js, Supabase and Stripe Using an AI Agent

Sora2 Hub Team — Fri, 09 Oct 2026 12:48:50 +0000

A practical plan for using an AI coding agent to scaffold a subscription SaaS, plus the auth, database and billing details you still need to check yourself.

Quick answer: Write a one-page spec first: users, the one core workflow, the data tables and the paid plan. Have an AI agent scaffold the Next.js app with Supabase auth and a Stripe subscription flow, then review four areas yourself. (1) Server code checks the user with supabase.auth.getClaims() (or getUser()), never getSession(). (2) Row Level Security is enabled on every table in an exposed schema. (3) The Stripe webhook verifies signatures against the raw request body and handles duplicate and out-of-order events. (4) Secret keys never reach the browser. Then deploy to Vercel with separate test and live environment variables.

Step 1: Scope the MVP before you prompt

AI agents are fast, so scope creep is cheap to start and expensive to maintain. Write down:

Who signs up: individuals, or teams with multiple members?
The one workflow worth paying for: for example, "upload a CSV, get a cleaned report, download it".
Data: three to six tables at most, such as profiles, projects, reports, subscriptions.
Plans: one free tier and one paid tier is enough to test willingness to pay.
Out of scope: admin panels, teams, usage-based billing, i18n. Add them after real users ask.

This spec becomes your first prompt.

Step 2: Write a prompt the agent can execute

Name the stack, the pages and the rules. Leave visual polish for later. For example:

Build a Next.js App Router app in TypeScript with Tailwind. Use Supabase for email/password and magic-link auth and for the database. Tables: profiles (id = auth user id), projects (owner_id, name, created_at), subscriptions (user_id, stripe_customer_id, stripe_subscription_id, status, price_id, current_period_end). Enable RLS on all tables so users can only read and write their own rows. Pages: landing, pricing, login, dashboard (list and create projects), settings (manage billing). Use Stripe Checkout in subscription mode for one "Pro" monthly price, and the Stripe customer portal for plan changes and cancellation. Add a webhook route that verifies the Stripe signature and updates subscriptions. Gate project creation beyond 3 projects behind an active subscription, checked on the server.

Ask the agent to plan first and show you the file structure and SQL migration before writing everything. Fixing a plan is much cheaper than fixing code.

Step 3: Review authentication

Supabase's Next.js server-side auth guide uses the @supabase/ssr package with cookie-based sessions. Two things to check in what the agent generates:

Verify the user on the server properly. Supabase's docs say to use supabase.auth.getClaims() to protect pages and user data. It verifies the access token's signature. You can also call getUser() for a fresh, server-confirmed user record. The docs also say never to trust supabase.auth.getSession() in server code, because it reads the session from a cookie without revalidating it, and cookies can be forged.
Session refresh runs in middleware. The Supabase guide refreshes the auth token in middleware (Next.js 16 renamed middleware to "Proxy"). In Next.js 15, cookies() is async, so the server client is created with await.

Also check that protected routes redirect anonymous users on the server, not only in client components.

Step 4: Review the database and Row Level Security

This is the most important review step. Supabase's RLS guide is blunt: a table in an exposed schema without RLS is readable and writable by any role with a grant on it. Enable RLS on every table in an exposed schema.

Check the generated migration for:

alter table ... enable row level security; on every table, including tables added later.
Policies scoped to auth.uid(): for example, projects rows where owner_id = auth.uid().
Views: Supabase notes that views bypass RLS by default because they are usually created by the postgres user. Don't expose a view over a protected table unless it's made safe.
Secret key usage: the secret (service role) key bypasses RLS. Supabase says never to use it in the browser. Use it only in server code such as the webhook handler.

Then test as a real user: create two accounts and confirm neither can see the other's data through the API.

Step 5: Review Stripe billing

The standard flow is: Checkout (subscription mode) → webhook updates your subscriptions table → your app reads that table to grant access → the customer portal handles upgrades and cancellation.

Stripe's webhook documentation covers what agents most often get wrong:

Verify every event. Use the Stripe-Signature header and your whsec_ signing secret. Without verification, Stripe warns, attackers could send fake events to grant access.
Use the raw body. Stripe requires the unmodified raw request body for signature verification. In a Next.js route handler, read it with await request.text() and don't parse JSON first.
Return 2xx quickly, before slow work, to avoid timeouts.
Handle duplicates. Endpoints can receive the same event more than once, so log processed event IDs and skip repeats.
Don't assume order. Stripe doesn't guarantee event order. Fetch the current subscription from the API when in doubt, rather than trusting the sequence.

For subscriptions, Stripe's docs describe customer.subscription.created, .updated (renewals, plan changes, discounts) and .deleted (subscription ends). On invoice.paid, Stripe recommends confirming the subscription status is active before extending access. Handle invoice.payment_failed too, so access lapses cleanly.

To test locally, run stripe listen --forward-to localhost:3000/api/webhooks/stripe (adjust to your route). The CLI prints a signing secret for local use.

Step 6: Deploy

Push the code to GitHub.
Import the repo into Vercel and set environment variables: Supabase URL and publishable key (public), Supabase secret key and Stripe secret and webhook secret (server-only, no NEXT_PUBLIC_ prefix).
Create a live-mode webhook endpoint in Stripe pointing at your production URL, and use its own signing secret.
Add your production URL to Supabase Auth's redirect settings so magic links and OAuth return to the right domain.
Run one real subscription with a live card, then cancel it through the portal and confirm access is removed.

Where an AI agent helps, and where it doesn't

An agent removes most of the boilerplate: routing, forms, dashboard UI, the Supabase client setup, the migration skeleton and the Checkout and portal routes. It doesn't remove your responsibility for security and money. RLS policies, webhook verification and server-side access checks are where a plausible-looking bug costs real data or revenue. Read those files line by line.

This is the workflow Massvai is built around. Its AI coding agent turns a prompt into a full-stack Next.js 15 + TypeScript app with Supabase and Stripe in the stack, shows the build in a live preview, syncs the repo to GitHub, and deploys to your own Vercel project through a guided flow. You can export every file, so you can run the review checklist below in your own editor.

Pre-launch checklist

[ ] Server code uses getClaims() / getUser(), never getSession(), for access decisions
[ ] RLS enabled on every exposed table; policies tested with two accounts
[ ] No views exposing protected tables
[ ] Supabase secret key and Stripe secrets are server-only
[ ] Webhook verifies signatures against the raw body
[ ] Duplicate events skipped by event ID; no dependence on event order
[ ] Access granted only for active subscriptions; failed payments handled
[ ] Customer portal linked from settings
[ ] Separate test and live keys, webhooks and redirect URLs

Sources: Supabase docs ("Setting up Server-Side Auth for Next.js", "Row Level Security"); Stripe docs ("Receive Stripe events in your webhook endpoint", "Using webhooks with subscriptions"); Next.js docs (Proxy file convention, version history); Massvai homepage. Checked October 2026.

How to Turn a Product Photo Into a Video Ad with AI (Step by Step)

Sora2 Hub Team — Fri, 09 Oct 2026 12:26:56 +0000

A practical workflow for turning one packshot into a short, platform-ready video ad, from prepping the photo to picking a model, writing the motion prompt and checking the result.

Quick answer: Start with a sharp, well-lit photo of the real product. If you need a new setting, build a styled first frame with an image model such as Nano Banana Pro or GPT Image 2. Then animate that frame with an image-to-video model. Veo 3.1 suits single cinematic shots with native audio. Kling 3.0 suits multi-shot ads up to 15 seconds. Seedance 2.0 suits ads built from several references (extra images, a video for camera movement, a music track). Keep the camera move simple, generate a few variations, and check every frame for a warped logo or a changed product before you publish.

Why start from a photo instead of text

Text-to-video is fine for mood footage, but it invents the product. For an ad, the product on screen has to match what the customer receives. Image-to-video starts from your actual packshot, so shape, color and label are anchored from the first frame. All three models in this guide support image-to-video. Veo 3.1 and Kling 3.0 also let you set start and end frames.

Step 1: Prepare the product photo

The output is limited by the input. Before you generate anything:

Use the highest-resolution original you have. Don't use a compressed screenshot from your store page.
Show the product clearly. Keep hands, props and other packaging out of the way of the label and logo.
Light it evenly. Hard reflections on glossy packaging often turn into flicker once the image moves.
Pick the hero angle. The model will mostly keep the angle you give it, so choose the one you want viewers to remember.
Check your rights. Use photos you own or have licensed, especially if they show people.

A plain background is fine. You can build the scene in the next step.

Step 2 (optional): Build a styled first frame with an image model

A product on a white background works for a clean spin or zoom. For a lifestyle ad (a skincare bottle on a bathroom shelf, a sneaker on wet pavement), first make a still frame with the product placed in that scene.

Nano Banana Pro (Google's Gemini 3 Pro Image). Google says it can blend multiple reference images, keep up to six objects at high fidelity, output at 1K, 2K or 4K, and render legible text. That last point matters when the label has to survive.
GPT Image 2 (OpenAI). OpenAI describes it as a model for fast, high-quality generation and editing, with high-fidelity image inputs and inpainting. Inpainting is useful when you only want to change the background around an untouched product.

Whichever you use, zoom in and compare the label to the real product. Both vendors note that their models can still get small text and fine details wrong. A frame that looks right at thumbnail size can have a misspelled ingredient list.

Generate the frame in the ratio you plan to publish in, such as 9:16 for vertical feeds. That way you won't have to crop the product later.

Step 3: Pick the video model for the job

If your ad needs…	Try	Why (per the vendor's documentation)
One polished hero shot with sound	Veo 3.1	Generates 4, 6 or 8 s clips with native audio at 720p, 1080p or 4K (1080p and 4K at 8 s only), 16:9 or 9:16. Accepts up to three reference images to preserve a product's appearance, plus first and last frames.
Several shots in one clip (close-up, then wide, then a pack shot)	Kling 3.0	Multi-shot generation, 3–15 s durations, native audio, start frame plus element references. Kuaishou cites better text preservation for signage and logos, with e-commerce ads as an example.
Ads built from several assets (extra product angles, a reference video for the camera move, a music track)	Seedance 2.0	Accepts text, images, video and audio together. ByteDance says one generation can use up to 9 images, 3 video clips and 3 audio clips, with multi-shot output up to 15 s.

If you aren't sure, run the same first frame through two models and compare them. Results depend heavily on the product. Reflective, transparent and heavily branded packaging all behave differently.

Step 4: Write a motion prompt, not a description

The image already shows what the product looks like. The prompt should describe what happens. A good structure:

Camera: one move per shot (slow push-in, orbit, top-down tilt).
Action: what moves in the scene (steam rising, water droplets running down the can, fabric shifting in a breeze).
Product constraint: tell the model to keep the product unchanged.
Light and mood: golden hour, soft studio light, neon reflections.
Audio (for models with native audio): ambient sound, a sound effect, or a short spoken line.

Example (single shot, Veo 3.1 or Kling 3.0):

Slow push-in on the amber glass serum bottle on a marble bathroom shelf. Morning sunlight moves across the shelf and a few water droplets slide down the glass. The bottle, label and cap stay exactly as in the image. Soft, calm mood. Audio: quiet room tone and a light glass clink at the end.

Example (multi-shot, Kling 3.0):

Shot 1: macro close-up of the sneaker's sole stepping into a shallow puddle, splash in slow motion. Shot 2: wide shot of a runner on a wet city street at dusk, neon reflections. Shot 3: the sneaker on a black plinth, slow orbit, logo clearly visible. Keep the sneaker design and logo identical across all shots.

Avoid asking for the product to transform, open, or show features it doesn't have. Every extra instruction is another chance for the model to change the product.

Step 5: Generate variations and check every frame

Generate three or four versions of each shot, then review them at full size, frame by frame:

Logo and label: do the letters stay readable and stable, or do they melt or shift mid-clip?
Geometry: did the bottle gain a second cap, did the shoe change its stitching, did the strap disappear?
Color: compare the product color to the real item under neutral light.
Hands and physics: if a person handles the product, check fingers and contact points.
Audio: make sure any generated speech says nothing you can't legally claim.

If a clip is close but flawed, try a shorter duration or a simpler camera move before you rewrite the whole prompt. Count cost per usable clip, not per generation.

Step 6: Finish the ad in an editor

AI output is raw footage, not a finished ad. In your editor:

Add the offer and call to action as real text overlays. That's more reliable than asking the model to render them.
Add licensed music if you didn't use native audio, and set levels for sound-off viewing with captions.
Export each platform's ratio and check its current ad specs. They change, so check the platform's help pages rather than an old blog post.
Follow disclosure rules. Many ad platforms and marketplaces have policies on AI-generated or altered media. Check the ones you advertise on.

Most important: the ad must show the product as it really is. A video that makes a product look bigger, glossier or more capable than it is can mislead customers and lead to returns.

Doing it all in one place

This workflow often uses two or three models: an image model for the first frame and one or two video models to compare. If you'd rather not keep separate accounts and bills, Sora2 Hub is a credit-based studio that offers Nano Banana Pro, GPT Image 2, Veo 3.1, Kling 3.0, Seedance 2.0, Hailuo and Wan from one credit balance. You can make the first frame, animate it with two different models and compare the clips without switching tools.

Checklist

High-resolution, well-lit photo of the real product, with rights cleared.
Optional styled first frame, with the label checked at full zoom.
Model chosen for the job: Veo 3.1 (hero shot), Kling 3.0 (multi-shot), Seedance 2.0 (multi-reference).
Motion prompt: camera, action, "keep the product unchanged", light, audio.
Three or four variations per shot, checked frame by frame.
Text, CTA, music and captions added in the editor. Platform specs and disclosure rules checked.

Sources: Google Gemini API Veo 3.1 documentation; Google DeepMind Nano Banana Pro page and Gemini API image generation docs; OpenAI GPT-Image-2 model page and image generation guide; Kling VIDEO 3.0 model guide and Kling API capability map; ByteDance Seed "Seedance 2.0 Official Launch" post. Checked October 2026. Model capabilities change often, so confirm current limits before production.

Nano Banana Pro vs GPT Image 2 for Product Images

Sora2 Hub Team — Fri, 09 Oct 2026 12:25:29 +0000

How Google's and OpenAI's image models compare for packshots, lifestyle scenes, labels, localized ads and edits, based on what each vendor documents.

Quick answer: Both models can place a real product into new scenes and edit existing photos, so the choice depends on the job. Nano Banana Pro (Google's Gemini 3 Pro Image) suits multi-reference composites and text-heavy creatives. Google documents up to 14 reference images (up to 6 objects at high fidelity, plus character and style references), legible and translatable in-image text, Google Search grounding, and 1K/2K/4K output. GPT Image 2 (OpenAI) suits precise, controlled edits and odd sizes. It processes every input image at high fidelity, supports mask-based inpainting, and accepts custom sizes up to 3840 px on the long edge with ratios up to 3:1. Neither model is perfect with small text, so check every label against the real product.

First, a note on versions

Both vendors ship quickly. As of October 2026, Google's docs list newer Nano Banana 2 and 2.1 models next to Nano Banana Pro, and position Pro as the premium option for complex tasks, localization and brand consistency. OpenAI's image guide now leads with GPT Image 2.5 (Sunburst and Flare) and lists gpt-image-2 under earlier models. This article compares Nano Banana Pro and GPT Image 2 because they're widely available in creative tools. Check which versions your tool offers before you standardize.

What each vendor documents

	Nano Banana Pro (`gemini-3-pro-image`)	GPT Image 2 (`gpt-image-2`)
Inputs	Text + up to 14 reference images: up to 6 objects at high fidelity, up to 5 characters, up to 3 style references	Text + one or more reference images. All image inputs are processed at high fidelity automatically
Output size	1K, 2K or 4K. Aspect ratio follows the input image unless set	Flexible: edges in multiples of 16 px, max 3840 px, ratio up to 3:1. Popular sizes include 1024×1024, 1536×1024, 2048×2048 and 3840×2160
Quality setting	Resolution tier (1K/2K/4K)	`low`, `medium`, `high`, `auto`
Editing	Conversational, multi-turn edits; localized changes to lighting, focus, angle, color	Edits endpoint with masks (inpainting); multi-turn editing via the Responses API
Text in images	Google highlights legible text and translating text inside images for other locales	OpenAI says text rendering is improved but placement and clarity can still miss
Extras	Grounding with Google Search; a "thinking" step for complex prompts	Batch API support
Provenance	All outputs carry an invisible SynthID watermark	Check OpenAI's current documentation for provenance details
Documented limits	Can struggle with small faces, spelling and fine detail; complex blends can look unnatural	Complex prompts up to ~2 min; text placement; consistency across generations; precise layout

Job by job

1. Clean packshots and catalog consistency

You want the same product, lit the same way, across a set of images.

GPT Image 2 works well when you already have a decent photo and want controlled changes: tidy the background, fix the lighting, extend the canvas to a marketplace ratio. Because it always processes inputs at high fidelity, product details carry through edits. OpenAI notes this can raise input-token costs on edit requests.
Nano Banana Pro is useful when you need a series of variants, such as the same bottle in several colorways or angles. Its multi-turn editing and object-fidelity references help keep the product stable across the set.

Marketplaces often have strict rules for main images (background, cropping, added text). Check them before you generate, and keep a real photo as the source of truth.

2. Lifestyle scenes

You want the product on a kitchen counter, a beach towel or a desk setup.

Nano Banana Pro has the edge on paper for composites. You can supply the product, a model, a prop and a style reference in one request, within the documented limits of 6 high-fidelity objects, 5 characters and 3 style images.
GPT Image 2 handles reference-based scenes too. OpenAI's own example combines four product images into one gift-basket shot. Inpainting a new background around a masked product is a dependable way to leave the product pixels largely alone.

3. Labels, packaging text and localized ads

This is where the two models differ most in emphasis. Google markets Nano Banana Pro around clear text and localization. Its examples include translating can labels into another language while keeping everything else the same, and adapting a poster to a new market. OpenAI's guide is more cautious and lists text placement and clarity as a known limitation.

In practice, don't trust either model with regulated text. Ingredient lists, dosages, certifications and legal copy should be added as real text in a design tool. Use the model for headlines and mood, and proofread everything.

4. Precise edits to an existing photo

You want to remove a stray cable, swap the backdrop, or change the strap color only.

GPT Image 2's mask-based inpainting is built for this. You mark the area, describe the change, and leave the rest.
Nano Banana Pro supports localized edits through conversation. Google notes that masked editing and major lighting changes can sometimes produce artifacts.

5. Ad creatives at unusual sizes

Banners, marketplace headers and story frames often need awkward dimensions. GPT Image 2's custom WIDTHxHEIGHT sizing (up to 3:1, max 3840 px edge) covers many of them directly. Nano Banana Pro offers 1K/2K/4K with set aspect ratios, and Google demonstrates outpainting to new ratios while keeping the subject in place.

6. First frames for video

If the image will be animated later with a model like Veo 3.1 or Kling 3.0, generate it at the video's aspect ratio and at least at the video's resolution. Nano Banana Pro's 4K tier and GPT Image 2's 4K sizes both cover this. Google's own Veo docs show Nano Banana images used as Veo reference images.

Summary: which to pick

Job	Lean toward
Multi-reference composite (product + model + props + style)	Nano Banana Pro
Localized versions of the same creative	Nano Banana Pro
Masked edit on an existing photo	GPT Image 2
Unusual banner sizes	GPT Image 2
Colorway or angle variant sets	Nano Banana Pro (test GPT Image 2 too)
Background cleanup of a real packshot	GPT Image 2 (test Nano Banana Pro too)
First frame for image-to-video	Either, at the video's ratio

These are tendencies based on the documented features, not benchmark results. The best way to decide is to send your own product photo and brief to both.

Keep it honest

AI product images still have to show the product the customer will receive. Don't add features, change proportions, or improve materials beyond reality. Check the platform's rules on AI-generated or edited imagery. Keep the original photos on file.

Testing both without two accounts

Sora2 Hub is a credit-based multi-model studio where Nano Banana Pro and GPT Image 2 sit alongside video models such as Veo 3.1, Kling 3.0 and Seedance 2.0. You can run the same product brief through both image models, keep the better result, and animate it, all from one credit balance.

Sources: Google DeepMind Nano Banana Pro page; Google Gemini API "Nano Banana image generation" docs (features, reference-image limits, limitations); OpenAI GPT-Image-2 model page and image generation guide (sizes, quality, input fidelity, edits, limitations); Google Gemini API Veo 3.1 docs. Checked October 2026.

Lovable vs Bolt vs v0 vs Massvai: Which AI App Builder for a Full-Stack Next.js App?

Sora2 Hub Team — Fri, 09 Oct 2026 12:21:00 +0000

Four prompt-to-app tools compared on stack, deployment, code ownership and pricing model, with recommendations by scenario rather than a single winner.

Quick answer: If your project must be a Next.js app, v0 (by Vercel) and Massvai generate Next.js by default. v0 builds on Next.js, React, Tailwind and shadcn/ui and deploys to Vercel. Massvai scaffolds Next.js 15 with Supabase and Stripe and deploys through GitHub to Vercel. Bolt supports JavaScript frameworks broadly, so Next.js is possible but not its only path, and it hosts on Bolt Cloud by default. Lovable doesn't offer Next.js: new Lovable apps use TanStack Start with a built-in backend or Supabase. All four let you take your code to GitHub.

Quick Verdict

There's no single best tool here, because the four products make different bets:

Lovable bets on an all-in-one platform: it builds, hosts and runs the backend for you, on its own fixed stack.
Bolt bets on flexibility: many JavaScript frameworks, built-in hosting and databases, and even Expo mobile apps.
v0 bets on the Vercel ecosystem: Next.js-first code, GitHub pull-request workflows and one-click Vercel production deploys.
Massvai bets on one opinionated full-stack template: Next.js 15 + Supabase + Stripe, built by an agent, then pushed to GitHub and deployed to Vercel, with every file exportable.

If "Next.js" is a hard requirement, start with v0 or Massvai. If it isn't, Lovable and Bolt deserve a fair look.

Comparison table

Based on each vendor's official site and documentation, checked October 2026. Plans change often, so confirm current details before you buy.

	Lovable	Bolt	v0	Massvai
Default framework	TanStack Start (new apps from May 13, 2026); React + Vite for older apps	JavaScript frameworks; Node.js backends	Next.js, React, TypeScript, Tailwind, shadcn/ui	Next.js 15, TypeScript, Tailwind
Can you choose Next.js?	No (fixed stack)	Yes, among other JS frameworks	Yes, it's the default	Yes, it's the default
Database / backend	Built-in Lovable Cloud (managed PostgreSQL, auth, storage) or Supabase	Bolt Cloud database or Supabase	Vercel Marketplace integrations (e.g. Neon, Supabase, Upstash)	Supabase
Payments	Built-in payments (paid plans) or your own Stripe	Stripe via Bolt Cloud	Stripe available as a Marketplace integration	Stripe in the stack
Default hosting	Lovable hosting (`lovable.app`); self-host possible	Bolt Cloud; Netlify integration; others via GitHub	Vercel	Vercel (guided GitHub → Vercel flow)
GitHub	Git sync on any plan; creates a new repo (no import of existing repos)	Sync, branches, import existing repos	Import repos; automatic branches and PRs	GitHub sync
Code export	ZIP download on paid plans; Git sync on all plans	Via GitHub sync or download	Export code; deploy elsewhere	Export every file
Pricing model	Credits, priced per workspace (not per seat)	Tokens, per user on Teams	Plan + usage credits, per user on team plans	Credits, monthly or annual plans

Stack: how "Next.js" is each tool?

v0 says plainly that it "uses Next.js, React, TypeScript, Tailwind CSS, and shadcn/ui," and its docs point to Next.js patterns for things like internationalization. Since its February 2026 update, previews run in Vercel Sandbox, so server-side code, API routes and database connections work in the preview.

Massvai is built around one stack: a Next.js 15 project in TypeScript with Tailwind, Supabase for auth and data, and Stripe for payments. Its agent plans the work first and keeps the build visible in a live preview. Because the stack is fixed, you get fewer choices but a consistent project structure.

Bolt focuses on JavaScript web technologies: Node.js on the backend and any JavaScript framework on the frontend. You can ask for Next.js, but Bolt isn't tied to it. You can also get an Expo-compatible mobile app by putting "mobile app" in your first prompt. Bolt doesn't support Python or PHP backends.

Lovable is open about this: you can't choose a different framework such as Next.js. Apps created from May 13, 2026 use TanStack Start, which renders on the server. Older apps use React + Vite. If you need another stack, Lovable suggests syncing to Git and continuing outside Lovable. For many apps that's fine. For a team standardized on Next.js, it decides the question.

Deploy: where does the app live?

Lovable hosts your app for you. Publishing to a lovable.app URL is free on all plans, and custom domains need a paid plan. You can also deploy to other hosting.
Bolt hosts every new project on Bolt Cloud, which Bolt says is powered by Netlify and Supabase and covers hosting, domains, databases, auth, file storage, server functions and analytics. You can also publish through the Netlify integration, or anywhere else via GitHub.
v0 deploys to Vercel with Publish. In GitHub-backed projects, Publish creates or reuses a pull request, merges it, and starts the production deployment. v0 never pushes directly to main, and required checks still apply.
Massvai pushes the reviewed codebase to GitHub and deploys to Vercel through a guided one-click flow. Your app runs on your own Vercel project.

The real question is whether you want the builder to also be your host (Lovable, Bolt Cloud) or want deployments in an account you already control (v0 and Massvai on Vercel, or anything via GitHub).

Code ownership and portability

All four vendors say you can take your code with you, but the details differ:

Lovable states that your apps, code and content are yours. Git sync works on any plan, and ZIP download needs a paid plan. One catch is that Git sync creates a new repository, so you can't start from an existing repo. Database data is exported separately.
Bolt syncs commits to GitHub automatically, supports branches, and can import an existing repository as a new project. Merging branches happens on GitHub, not inside Bolt.
v0 can import existing GitHub repositories, Vercel projects or ZIP files, and syncs with GitHub in both directions. Vercel's FAQ says it doesn't own the code generated from your prompts. The full experience assumes a connected Vercel project, but you can export and deploy elsewhere.
Massvai lets you inspect and export every file and sync the repo to GitHub. Because it's a standard Next.js + Supabase project, you can keep developing in your own editor and pipeline.

If you might leave the tool later, a standard framework plus a GitHub repo you control matters more than any single feature.

Pricing model (not just price)

All four use usage-based units, but they count usage differently:

Lovable uses credits. One workspace balance covers building, chatting, hosting, the built-in backend and in-app AI features. Plans are priced by credits, not seats, so adding teammates doesn't change the subscription. The Free plan includes 5 daily build credits, up to 30 a month.
Bolt uses tokens. Bolt says most token use comes from syncing your project files to the AI, so larger projects use more tokens per message. Its pricing page lists a Free plan (1M tokens a month, 300K daily limit), Pro from $25 a month (starting at 10M tokens), and Teams at $30 per member a month. Paid tokens roll over one extra month.
v0 combines a plan with usage credits, billed against per-model token rates. Its pricing page lists Free (7 messages a day), Plus at $30 per user a month with $30 of monthly credits, Business at $100 per user a month, and Enterprise.
Massvai uses credits per agent generation. The Free plan includes 100 welcome credits with live preview and code export. Paid plans are Builder ($25 a month, 1,500 credits) and Pro ($49 a month, 4,000 credits, with Supabase setup help, version history and priority support).

Don't compare headline prices alone. The credit and token systems aren't equivalent, and the real cost depends on project size and how many iterations you need.

Best for: by scenario

"My team standardizes on Next.js and Vercel, and we work through pull requests."
→ v0. It's Next.js-first, imports existing repos, and its PR-based publish flow fits teams with code review and CI checks.

"I'm a founder who wants a SaaS skeleton (auth, database, payments) on a standard stack I can hand to a developer later."
→ Massvai. It starts with Next.js 15 + Supabase + Stripe, syncs to GitHub, deploys to your Vercel account, and exports every file.

"I don't care about the framework. I want the platform to handle hosting, database and payments."
→ Lovable. Built-in hosting, a managed backend, built-in payments and one credit balance mean fewer services to set up.

"I want framework flexibility, or a mobile app too."
→ Bolt. It supports a range of JavaScript frameworks, Node backends, Bolt Cloud or Netlify hosting, and Expo mobile apps.

"I already have a repo and want AI help on it."
→ v0 or Bolt, since both import existing GitHub repositories. Lovable currently doesn't.

"I'm budgeting a team."
→ Look closely at seat vs. pool pricing: Lovable prices by workspace credits, while v0 team plans and Bolt Teams are priced per user.

Final thoughts

These tools overlap less than their marketing suggests. Lovable and Bolt are full platforms that can host everything. v0 and Massvai produce Next.js code that deploys to Vercel. If Next.js is a requirement, that narrows the field quickly. Then decide whether you want an open-ended Vercel-native assistant (v0) or an opinionated Next.js 15 + Supabase + Stripe starting point you fully own (Massvai). The best test is to give the same product brief to two of them and see which output you'd rather maintain.

Sources: Lovable docs FAQ and pricing page; Bolt pricing page and support docs (Supported technologies, Bolt Cloud, GitHub); v0 docs, FAQ and pricing page; Massvai homepage. Checked October 2026.

Best AI Image-to-Video Generators in 2026

Sora2 Hub Team — Fri, 09 Oct 2026 12:19:48 +0000

Best AI Image-to-Video Generators in 2026

How Veo 3.1, Kling 3.0, Seedance 2.0, Hailuo 2.3 and Wan 2.7 handle the job of animating a still image, compared on frame control, references, length and resolution.

Quick answer: For image-to-video in 2026, the main options are: Veo 3.1 for polished 8-second shots up to 4K with native audio, first-and-last-frame control and up to three reference images. Kling 3.0 for 3–15-second clips that can cut between several shots while keeping a character or product consistent. Seedance 2.0 when you want to combine a still with other images, video and audio references. Hailuo 2.3 for expressive motion and camera-move commands in 6–10-second clips. Wan 2.7 for first-frame, first-and-last-frame and video-continuation workflows from 2 to 15 seconds. Choose based on how much control you need over the start, the end and the motion in between.

What image-to-video actually needs

Animating a still image is a different job from text-to-video. The image already sets the subject, composition and style, so what matters is:

Fidelity to the source: does the face, product or logo stay the same once it moves?
Frame control: can you set only the first frame, or both first and last?
References beyond the first frame: can you add more images (another angle, a character sheet) or a reference video for motion?
Length and resolution: how long a clip can you get from one generation, and at what quality?
Audio: is sound generated with the video, or added later?

The comparison below sticks to what each vendor documents. Quality on your images is something to test yourself.

Comparison at a glance

From official vendor documentation, checked October 2026. Options vary by plan, API tier and the tool you use.

Model	First frame	First + last frame	Extra references	Length per generation	Resolution	Native audio
Veo 3.1 (Google)	Yes	Yes	Up to 3 reference images	4, 6 or 8 s (8 s at 1080p/4K or with references)	720p, 1080p, 4K	Yes
Kling 3.0 (Kuaishou)	Yes	Yes	Elements (2–4 images each; video elements in 3.0/Omni)	3–15 s, multi-shot	720P, 1080P, 4K (per Kling's API capability map)	Yes
Seedance 2.0 (ByteDance)	Yes	Check provider	Up to 9 images, 3 videos, 3 audio clips	Up to 15 s, multi-shot	Not stated in launch post	Yes (stereo)
Hailuo 2.3 (MiniMax)	Yes (required)	Not listed for 2.3	No	6 or 10 s at 768P; 6 s at 1080P	768P, 1080P	Check your provider
Wan 2.7 (Alibaba)	Yes	Yes	Driving audio; video continuation	2–15 s	720P, 1080P	Yes (generated or driven by your audio)

Veo 3.1: the polished single shot

Google's Gemini API documentation lists three image-driven modes for Veo 3.1. You can animate a starting image, interpolate between a first and a last frame, or use up to three reference images of a person, character or product to preserve its appearance. Output is 720p, 1080p or 4K at 24 fps, in 16:9 or 9:16, with audio generated automatically.

Good to know: 1080p, 4K and reference-image generations are fixed at 8 seconds. To go longer, you extend a Veo clip, but extension works only at 720p in the Gemini API. Google's docs now also present Gemini Omni Flash as the default video model for many workflows, keeping Veo 3.1 for extension and last-frame control. If you work in Google's ecosystem, it's worth a look too.

Pick it when: you need one beautiful shot, such as a product reveal or a cinematic establishing shot, at high resolution with synced sound.

Kling 3.0: one image, several shots

Kling's VIDEO 3.0 guide lists image-to-video, start-and-end-frame generation, and a combination of start frame plus element references. Elements are reusable assets built from 2–4 images of a character or object, so the subject in your still stays consistent when the model cuts to a new angle. Clips run 3–15 seconds, and multi-shot mode lets one generation contain several shots, planned automatically or by you shot by shot. Native audio supports several languages, dialects and accents.

Good to know: Kling's variants differ. In the API capability map, 3.0 Turbo is cheaper but doesn't support element control, so check which variant you're using.

Pick it when: your still is the start of a short story, like a character or product that should appear in a close-up, a wide shot and a pack shot within 15 seconds.

Seedance 2.0: the still as one ingredient among many

ByteDance's launch post describes Seedance 2.0 as accepting text, images, audio and video together. It can take up to 9 images, 3 video clips and 3 audio clips in one generation, and borrow composition, camera movement, motion rhythm and sound from them. Outputs are multi-shot audio-video clips up to 15 seconds, and the model supports extension and targeted editing.

Good to know: this flexibility rewards preparation. ByteDance's own post says detail stability, multi-subject consistency and text rendering still need work, and that real people's portraits used as references require identity verification or authorization.

Pick it when: you have more than a still, such as a reference clip for the camera move, a music track for timing, or several product angles.

Hailuo 2.3: motion and camera direction

MiniMax's API reference makes the first-frame image required for Hailuo 2.3 image-to-video. You get 6- or 10-second clips at 768P, or 6 seconds at 1080P. Its prompts support bracketed camera commands such as [Pan left] or [Push in], including sequences of commands, and MiniMax recommends at most three combined. MiniMax describes 2.3 as improving body movement, physical realism and facial micro-expressions, with better support for anime, illustration and game-CG styles. A Hailuo 2.3 Fast variant is also offered.

Good to know: input images need a short side over 300 px and an aspect ratio between 2:5 and 5:2, per MiniMax's docs.

Pick it when: you're animating characters or stylized art at volume and want explicit, repeatable camera moves.

Wan 2.7: frames, continuation and audio-driven video

Alibaba Cloud Model Studio documents wan2.7-i2v as handling first-frame-to-video, first-and-last-frame-to-video and video continuation through one API, at 720P or 1080P, 2–15 seconds, 30 fps. You can also supply driving audio (2–30 seconds) for lip-sync and action timing. Without it, the model generates matching music or sound effects. Output keeps the first frame's aspect ratio, so upload your still in the ratio you want.

Good to know: Alibaba's model list now also includes a newer Wan 3.0 video model. If you depend on Wan, check which version your tool offers.

Pick it when: you want to continue an existing clip, match a voice track, or move cleanly between two designed frames.

Recommendations by use case

You want to…	Start with	Then try
Animate a product photo into an 8 s hero shot	Veo 3.1	Kling 3.0
Turn one character image into a 15 s multi-shot scene	Kling 3.0	Seedance 2.0
Match a reference video's camera move	Seedance 2.0	Hailuo 2.3 (camera commands)
Morph from one designed frame to another	Veo 3.1 or Wan 2.7	Kling 3.0
Lip-sync a portrait to your own audio	Wan 2.7	Kling 3.0 Omni
Animate anime or illustration at volume	Hailuo 2.3	Kling 3.0

How to compare them on your own images

Use the same still, a similar prompt, and the same aspect ratio for each model. Generate a few takes each and count only clips you'd publish without regenerating. Then compare cost per usable clip.

Sora2 Hub makes this kind of test simpler. It's an AI image and video generator with many models on one credit balance, including Veo 3.1, Kling 3.0, Seedance 2.0, Hailuo and Wan for video (plus Nano Banana Pro and GPT Image 2 for making the still), so you can try several models without opening several accounts.

Bottom line

There's no single best image-to-video model, but there's a strong option for each kind of control. Veo 3.1 for frame-accurate, high-resolution shots. Kling 3.0 for multi-shot consistency. Seedance 2.0 for reference-driven work. Hailuo 2.3 for directed motion. Wan 2.7 for continuation and audio-driven clips.

Sources: Google Gemini API Veo 3.1 and video generation docs; Kling VIDEO 3.0 model guide, Element Library guide and API capability map; ByteDance Seed "Seedance 2.0 Official Launch"; MiniMax API reference (image-to-video) and Hailuo 2.3 announcement; Alibaba Cloud Model Studio Wan 2.7 image-to-video API reference and model list. Checked October 2026.