Vibecode Runway
track this build5 steps, step by step0%NOT REALLY · don't bother
price $15/moyou'd save $180/yrbuild time not realistically solocategory 🎞️ ai videoreplaced by 0 people
A UI around video models is buildable; the video-generation model quality, compute, safety, editing stack, and continuous research are not a solo afternoon project.
the project pack4 files · fastest path to a working personal build
You are building a lean indie version of Runway. Create the following project files first, then implement the application by following them. Keep the files updated as decisions change. Do not collapse this into a single README or prompt. ===== README.md ===== # Runway indie build ## Goal Build the smallest trustworthy replacement for the core Runway workflow for one developer or a tiny team. ## Scope Wrap open-source/video APIs to generate or edit clips, manage credits, and export assets. ## Quick start 1. Install the documented dependencies. 2. Copy `.env.example` to `.env`. 3. Run the development command chosen during implementation. 4. Complete the acceptance checks in `BUILD_PLAN.md`. ## Honest limits This build deliberately does not replace: - frontier video models - compute - asset history - model updates - commercial rights workflows - editing polish If those capabilities are essential, use diffusers instead of pretending the gap is solved. ===== AGENTS.md ===== # Agent instructions - Optimize for a working, understandable weekend build. - Prefer the fewest moving parts that satisfy the brief. - Do not invent cryptography, security guarantees, APIs, or compliance claims. - Keep secrets out of source control and logs. - Add focused tests for destructive, security-sensitive, and data-loss paths. - Run the project checks before declaring the build complete. - Record any deliberate shortcut in the README under "Tradeoffs". ===== BUILD_PLAN.md ===== # Build plan ## Original build brief Build me a thin video-generation workbench over hosted models, in place of Runway. Requirements: - A local web page on localhost:3300: Node + Express, a prompt box, a model picker, and a gallery of past generations. - Generate via the Replicate API (token in .env) against current open video models (Wan/LTX/Hunyuan class). Pick one provider and stick to it; fal.ai only if it has a model Replicate lacks. - Poll the job, download the mp4 to ~/generations/YYYY-MM-DD/, and record prompt, model, params, cost, and file path in SQLite (better-sqlite3). - Image-to-video: upload a still and pass it as the conditioning frame. - Show a month-to-date spend total computed from per-job cost so usage stays honest. - No accounts, no telemetry; prompts and outputs stay local except the API calls themselves. - Out of scope: editing timelines, inpainting, and anything realtime. Do not build model hosting or a job queue beyond simple polling. - README: where to get the Replicate token, rough dollars per second of video on the default model, and a note that this rents models rather than replacing Runway, whose frontier video models and compute cannot be rebuilt solo. ## Required capabilities - GPU/hosted video API - storage - video rendering - safety filters - prompt/workflow UI ## Delivery order 1. Scaffold the smallest runnable application and document its commands. 2. Implement the primary data model and core workflow. 3. Add validation, safe failure states, and persistence. 4. Cover the critical path with automated tests. 5. Exercise a clean install from the README and fix every missing step. ## Done when - A new user can go from clone to first successful workflow using only the README. - The core workflow works without paid infrastructure unless the brief requires it. - Tests cover the highest-risk behavior. - Known limitations are explicit rather than hidden. ===== .env.example ===== # Copy to .env and document every variable when it is introduced. # Never put real credentials in this file. APP_ENV=development # Add only values required by the selected implementation.
You are building a lean indie version of Runway. Create the following project files first, then implement the application by following them. Keep the files updated as decisions change. Do not collapse this into a single README or prompt. ===== README.md ===== # Runway indie build ## Goal Build the smallest trustworthy replacement for the core Runway workflow for one developer or a tiny team. ## Scope Wrap open-source/video APIs to generate or edit clips, manage credits, and export assets. ## Quick start 1. Install the documented dependencies. 2. Copy `.env.example` to `.env`. 3. Run the development command chosen during implementation. 4. Complete the acceptance checks in `BUILD_PLAN.md`. ## Honest limits This build deliberately does not replace: - frontier video models - compute - asset history - model updates - commercial rights workflows - editing polish If those capabilities are essential, use diffusers instead of pretending the gap is solved. ===== AGENTS.md ===== # Agent instructions - Optimize for a working, understandable weekend build. - Prefer the fewest moving parts that satisfy the brief. - Do not invent cryptography, security guarantees, APIs, or compliance claims. - Keep secrets out of source control and logs. - Add focused tests for destructive, security-sensitive, and data-loss paths. - Run the project checks before declaring the build complete. - Record any deliberate shortcut in the README under "Tradeoffs". ===== BUILD_PLAN.md ===== # Build plan ## Original build brief Build me a thin video-generation workbench over hosted models, in place of Runway. Requirements: - A local web page on localhost:3300: Node + Express, a prompt box, a model picker, and a gallery of past generations. - Generate via the Replicate API (token in .env) against current open video models (Wan/LTX/Hunyuan class). Pick one provider and stick to it; fal.ai only if it has a model Replicate lacks. - Poll the job, download the mp4 to ~/generations/YYYY-MM-DD/, and record prompt, model, params, cost, and file path in SQLite (better-sqlite3). - Image-to-video: upload a still and pass it as the conditioning frame. - Show a month-to-date spend total computed from per-job cost so usage stays honest. - No accounts, no telemetry; prompts and outputs stay local except the API calls themselves. - Out of scope: editing timelines, inpainting, and anything realtime. Do not build model hosting or a job queue beyond simple polling. - README: where to get the Replicate token, rough dollars per second of video on the default model, and a note that this rents models rather than replacing Runway, whose frontier video models and compute cannot be rebuilt solo. ## Required capabilities - GPU/hosted video API - storage - video rendering - safety filters - prompt/workflow UI ## Delivery order 1. Scaffold the smallest runnable application and document its commands. 2. Implement the primary data model and core workflow. 3. Add validation, safe failure states, and persistence. 4. Cover the critical path with automated tests. 5. Exercise a clean install from the README and fix every missing step. ## Done when - A new user can go from clone to first successful workflow using only the README. - The core workflow works without paid infrastructure unless the brief requires it. - Tests cover the highest-risk behavior. - Known limitations are explicit rather than hidden. ===== .env.example ===== # Copy to .env and document every variable when it is introduced. # Never put real credentials in this file. APP_ENV=development # Add only values required by the selected implementation.
You are building a production product version of Runway. Create the following project files first, then implement the application by following them. Keep the files updated as decisions change. Do not collapse this into a single README or prompt. ===== PRODUCT.md ===== # Runway product brief ## Problem A UI around video models is buildable; the video-generation model quality, compute, safety, editing stack, and continuous research are not a solo afternoon project. ## Product outcome Wrap open-source/video APIs to generate or edit clips, manage credits, and export assets. ## Target user A serious builder who needs a maintainable product foundation rather than a one-off demo. ## Required capabilities - GPU/hosted video API - storage - video rendering - safety filters - prompt/workflow UI ## Explicit non-goals for v1 - frontier video models - compute - asset history - model updates - commercial rights workflows - editing polish ## Success criteria - The primary workflow is measurable end to end. - Setup is reproducible in a clean environment. - Failure, recovery, and support paths are documented. - Product claims match what the implementation actually guarantees. ===== ARCHITECTURE.md ===== # Architecture ## Starting brief Build me a thin video-generation workbench over hosted models, in place of Runway. Requirements: - A local web page on localhost:3300: Node + Express, a prompt box, a model picker, and a gallery of past generations. - Generate via the Replicate API (token in .env) against current open video models (Wan/LTX/Hunyuan class). Pick one provider and stick to it; fal.ai only if it has a model Replicate lacks. - Poll the job, download the mp4 to ~/generations/YYYY-MM-DD/, and record prompt, model, params, cost, and file path in SQLite (better-sqlite3). - Image-to-video: upload a still and pass it as the conditioning frame. - Show a month-to-date spend total computed from per-job cost so usage stays honest. - No accounts, no telemetry; prompts and outputs stay local except the API calls themselves. - Out of scope: editing timelines, inpainting, and anything realtime. Do not build model hosting or a job queue beyond simple polling. - README: where to get the Replicate token, rough dollars per second of video on the default model, and a note that this rents models rather than replacing Runway, whose frontier video models and compute cannot be rebuilt solo. ## Boundaries Separate the product into replaceable modules for interface, application logic, persistence, external integrations, and operational concerns. Keep domain logic independent from delivery frameworks and vendors. ## Production baseline - Configuration: validated at startup with safe local defaults where possible. - Security: least privilege, input validation, secret redaction, rate limits on abuse-prone paths, and no invented security primitives. - Data: explicit schema and migrations, transactional writes where integrity matters, backup and restore instructions. - Integrations: adapters around third-party providers, idempotent webhook or job processing, bounded retries, and timeouts. - Observability: structured logs with request or operation IDs, an error-tracking hook, and health/readiness checks where a server exists. - Quality: unit tests for domain rules, integration tests at module boundaries, and one end-to-end critical-path test. ## Decision records For each major dependency, document why it was chosen, its failure mode, and how it can be replaced. Do not introduce infrastructure until a requirement justifies it. ===== AGENTS.md ===== # Agent instructions - Read `PRODUCT.md` and `ARCHITECTURE.md` before changing code. - Implement milestone by milestone; keep each change reviewable and leave the application runnable. - Treat authentication, payments, encryption, imports, webhooks, and destructive actions as high-risk boundaries when present. - Never invent cryptography or silently weaken a requirement to make a test pass. - Use provider interfaces for external services and deterministic fakes in tests. - Add migrations and rollback or recovery notes for persistent data changes. - Log useful operational context without credentials, tokens, passwords, or personal data. - Update documentation and run all checks before completing a milestone. ===== MILESTONES.md ===== # Delivery milestones ## M0 — Decisions and scaffold - Confirm the runtime, persistence model, threat boundaries, and deployment target. - Create a reproducible local environment and continuous checks. ## M1 — Core workflow - Implement the smallest end-to-end product path with validation and tests. - Keep integrations behind interfaces. ## M2 — Trust layer - Add secure failure behavior, recovery paths, audit-relevant events, and data safeguards. - Test abuse cases and destructive operations. ## M3 — Operability - Add structured logs, error reporting hooks, health signals, backup/restore documentation, and deployment configuration. ## M4 — Release gate - Run a clean-install test, critical-path end-to-end test, dependency review, and documented rollback exercise. - Compare the shipped behavior with `PRODUCT.md` and publish remaining limitations. ===== OPERATIONS.md ===== # Operations ## Before release - Validate configuration and secrets at startup. - Define backup, restore, and rollback procedures and test them. - Document logs, error tracking, health signals, and alert ownership. - Set dependency update and vulnerability review expectations. ## Incident checklist 1. Contain the issue without destroying evidence or user data. 2. Record the timeline and affected scope. 3. Rotate exposed secrets and revoke compromised sessions or credentials. 4. Restore from a verified source when needed. 5. Document the root cause, remediation, and regression test. ## Launch constraint Do not market omitted Runway capabilities as implemented. The v1 non-goals in `PRODUCT.md` remain user-visible limitations until they are deliberately delivered.
README.md
# Runway indie build ## Goal Build the smallest trustworthy replacement for the core Runway workflow for one developer or a tiny team. ## Scope Wrap open-source/video APIs to generate or edit clips, manage credits, and export assets. ## Quick start 1. Install the documented dependencies. 2. Copy `.env.example` to `.env`. 3. Run the development command chosen during implementation. 4. Complete the acceptance checks in `BUILD_PLAN.md`. ## Honest limits This build deliberately does not replace: - frontier video models - compute - asset history - model updates - commercial rights workflows - editing polish If those capabilities are essential, use diffusers instead of pretending the gap is solved.
AGENTS.md
# Agent instructions - Optimize for a working, understandable weekend build. - Prefer the fewest moving parts that satisfy the brief. - Do not invent cryptography, security guarantees, APIs, or compliance claims. - Keep secrets out of source control and logs. - Add focused tests for destructive, security-sensitive, and data-loss paths. - Run the project checks before declaring the build complete. - Record any deliberate shortcut in the README under "Tradeoffs".
BUILD_PLAN.md
# Build plan ## Original build brief Build me a thin video-generation workbench over hosted models, in place of Runway. Requirements: - A local web page on localhost:3300: Node + Express, a prompt box, a model picker, and a gallery of past generations. - Generate via the Replicate API (token in .env) against current open video models (Wan/LTX/Hunyuan class). Pick one provider and stick to it; fal.ai only if it has a model Replicate lacks. - Poll the job, download the mp4 to ~/generations/YYYY-MM-DD/, and record prompt, model, params, cost, and file path in SQLite (better-sqlite3). - Image-to-video: upload a still and pass it as the conditioning frame. - Show a month-to-date spend total computed from per-job cost so usage stays honest. - No accounts, no telemetry; prompts and outputs stay local except the API calls themselves. - Out of scope: editing timelines, inpainting, and anything realtime. Do not build model hosting or a job queue beyond simple polling. - README: where to get the Replicate token, rough dollars per second of video on the default model, and a note that this rents models rather than replacing Runway, whose frontier video models and compute cannot be rebuilt solo. ## Required capabilities - GPU/hosted video API - storage - video rendering - safety filters - prompt/workflow UI ## Delivery order 1. Scaffold the smallest runnable application and document its commands. 2. Implement the primary data model and core workflow. 3. Add validation, safe failure states, and persistence. 4. Cover the critical path with automated tests. 5. Exercise a clean install from the README and fix every missing step. ## Done when - A new user can go from clone to first successful workflow using only the README. - The core workflow works without paid infrastructure unless the brief requires it. - Tests cover the highest-risk behavior. - Known limitations are explicit rather than hidden.
.env.example
# Copy to .env and document every variable when it is introduced. # Never put real credentials in this file. APP_ENV=development # Add only values required by the selected implementation.
PRODUCT.md
# Runway product brief ## Problem A UI around video models is buildable; the video-generation model quality, compute, safety, editing stack, and continuous research are not a solo afternoon project. ## Product outcome Wrap open-source/video APIs to generate or edit clips, manage credits, and export assets. ## Target user A serious builder who needs a maintainable product foundation rather than a one-off demo. ## Required capabilities - GPU/hosted video API - storage - video rendering - safety filters - prompt/workflow UI ## Explicit non-goals for v1 - frontier video models - compute - asset history - model updates - commercial rights workflows - editing polish ## Success criteria - The primary workflow is measurable end to end. - Setup is reproducible in a clean environment. - Failure, recovery, and support paths are documented. - Product claims match what the implementation actually guarantees.
ARCHITECTURE.md
# Architecture ## Starting brief Build me a thin video-generation workbench over hosted models, in place of Runway. Requirements: - A local web page on localhost:3300: Node + Express, a prompt box, a model picker, and a gallery of past generations. - Generate via the Replicate API (token in .env) against current open video models (Wan/LTX/Hunyuan class). Pick one provider and stick to it; fal.ai only if it has a model Replicate lacks. - Poll the job, download the mp4 to ~/generations/YYYY-MM-DD/, and record prompt, model, params, cost, and file path in SQLite (better-sqlite3). - Image-to-video: upload a still and pass it as the conditioning frame. - Show a month-to-date spend total computed from per-job cost so usage stays honest. - No accounts, no telemetry; prompts and outputs stay local except the API calls themselves. - Out of scope: editing timelines, inpainting, and anything realtime. Do not build model hosting or a job queue beyond simple polling. - README: where to get the Replicate token, rough dollars per second of video on the default model, and a note that this rents models rather than replacing Runway, whose frontier video models and compute cannot be rebuilt solo. ## Boundaries Separate the product into replaceable modules for interface, application logic, persistence, external integrations, and operational concerns. Keep domain logic independent from delivery frameworks and vendors. ## Production baseline - Configuration: validated at startup with safe local defaults where possible. - Security: least privilege, input validation, secret redaction, rate limits on abuse-prone paths, and no invented security primitives. - Data: explicit schema and migrations, transactional writes where integrity matters, backup and restore instructions. - Integrations: adapters around third-party providers, idempotent webhook or job processing, bounded retries, and timeouts. - Observability: structured logs with request or operation IDs, an error-tracking hook, and health/readiness checks where a server exists. - Quality: unit tests for domain rules, integration tests at module boundaries, and one end-to-end critical-path test. ## Decision records For each major dependency, document why it was chosen, its failure mode, and how it can be replaced. Do not introduce infrastructure until a requirement justifies it.
AGENTS.md
# Agent instructions - Read `PRODUCT.md` and `ARCHITECTURE.md` before changing code. - Implement milestone by milestone; keep each change reviewable and leave the application runnable. - Treat authentication, payments, encryption, imports, webhooks, and destructive actions as high-risk boundaries when present. - Never invent cryptography or silently weaken a requirement to make a test pass. - Use provider interfaces for external services and deterministic fakes in tests. - Add migrations and rollback or recovery notes for persistent data changes. - Log useful operational context without credentials, tokens, passwords, or personal data. - Update documentation and run all checks before completing a milestone.
MILESTONES.md
# Delivery milestones ## M0 — Decisions and scaffold - Confirm the runtime, persistence model, threat boundaries, and deployment target. - Create a reproducible local environment and continuous checks. ## M1 — Core workflow - Implement the smallest end-to-end product path with validation and tests. - Keep integrations behind interfaces. ## M2 — Trust layer - Add secure failure behavior, recovery paths, audit-relevant events, and data safeguards. - Test abuse cases and destructive operations. ## M3 — Operability - Add structured logs, error reporting hooks, health signals, backup/restore documentation, and deployment configuration. ## M4 — Release gate - Run a clean-install test, critical-path end-to-end test, dependency review, and documented rollback exercise. - Compare the shipped behavior with `PRODUCT.md` and publish remaining limitations.
OPERATIONS.md
# Operations ## Before release - Validate configuration and secrets at startup. - Define backup, restore, and rollback procedures and test them. - Document logs, error tracking, health signals, and alert ownership. - Set dependency update and vulnerability review expectations. ## Incident checklist 1. Contain the issue without destroying evidence or user data. 2. Record the timeline and affected scope. 3. Rotate exposed secrets and revoke compromised sessions or credentials. 4. Restore from a verified source when needed. 5. Document the root cause, remediation, and regression test. ## Launch constraint Do not market omitted Runway capabilities as implemented. The v1 non-goals in `PRODUCT.md` remain user-visible limitations until they are deliberately delivered.
$ choose a build depth, inspect the files, then open the complete pack in your agent · this prompt is generated from the build plan · improve it via PR
They pay because generative video quality and inference infrastructure are the product.
xfrontier video models
xcompute
xasset history
xmodel updates
xcommercial rights workflows
xediting polish
share on X ↗"I just replaced Runway ($15/mo) with one prompt"
Don't feel like building it? These folks already made it free.
ComfyUIImages, video, masking, upscaling and effects in one graph; it replaces tools, not Runway's polish.open source↗
SwarmUIA friendlier shell around local image and video workflows; editing remains thinner than Runway's.open source↗
LTX DesktopThe closest free thing to a generative video suite: local models, retakes and an actual editing timeline.open source↗all 6 free alternatives to Runway →· no votes, no pay-to-list · just what's real
Runway pricing
| plan | monthly | annual (per mo) | what you get |
|---|---|---|---|
| free | $0 | $0 | 125 one-time credits and 5 GB storage. |
| standard | $15 | $12 | 625 credits/month, shown as about 52 seconds of Gen-4.5; watermark removal and 4K upscale. |
| pro | $35 | $28 | 2,250 credits/month, shown as about 187 seconds of Gen-4.5; 500 GB storage. |
| max | $95 | $76 | 9,500 credits/month, shown as about 791 seconds of Gen-4.5; unused included credits can roll over for 1 month. |
| enterprise | custom | — | Custom credits, seats, security and support; public price is not listed. |
free tier125 credits once, not monthly, plus 5 GB storage.
billingmonthly + annual (-20%)
hidden costsStandard and Pro included credits do not roll over; Max rolls them for only one month. Extra credit purchases start at 1,000 credits and do not expire.
verified 2026-08-14 · source ↗
Vibecode Runway
Not really. Runway's value is not the code: . See the honest breakdown above.
How much does Runway cost?
Runway costs about $15/month (Standard, checked 2026-07-30), which is $180 per year.
What do I lose by replacing Runway?
Honestly: frontier video models; compute; asset history; model updates; commercial rights workflows; editing polish. If any of those are load-bearing for you, keep paying.
Is there an open-source alternative to Runway?
Yes: ComfyUI (Images, video, masking, upscaling and effects in one graph; it replaces tools, not Runway's polish.) SwarmUI (A friendlier shell around local image and video workflows; editing remains thinner than Runway's.) LTX Desktop (The closest free thing to a generative video suite: local models, retakes and an actual editing timeline.) All 6 curated free alternatives are at vibecodeit.com/runway/alternatives. The prompt is for when you want it exactly your way.