# Fal.ai Models Scraper (`automation-lab/fal-ai-models-scraper`) Actor Scrape all AI models from fal.ai including pricing, categories, and metadata - **URL**: https://apify.com/automation-lab/fal-ai-models-scraper.md - **Developed by:** [Stas Persiianenko](https://apify.com/automation-lab) (community) - **Categories:** AI - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing Pay per event This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## Fal.ai Models Scraper Extract the complete catalog of AI models from [fal.ai](https://fal.ai) — including pricing, categories, model labs, API endpoints, license types, and more. Monitor 1,300+ models across text-to-image, image-to-video, text-to-speech, and every other inference category on the platform. No browser required. Uses the official fal.ai JSON API with fast pagination and optional filtering by category or keyword. ### What does it do? This actor fetches every model listed on [fal.ai](https://fal.ai) using their paginated REST API. For each model it extracts: - 🆔 **Model path** — unique identifier (e.g. `fal-ai/flux/schnell`) - 📝 **Title** and **short description** - 📂 **Category** (text-to-image, image-to-video, speech-to-text, etc.) - 🏷️ **Tags** (e.g. realism, typography, open-source) - 🔗 **API URL** and **landing page URL** - 💰 **Pricing info** (cost per image/run as published by fal.ai) - 🏢 **Model lab / provider** (Google, Stability AI, Black Forest Labs, etc.) - 🔑 **License type** (commercial, open-source, etc.) - 🖥️ **Hosting type** (proxy, native) - 📅 **Published date** and **deprecation status** - 🖼️ **Thumbnail URL** and **GitHub URL** (when available) Pagination is handled automatically — all 34+ pages are fetched in sequence. You can cap the result count or filter by category or keyword. ### Who is it for? **AI developers & engineers** — Building on fal.ai or comparing it against Replicate, RunPod, or Hugging Face Inference? Scrape the full catalog programmatically to search, compare, and integrate without clicking through the web UI. **Competitor analysts & product managers** — Track which model labs (Google, Stability AI, Black Forest Labs) have the most models on fal.ai. Monitor pricing changes as new models launch. Export the full catalog to a spreadsheet or BI dashboard. **ML researchers** — Catalog model availability, license types, and API endpoints across inference providers. Correlate fal.ai model launches with paper releases or GitHub activity. **Automation builders** — Trigger workflows whenever new models are added in a specific category (e.g. text-to-video) using Apify schedules + Make/Zapier/n8n integrations. **Data aggregators** — Combine fal.ai model data with Replicate, Hugging Face, and Civitai data for a unified AI model catalog. ### Why use this actor? - ✅ **Zero browser overhead** — uses fal.ai's official JSON API, 10× faster than HTML scraping - ✅ **Full catalog** — fetches all 1,300+ models automatically across all pages - ✅ **Structured output** — clean JSON/CSV with 18 fields per model, ready for analysis - ✅ **Flexible filtering** — filter by category (text-to-image, image-to-video, etc.) or keyword before export - ✅ **No proxy required** — the fal.ai API is publicly accessible, no residential proxy cost - ✅ **No proxy required** — the fal.ai API is publicly accessible, so there are no residential proxy costs ### Data extracted | Field | Description | Example | |---|---|---| | `modelPath` | Unique model identifier | `fal-ai/flux/schnell` | | `title` | Display name | `FLUX.1 [schnell]` | | `category` | Inference category | `text-to-image` | | `tags` | Associated tags | `["open-source", "speed"]` | | `shortDescription` | One-line description | `Fast 4-step text-to-image model` | | `modelUrl` | API call endpoint | `https://fal.run/fal-ai/flux/schnell` | | `landingPageUrl` | Model landing page | `https://fal.ai/models/flux-schnell` | | `thumbnailUrl` | Preview image URL | `https://v3b.fal.media/...` | | `githubUrl` | GitHub repo (if available) | `https://github.com/black-forest-labs/flux` | | `licenseType` | License | `open-source` | | `status` | Availability | `public` | | `deprecated` | Is model deprecated? | `false` | | `publishedAt` | ISO 8601 publish date | `2024-08-01T12:00:00.000Z` | | `modelLab` | Creator / provider | `Black Forest Labs` | | `modelFamily` | Model family | `FLUX.1` | | `groupLabel` | Display group | `Image Generation` | | `hostingType` | How it's hosted | `proxy` | | `kind` | Model kind | `inference` | | `pricingInfo` | Pricing text | `$0.003 per image...` | ### How much does it cost to scrape fal.ai models? This is a pure HTTP actor — no browser, no proxies. Costs are very low. | Run type | Models scraped | FREE tier cost | DIAMOND tier cost | |---|---|---|---| | Quick sample | 50 models | ~$0.063 | ~$0.019 | | Full catalog | 1,300+ models | ~$1.50 | ~$0.37 | | Category only | ~50–200 models | ~$0.063–$0.24 | ~$0.019–$0.061 | Pricing uses a **pay-per-result** model: - 🚀 **Start fee**: $0.005 per run (one-time) - 📄 **Per model**: tiered pricing from $0.00115 (FREE tier) to $0.00028 (DIAMOND tier for high-volume users) Higher-volume Apify subscription tiers pay significantly less per result. DIAMOND users (highest volume) pay ~4× less per model than FREE users. ### How to scrape fal.ai models — step-by-step 1. Open the actor in [Apify Store](https://apify.com/automation-lab/fal-ai-models-scraper) 2. Click **Try for free** 3. Configure your inputs (or keep the defaults): - Set **Max models** to the number you want (default: 200; use 0 for all) - Optionally filter by **Category** (e.g. `text-to-image`) - Optionally set a **Keyword filter** (e.g. `stable diffusion`) 4. Click **Start** and wait ~5–30 seconds 5. Download results as **JSON**, **CSV**, or **Excel** ### Input parameters | Parameter | Type | Description | Default | |---|---|---|---| | `maxResults` | Integer | Maximum models to extract | 200 | | `category` | String | Filter by category (e.g. `text-to-image`) | All | | `searchQuery` | String | Keyword filter (matches title, description, tags, lab) | None | | `maxRequestRetries` | Integer | HTTP retry attempts | 3 | ### Output example ```json { "modelPath": "fal-ai/flux/schnell", "title": "FLUX.1 [schnell]", "category": "text-to-image", "tags": ["open-source", "speed"], "shortDescription": "FLUX.1 [schnell] is a 12B parameter rectified flow transformer for rapid text-to-image generation.", "modelUrl": "https://fal.run/fal-ai/flux/schnell", "landingPageUrl": "https://fal.ai/models/flux-schnell", "thumbnailUrl": "https://v3b.fal.media/files/flux/preview.jpg", "githubUrl": "https://github.com/black-forest-labs/flux", "licenseType": "open-source", "status": "public", "deprecated": false, "publishedAt": "2024-08-01T12:00:00.000Z", "modelLab": "Black Forest Labs", "modelFamily": "FLUX.1", "groupLabel": "Image Generation", "hostingType": "proxy", "kind": "inference", "pricingInfo": "Your request will cost $0.003 per image..." } ```` ### Tips for best results - 🔢 **Get the full catalog**: leave `category` empty and set `maxResults` to 2000 (the API has ~1,300 models as of 2026) - 📂 **Track a category**: set `category = text-to-video` and run on a weekly schedule to monitor new video models - 🔍 **Find models by provider**: set `searchQuery = Google` to get only Google-created models - 📉 **Find deprecated models**: download the full catalog and filter `deprecated = true` in your spreadsheet - 🔄 **Automate monitoring**: combine with Apify Schedules + Make/Zapier to get Slack/email alerts when new models appear ### Integrations #### Apify + Make (Zapier) Use this actor as a **scheduled trigger** in Make to monitor fal.ai for new model launches: 1. Create a Make scenario with the **Apify → Watch Dataset Items** trigger 2. Schedule the Fal.ai Models Scraper to run weekly 3. Add a filter: `publishedAt > last_run_date` 4. Send new model notifications to Slack, email, or Notion #### Google Sheets Export the full model catalog to Google Sheets for analysis: 1. Run the actor and download as **CSV** 2. Import to Google Sheets: **File → Import → Upload** 3. Use `=FILTER(A:A, J:J="text-to-image")` to slice by category 4. Add conditional formatting to highlight deprecated models (`K:K = TRUE`) #### Airtable / Notion Use the **Apify → Airtable** or **Apify → Notion** integrations in Make or Zapier to sync model data directly into a database view for team collaboration. #### Python pipeline ```python import requests run = requests.post( "https://api.apify.com/v2/acts/automation-lab~fal-ai-models-scraper/runs", headers={"Authorization": "Bearer YOUR_API_TOKEN"}, json={"maxResults": 500, "category": "text-to-image"} ).json() run_id = run["data"]["id"] ## Poll until done, then fetch data items = requests.get( f"https://api.apify.com/v2/actor-runs/{run_id}/dataset/items", headers={"Authorization": "Bearer YOUR_API_TOKEN"} ).json() ``` ### API usage #### Node.js ```javascript import { ApifyClient } from 'apify-client'; const client = new ApifyClient({ token: 'YOUR_API_TOKEN' }); const run = await client.actor('automation-lab/fal-ai-models-scraper').call({ maxResults: 500, category: 'text-to-image', }); const { items } = await client.dataset(run.defaultDatasetId).listItems(); console.log(`Extracted ${items.length} models`); ``` #### Python ```python from apify_client import ApifyClient client = ApifyClient("YOUR_API_TOKEN") run = client.actor("automation-lab/fal-ai-models-scraper").call(run_input={ "maxResults": 500, "category": "text-to-image", }) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item["title"], item["modelLab"]) ``` #### cURL ```bash curl -s -X POST \ "https://api.apify.com/v2/acts/automation-lab~fal-ai-models-scraper/runs" \ -H "Authorization: Bearer YOUR_API_TOKEN" \ -H "Content-Type: application/json" \ -d '{"maxResults": 100, "category": "text-to-image"}' \ | jq '.data.id' ``` ### Use with Claude (MCP) You can use this actor directly in Claude Desktop, Claude Code, or Cursor via the Apify MCP server: **Claude Code / terminal:** ```bash claude mcp add --transport http apify "https://mcp.apify.com?tools=automation-lab/fal-ai-models-scraper" ``` **Claude Desktop / Cursor / VS Code** — add to your MCP config: ```json { "mcpServers": { "apify": { "type": "http", "url": "https://mcp.apify.com?tools=automation-lab/fal-ai-models-scraper", "headers": { "Authorization": "Bearer YOUR_APIFY_TOKEN" } } } } ``` **Example prompts for Claude:** - *"Scrape all text-to-image models from fal.ai and show me the ones from Black Forest Labs"* - *"Get the full fal.ai model catalog and find all models with open-source licenses"* - *"Fetch fal.ai models filtered by category image-to-video and export a comparison table"* ### Legality and terms of service This actor uses fal.ai's publicly available REST API (`/api/models`) to fetch model metadata — the same data shown on the [fal.ai models page](https://fal.ai/models). No login is required and no personal data is collected. fal.ai's public API is intended for developers to programmatically access model information. Always review [fal.ai's Terms of Service](https://fal.ai/terms) and their robots.txt before automated use. This actor does not scrape user data, authentication tokens, or any non-public endpoints. Use of Apify's platform is subject to [Apify Terms of Service](https://apify.com/terms-of-use). Always use data responsibly and in compliance with applicable laws. ### FAQ #### How many models does fal.ai have? As of early 2026, fal.ai hosts over 1,300 models across 10+ inference categories. The catalog grows regularly as new models are added by labs like Google, Stability AI, Black Forest Labs, and others. #### Does this require proxies or login? No. The fal.ai `/api/models` endpoint is a public JSON API that requires no authentication or proxies. The actor makes plain HTTP requests. #### Can I filter to only non-deprecated models? The actor returns all non-private, non-removed models. After downloading, filter on `deprecated = false` in your spreadsheet or code to exclude deprecated models. #### Can I get all models in a specific category? Yes — set the `category` input to the desired category name (e.g. `text-to-image`, `image-to-video`). The supported categories are listed in the input dropdown. #### I got fewer results than expected. Why? If you set `maxResults` lower than the total catalog size, the actor stops early. Set `maxResults` to 2000 to ensure you get the full catalog. Models marked as removed or private are excluded from output. #### The run finished instantly with 0 results. What happened? If you set a `category` that does not exist on fal.ai (e.g. a typo), all models will be filtered out. Check the `category` input matches one of the available values in the dropdown. #### Does this actor stay up to date? The actor fetches live data from fal.ai on every run — there is no cached dataset. Schedule it weekly or monthly to keep your model catalog current. ### Related scrapers Looking for data from other AI model platforms? Check out our related actors: - [Replicate Models Scraper](https://apify.com/automation-lab/replicate-scraper) — models and pricing from Replicate - [Groq Models Scraper](https://apify.com/automation-lab/groq-models-scraper) — LLM models available on Groq - [Cloudflare Workers AI Scraper](https://apify.com/automation-lab/cloudflare-workers-ai-scraper) — models hosted on Cloudflare Workers AI - [Hugging Face Models Scraper](https://apify.com/automation-lab/huggingface-models-scraper) — open-source models on Hugging Face Hub # Actor input Schema ## `maxResults` (type: `integer`): Maximum number of models to extract. Leave empty to extract all models. ## `category` (type: `string`): Filter models by category. Leave empty to collect all categories. ## `searchQuery` (type: `string`): Filter models by keyword. Matches against title, description, tags, and model lab. Example: stable diffusion, Google, LoRA ## `maxRequestRetries` (type: `integer`): Number of retry attempts for failed HTTP requests. ## Actor input object example ```json { "maxResults": 20, "maxRequestRetries": 3 } ``` # Actor output Schema ## `overview` (type: `string`): No description # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "maxResults": 20, "category": "", "searchQuery": "", "maxRequestRetries": 3 }; // Run the Actor and wait for it to finish const run = await client.actor("automation-lab/fal-ai-models-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "maxResults": 20, "category": "", "searchQuery": "", "maxRequestRetries": 3, } # Run the Actor and wait for it to finish run = client.actor("automation-lab/fal-ai-models-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "maxResults": 20, "category": "", "searchQuery": "", "maxRequestRetries": 3 }' | apify call automation-lab/fal-ai-models-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=automation-lab/fal-ai-models-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Fal.ai Models Scraper", "description": "Scrape all AI models from fal.ai including pricing, categories, and metadata", "version": "0.1", "x-build-id": "qWzlxvCcMVQP46Rug" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/automation-lab~fal-ai-models-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-automation-lab-fal-ai-models-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/automation-lab~fal-ai-models-scraper/runs": { "post": { "operationId": "runs-sync-automation-lab-fal-ai-models-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/automation-lab~fal-ai-models-scraper/run-sync": { "post": { "operationId": "run-sync-automation-lab-fal-ai-models-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "properties": { "maxResults": { "title": "🔢 Max models", "minimum": 1, "type": "integer", "description": "Maximum number of models to extract. Leave empty to extract all models.", "default": 200 }, "category": { "title": "📂 Filter by category", "enum": [ "", "image-to-image", "text-to-image", "text-to-speech", "image-to-video", "text-to-video", "speech-to-text", "video-to-video", "image-to-3d", "text-to-3d", "text-to-music", "text-to-audio" ], "type": "string", "description": "Filter models by category. Leave empty to collect all categories." }, "searchQuery": { "title": "🔍 Keyword filter", "type": "string", "description": "Filter models by keyword. Matches against title, description, tags, and model lab. Example: stable diffusion, Google, LoRA" }, "maxRequestRetries": { "title": "🔄 Max request retries", "minimum": 1, "maximum": 10, "type": "integer", "description": "Number of retry attempts for failed HTTP requests.", "default": 3 } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```