# Dish Photo Matcher (No API Key): Food Photos to Menu Items (`traveleat/dish-photo-matcher-managed`) Actor

$0.2/1K photos | Match restaurant food and drink photos to your menu items with AI vision. Send photo URLs or a Google Maps place URL plus your menu; unmatched photos return null. No Gemini or Outscraper key. Not affiliated with Google.

- **URL**: https://apify.com/traveleat/dish-photo-matcher-managed.md
- **Developed by:** [Travel Eat](https://apify.com/traveleat) (community)
- **Categories:** AI, Travel
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $0.10 / 1,000 photo matcheds

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/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
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.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## 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.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

## Dish Photo Matcher (No API Key): Food Photos to Menu Items

$0.2/1K photos | Match restaurant food and drink photos to your menu items with AI vision. Send photo URLs or a Google Maps place URL plus your menu; unmatched photos return null. No Gemini or Outscraper key. Not affiliated with Google.

### Restaurant Dish Photo Matcher, Managed AI

Our matcher takes your menu and a set of restaurant food or drink photos, and uses AI vision to say which menu item, if any, each photo shows. You get one record per photo with the exact menu item name, or `null` when nothing is a clear match. **No Gemini or Outscraper key is needed**: the AI and the photo fetching run on our keys and their cost is included in the per-photo price. It is ideal for enriching menus with real dish photos, labelling unlabeled Google Maps photos and shortlisting guest uploads for review.

- Prefer to pay Google and Outscraper directly? [Dish Photo Matcher (No AI Markup): Food Photos to Menu Items](https://apify.com/traveleat/dish-photo-matcher) costs $0.10 per 1,000 photos plus your own provider usage.
- Need the menu first? [AI Restaurant Menu Parser (No API Key) + Allergens](https://apify.com/traveleat/ai-menu-parser-managed) turns menu photos into dish records this Actor accepts.
- Browse all our scrapers: https://apify.com/traveleat

#### Key Benefits

- No API key: no Google AI Studio or Outscraper signup, the AI and photo fetching are included.
- Low pricing: $0.20 per 1,000 photos, plus a $0.00005 start fee per run.
- Free to try: Start on the prefilled form returns a clearly marked DEMO row at no charge.
- Honest nulls: photos that are not clearly on your menu come back as `null` (and are billed like any analysed photo).
- Your menu is the label set: results join straight back onto your own menu items.

### How It Works

1. Provide your menu in `menuItems`: objects with at least a `name` (`originalName`, `category` and `description` improve matching), or plain strings.
2. Provide the photos: upload JPEG, PNG or WebP files, pass direct `photoUrls`, or set `placeUrl` to a Google Maps place and the Actor matches its "Food & drink" guest photos. When `photoUrls` is set it wins over `placeUrl`.
3. Run the Actor. Photos are matched in batches of 9. In our test, one pizza photo against a two-item menu finished in about 21 seconds.

More settings:

- `maxPhotos` - cap on photos matched, 1 to 100. Default is 9, which is one AI batch.

The prefilled form (Katz's Delicatessen and a two-item menu) is a deterministic preview: it returns one DEMO row, scrapes nothing, calls no model and bills nothing. Pass your own `photoUrls` or place for a live run.

Limits: AI matching can be wrong, so verify matches before publishing. We have not run a live Google Maps place lookup for this listing, so tell us on the Issues tab if a place returns fewer photos than expected.

**AI matching can be wrong.** Verify matches before publishing or using them in ordering workflows, and never treat a visual match as evidence of ingredients, allergens or dietary suitability.

#### Data You'll Receive

- Match: `photoUrl` (or `photoRef` for uploaded images), `dishName` (the exact menu item name, or `null`) and `matched` (true or false).
- Place: the Google Maps place URL used for the run.
- Record type: `MATCH` for live results; `DEMO` for the free preview and `DIAGNOSTIC` for an explained empty run.

#### Sample output data

Here is an example row from a live run of this Actor (one pizza photo against a two-item menu):

```json
{
  "recordType": "MATCH",
  "photoUrl": "https://upload.wikimedia.org/wikipedia/commons/a/a3/Eq_it-na_pizza-margherita_sep2005_sml.jpg",
  "photoRef": "https://upload.wikimedia.org/wikipedia/commons/a/a3/Eq_it-na_pizza-margherita_sep2005_sml.jpg",
  "dishName": "Margherita Pizza",
  "matched": true,
  "place": "https://www.google.com/maps/place/Katz's+Delicatessen,+205+E+Houston+St,+New+York"
}
```

### Dish Photo Matcher API

Results are stored in a dataset that you can export as CSV, JSON or Excel, or read on demand through the Apify API.

#### Support and Feedback

We improve these Actors from your feedback. If something breaks or you need a field:

- Open an issue on the Actor's Issues tab
- If this Actor saved you time, a short review on the Store helps other users find it.

### Run evidence

#### Start with one input and output

An Apify account is required. Use your Apify API token when calling the API; Console runs do not need that token pasted into the input. No user API-key field is required by this input schema. none; provider calls managed by Actor.

Free-tier event rates checked 2026-10-04: `actor-start` $0.00005; `result` $0.0002.

**Cost:** Total = Actor event charges + platform usage if separately billed + any BYO-provider charges. See “Current costs” for your event rates, plan tiers and a calculation. A sample does not promise future billable volume.

Input and output below were read from the same successful run `haAr6sUAAZszrvzWT` (2026-09-07, immutable build `j969njljdL483cTlX`). This is an observed example, not a guarantee that repeating it returns this row.

**Input JSON**

```json
{
  "menuItems": [
    {
      "name": "Margherita Pizza",
      "category": "Pizza"
    }
  ],
  "photoUrls": [
    "https://upload.wikimedia.org/wikipedia/commons/a/a3/Eq_it-na_pizza-margherita_sep2005_sml.jpg"
  ],
  "maxPhotos": 1
}
```

**Matching output excerpt**

Long strings and arrays are shortened for display; this is not a complete dataset fixture.

```json
{
  "recordType": "MATCH",
  "photoUrl": "https://upload.wikimedia.org/wikipedia/commons/a/a3/Eq_it-na_pizza-margherita_sep2005_sml.jpg",
  "photoRef": "https://upload.wikimedia.org/wikipedia/commons/a/a3/Eq_it-na_pizza-margherita_sep2005_sml.jpg",
  "dishName": "Margherita Pizza",
  "matched": true
}
```

Use AI vision to predict which supplied menu item, if any, appears in each
restaurant food or drink photo — no API key input or setup. Unmatched or
uncertain photos remain `null`. This actor joins menus and otherwise unlabeled
guest photos:

- **Input**: your menu (JSON array of items) + uploaded food/drink photos,
  direct `photoUrls`, or a Google Maps `placeUrl` whose "Food & drink" guest
  photos are fetched automatically.
- **Output**: one record per photo — `{photoUrl, dishName, matched}`, with
  `dishName` the exact menu item name or `null` when nothing matches.

More than a plain **Google Maps photos scraper**: instead of dumping raw
guest-photo URLs, AI vision proposes a menu-item match for each photo and
returns `null` when no supplied item is a clear match.

Photos are matched in batches of 9 by visual appearance; hallucinated names
(not on your menu) are filtered to `null` automatically. Ported from the
production photo-matching pipeline of a dietary-restrictions travel app.

#### What you can build with it

- **Menu enrichment** — propose guest-photo matches for dishes on a parsed
  menu (delivery apps, restaurant sites, food discovery apps).
- **Google Maps photo labeling** — turn an unlabeled pile of place photos
  into structured `photo → dish` data.
- **Menu photo review** — shortlist which menu items may appear in guest
  uploads for human verification.
- **Food image classification** against a *known* label set — unlike generic
  image-classification APIs, the label space is your menu, so results are
  directly usable.

#### No API keys needed

Photo fetching and AI matching both run on our managed keys, and their cost
is already included in the per-photo price. No Google AI Studio signup, no
Outscraper account, or API key input is needed.

#### Input example

```json
{
  "placeUrl": "https://www.google.com/maps/place/Katz's+Delicatessen,+205+E+Houston+St,+New+York",
  "menuItems": [
    {"name": "Pastrami on Rye", "category": "Sandwiches"},
    {"name": "Matzo Ball Soup", "category": "Soups"}
  ]
}
```

`menuItems` accepts plain strings too: `["Pastrami on Rye", "Matzo Ball Soup"]`.
Fields `originalName`, `category` and `description` improve match quality.
The untouched Console form is prefilled with Katz's Delicatessen as the
photo source. Running that exact default returns a deterministic row labelled
`recordType: "DEMO"` / `isDemo: true`; it performs no live photo lookup or AI
call and has no Actor event charge. Customize the input to start a live run.
The Console accepts local JPEG, PNG, or WebP uploads in `photoUrls`; API
callers may also provide URLs, base64 data URIs, or raw base64.
`photoUrl` remains a real sanitized HTTP(S) URL when one exists; inline images
use `photoUrl: null` plus a non-sensitive `photoRef` fingerprint.

#### Output example

| photoUrl | dishName | matched |
|---|---|---|
| `https://lh3.googleusercontent.com/p/AF1…` | Pastrami on Rye | true |
| `https://lh3.googleusercontent.com/p/AF2…` | Matzo Ball Soup | true |
| `https://lh3.googleusercontent.com/p/AF3…` | *null* | false |

Unmatched photos (interiors, receipts, dishes not on your menu) come back
with `dishName: null` — they are analyzed and billed like any other photo,
since confirming a non-match costs the same AI work as a match.

#### Chain it: scrape menu → match photos

The **[Google Maps Menu Scraper](https://apify.com/traveleat/google-maps-menu-scraper-managed)**
and **[AI Menu Parser](https://apify.com/traveleat/ai-menu-parser-managed)**
actors produce menu records this actor accepts directly:

1. Run Google Maps Menu Scraper on a place → structured `menuItems`.
2. Feed those items plus the same `placeUrl` into this actor.
3. Join rows with non-null `dishName`; unmatched dishes may still have no photo.

#### Pricing — what does a run cost?

Check this Actor’s **Pricing** tab for the current charges for your Apify plan. Actor-start and per-photo events apply to successful live output. AI and photo-fetching usage are included. Diagnostic-only runs incur neither event charge.

Prefer to pay Google and Outscraper directly and control your own quota?
The **[Restaurant Dish Photo Matcher (BYO keys)](https://apify.com/traveleat/dish-photo-matcher)**
Actor lets you supply `geminiApiKey` / `outscraperApiKey` and manage provider usage directly. Its per-photo fee is lower than this Actor's, but your providers bill you on top for the AI and photo lookups. Compare both Pricing tabs.

#### FAQ

**Why do some photos come back unmatched?**
Google Maps "Food & drink" galleries include drinks, interiors and dishes
that aren't on the menu you supplied. `matched: false` is a real answer, not
an error — filter on it.

**photoUrls vs placeUrl — which should I use?**
`photoUrls` if you have local photos or image URLs (from your own scrape or dataset).
`placeUrl` to let the actor fetch the place's guest food photos for you.
When both are set, `photoUrls` wins.

Full Google Maps URLs and `maps.app.goo.gl` short links are accepted; short
links are resolved only to trusted Google Maps destinations.

**How many photos can one run match?**
Up to 100 (`maxPhotos`, default 9). Values above 100 are clamped, not
rejected.

**What AI model does the matching?**
Gemini Flash-Lite vision — fast and accurate for appearance matching, and
its cost is included in the per-photo price.

**Can I run it on non-restaurant photos?**
It works on any photo-vs-item-list matching where appearance decides — but
it's tuned for food and drink.

**Can I rely on every match?**
No. AI photo matching can be wrong. Verify matches before publishing or using
them in ordering workflows, and never treat a visual match as evidence of
ingredients, allergens, or dietary suitability.

#### Support & links

- Questions, bug reports and FAQ:
  [github.com/Exdenta/traveleat-support](https://github.com/Exdenta/traveleat-support)
- This actor runs the same menu pipeline as [Travel Eat](https://traveleat.app) —
  scan a menu, get allergens, ingredients and dietary filters in 36 languages.

#### Product guides and related tools

- [Product overview and public links](https://jobatlas.dev/actors/directory/menus-and-photos#nomad-agent-dish-photo-matcher-managed)
- [Public setup guide](https://github.com/Exdenta/jobatlas/blob/main/docs/public-actors-setup.md)
- [Travel Eat menu API guide](https://traveleat.app/blog/ai-menu-parser-api)
- [Menu API documentation and support](https://github.com/Exdenta/traveleat-support)
- [Google Maps Menu Scraper — Allergens, Nutrition, No API Key](https://apify.com/traveleat/google-maps-menu-scraper-managed)
- [AI Restaurant Menu Parser — Dishes & Allergens, No API Key](https://apify.com/traveleat/ai-menu-parser-managed)

#### Current costs

Current Actor event rates, checked 2026-10-04. USD; use the **Pricing** tab for your plan and later changes.

| Event | Free | Bronze | Silver | Gold | Platinum | Diamond |
|---|---:|---:|---:|---:|---:|---:|
| Actor start (`actor-start`) | $0.00005 | $0.00005 | $0.00005 | $0.00005 | $0.00005 | $0.00005 |
| Photo matched (`result`) | $0.0002 | $0.0002 | $0.0002 | $0.0002 | $0.0002 | $0.0002 |

For example, 1 × `actor-start`, 100 × `result` totals **$0.02005 in Actor event charges on Free**. This is an arithmetic example, before any waiver; actual charged events depend on the run and selected options. It is not a promise of output or a charge for simply setting an item cap.

Your total is the charged Actor events, plus platform usage **only if this Actor’s Pricing tab bills it separately**, plus any external provider you use with your own key. Do not add platform usage twice when it is included. Apify credits and plan discounts affect your invoice. Set a run cost limit in Console before starting.

#### Agent setup skill

Use the maintained [SKILL.md](https://github.com/Exdenta/jobatlas/blob/main/.agents/skills/public-apify-actors/SKILL.md) to configure the documented inputs, select `latest`, record the immutable build and distinguish source results from preview or diagnostic rows. Check the current Pricing tab before starting a run.

***

**Independent tool.** We built and run this Actor ourselves. Google does not operate, sponsor or endorse it, and its name is used only to say which service the data is processed with. We are not affiliated with Google.

# Actor input Schema

## `menuItems` (type: `array`):

The restaurant's menu as a JSON array. Each item needs at least <code>name</code>; <code>originalName</code>, <code>category</code> and <code>description</code> improve match quality. Plain strings also work. Tip: the <b>Google Maps Menu Scraper</b> / <b>AI Menu Parser</b> actors produce compatible records.

## `photoUrls` (type: `array`):

Upload food/drink photos or provide direct image URLs (JPEG/PNG/WebP, 10 MiB each). API/JSON callers may also pass base64 data URIs or raw base64 strings. Leave empty to scrape photos from the Google Maps place below instead.

## `placeUrl` (type: `string`):

A full Google Maps place URL or maps.app.goo.gl short link; bare place IDs and names are also accepted. Its "Food & drink" guest photos are scraped and matched. Ignored when photoUrls is set.

## `maxPhotos` (type: `integer`):

Cap on how many photos are matched. The default is one 9-photo AI batch; increase it up to 100 for broader coverage and a higher run cost.

## Actor input object example

```json
{
  "menuItems": [
    {
      "name": "Pastrami on Rye",
      "category": "Sandwiches"
    },
    {
      "name": "Matzo Ball Soup",
      "category": "Soups"
    }
  ],
  "placeUrl": "https://www.google.com/maps/place/Katz's+Delicatessen,+205+E+Houston+St,+New+York",
  "maxPhotos": 9
}
```

# Actor output Schema

## `items` (type: `string`):

The run's dataset items (see the dataset schema for the field-by-field structure).

# 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 '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "menuItems": [
        {
            "name": "Pastrami on Rye",
            "category": "Sandwiches"
        },
        {
            "name": "Matzo Ball Soup",
            "category": "Soups"
        }
    ],
    "placeUrl": "https://www.google.com/maps/place/Katz's+Delicatessen,+205+E+Houston+St,+New+York",
    "maxPhotos": 9
};

// Run the Actor and wait for it to finish
const run = await client.actor("traveleat/dish-photo-matcher-managed").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 '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {
    "menuItems": [
        {
            "name": "Pastrami on Rye",
            "category": "Sandwiches",
        },
        {
            "name": "Matzo Ball Soup",
            "category": "Soups",
        },
    ],
    "placeUrl": "https://www.google.com/maps/place/Katz's+Delicatessen,+205+E+Houston+St,+New+York",
    "maxPhotos": 9,
}

# Run the Actor and wait for it to finish
run = client.actor("traveleat/dish-photo-matcher-managed").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "menuItems": [
    {
      "name": "Pastrami on Rye",
      "category": "Sandwiches"
    },
    {
      "name": "Matzo Ball Soup",
      "category": "Soups"
    }
  ],
  "placeUrl": "https://www.google.com/maps/place/Katz'\''s+Delicatessen,+205+E+Houston+St,+New+York",
  "maxPhotos": 9
}' |
apify call traveleat/dish-photo-matcher-managed --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,traveleat/dish-photo-matcher-managed"
        }
    }
}
```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/ICcdN02wb383RvZgI/builds/6qBYdAabhO9oSsxT2/openapi.json