Pull rich wine data from Vivino by name, vintage, or URL — ratings, reviews, taste profiles, food pairings, grape varieties, region, and live merchant prices in one flat record per wine. No API key, no account required.

Whether you're building a wine recommendation engine, tracking prices across vintages, or enriching a product catalog, this Vivino scraper delivers 31 structured fields per wine in seconds.

Features

• Name or URL input — pass free-text queries like "Caymus Cabernet Sauvignon 2019", bare names like "Cloudy Bay Sauvignon Blanc", or direct Vivino wine URLs (https://www.vivino.com/w/12345).
• Year-aware matching — "Caymus 2019" resolves the 2019 vintage specifically; a query without a year returns the top current vintage.
• 31-field output — catalog fields, community ratings, taste structure, food pairings, grape varieties, live merchant price, and up to 200 community reviews per wine.
• Live prices, localized — set shipTo to your target country (US, GB, FR, DE, …) and the actor fetches prices in the local currency via a residential proxy exit in that country.
• No API key needed — reads Vivino's public pages; runs on a residential proxy for reliable access out of the box.
• Pay per result — you are charged only for wines that successfully resolve and return data ($0.0025 / wine).
• Configurable depth — toggle taste profiles, food pairings, and community reviews on or off; cap reviews per wine (0–200).
• Free preview — free Apify plan users receive up to 5 wine records to evaluate data quality before committing to a paid run.

What data you get

One record per wine, all fields populated on a verified cloud run (parse_confidence = 1.0):

Field	Description
name	Full wine name (winery + label)
winery	Winery / producer name
vintage	Vintage year (integer)
wine_type	Red, White, Sparkling, Rosé, Dessert, Fortified
region	Region name (English)
country	Country of origin
appellation	Appellation / sub-region label
grape_varieties	List of grape variety names
average_rating	Community average rating for this vintage (0–5)
ratings_count	Number of community ratings for this vintage
wine_average_rating	All-vintage average rating for the wine label
wine_ratings_count	All-vintage ratings count
price	Live merchant price (numeric)
currency	Currency code (USD, EUR, GBP, …)
discount_percent	Discount off the reference price, if any
taste_profile	Object: body, acidity, tannins, sweetness, fizziness
food_pairings	List of recommended food pairing names
wine_description	Editorial description / tasting notes (plain text)
alcohol	Alcohol by volume (%)
image_url	Bottle/label image URL
reviews	List of community reviews (rating, note, user, date)
vivino_url	Canonical Vivino URL for this wine
wineId	Vivino internal wine ID
vintageId	Vivino internal vintage ID
shipTo	Country code used for price localisation
query	Your original input query (for traceability)
parse_confidence	Data completeness score (1.0 = all fields populated)
warnings	List of any missing-field warnings
scraped_at	ISO-8601 timestamp of the run

Input

Required field: wines — a list of wine names, search queries, or Vivino URLs.

{
  "wines": [
    "Caymus Cabernet Sauvignon 2019",
    "Cloudy Bay Sauvignon Blanc",
    "Sassicaia 2018",
    "https://www.vivino.com/w/1140610"
  ],
  "shipTo": "US",
  "currency": "USD",
  "includeTasteProfile": true,
  "includeReviews": true,
  "maxReviewsPerWine": 20
}

Parameter	Type	Default	Description
wines	list of strings	(required)	Wine names, queries, or Vivino URLs
shipTo	string	"US"	Two-letter country code for price localisation
currency	string	auto	Three-letter currency code; auto-selected from shipTo if omitted
includeTasteProfile	boolean	true	Fetch taste structure and food pairings
includeReviews	boolean	true	Fetch community reviews
maxReviewsPerWine	integer	20	Reviews per wine (0–200)

Pricing

This actor uses pay-per-result billing — you are charged only when a wine is successfully resolved and written to the dataset.

Event	Price
wine-result — one wine record delivered	$0.0025

That is $2.50 per 1,000 wines, or roughly $1 per 400 wines. Queries that return no match are not charged. This is approximately 17% cheaper than the comparable market leader on the Apify Store.

Apify platform compute costs (typically a few cents per run) are billed separately and are covered by your Apify plan.

FAQ

Do I need a Vivino API key or account? No. The actor reads Vivino's public pages and runs on a residential proxy for reliable access. You do not need to create a Vivino account or obtain any credentials.

How does year/vintage matching work? If your query includes a year — e.g. "Opus One 2017" — the actor resolves that specific vintage and returns its ratings, price, and reviews. If no year is given — e.g. "Opus One" — the actor returns the top current vintage Vivino ranks for that label.

Can I pass Vivino URLs directly? Yes. Any item in the wines list that contains /w/ is treated as a direct Vivino wine URL and skips the search step entirely. This is the most precise way to target a specific wine.

What countries and currencies are supported for pricing? The shipTo field accepts any two-letter country code. Common ones include US, GB, FR, DE, ES, IT, AU, CA, JP, BR, CH, SE. The currency auto-selects to the local default (USD for US, EUR for Eurozone countries, GBP for GB, etc.) but can be overridden with the currency parameter.

Use cases

• Wine retail & e-commerce — enrich your product catalog with Vivino ratings, descriptions, grape varieties, and food pairing suggestions sourced from the world's largest wine community.
• Price monitoring — track live merchant prices across vintages and geographies; compare your own pricing against Vivino's merchant data.
• Wine recommendation engines — build or feed ML models with structured taste profiles (body, acidity, tannins, sweetness) and food pairing data across thousands of wines.
• Market research & competitive intelligence — analyze rating distributions, vintage quality trends, and regional price points for a segment of the market.
• Content platforms & apps — auto-populate tasting notes, food pairing guides, and winery profiles from verified community data.
• AI agents & LLM pipelines — supply structured wine data to an LLM for sommelier-style Q&A, menu generation, or natural-language product descriptions.

Usage statistics

This Actor creates a small, content-free summary at the end of each run. It is used only to monitor reliability and improve this Actor. A copy is saved as USAGE_STATS in your own Apify key-value store, so you can see the exact record created for your run.

Set disableUsageStats to true in the input to opt out. Nothing is sent then; your USAGE_STATS record only says that statistics were disabled.

Only these fields are recorded:

• schema version, Actor name and build number;
• UTC start and finish hour (not a precise timestamp);
• run duration, number of results and time to the first result, each as a coarse range;
• whether the result was empty, the end status, and an error type from a fixed list;
• memory setting and counts of charged events;
• names of the input fields you set, never their values;
• the selected option for input fields that offer a fixed list of choices (for example a sort order).

We do not collect input text, search terms, URLs, domains, usernames, email addresses, names, proxy credentials, tokens, scraped records, output items, raw error messages, stack traces, or hashes of any of those values. Records are kept for no longer than 13 months, used only as aggregated operational statistics, and never sold or shared.

Additional fields (Phase 2)

This Actor also records your Apify user ID, whether Apify marks the account as paying, the size range of list inputs, the selected country when the input offers a fixed list of countries, and one category from a fixed Actor taxonomy. We use these fields only for aggregate reliability, repeat-use and cross-Actor analysis; reports suppress any cell with fewer than five distinct users.

The same disableUsageStats: true input flag turns these fields off too. The user ID is removed after 13 months; we do not export, sell, share, or attempt to re-identify this data.

Run-outcome signals (v2)

To learn whether a run did what it was asked to do, the record also holds a few more coarse ranges and yes/no flags. None of them contains content:

• the result limit you asked for (a range, when the input has one) and what share of it was delivered;
• results delivered per input item you listed (a range);
• output quality as ranges: how fully the result fields were filled, the share of rows that look like errors, the share of duplicate rows, and how many different fields appeared. These are counted in memory while results are saved; no result content is kept;
• how the run was started (console, API, schedule, webhook, another Actor);
• how it ended: stopped by you, timed out, reached the requested limit, stopped by the charge limit, and how many times the platform moved the run;
• if this Actor reports it: how many items to process worked or failed (ranges) and one failure reason from a fixed list;
• a short code made from the names of the input fields you set, never their values.

Repeat-run fingerprint (v2)

When your Apify user ID is recorded (see above), the record also holds an 8-character one-way code made from your input (proxy settings left out) and this Actor's name. It only lets us see that the same account ran the same input again soon after an unsatisfying run; we never see the input itself. It is stored only in the database, never published, and reports use it in aggregate with the same five-user minimum. It is the one exception to the statement above that no hashes are collected, and disableUsageStats: true turns it off.