We extract recipes, ingredient lists, nutritional macros, user reviews, and cooking directions from Food.com. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake on your cadence.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Recipe Metadata objects from food.com. All fields typed and schema-versioned.
"recipe_id": "45129", "title": "Classic Beef Stroganoff", "author_name": "ChefJohn", "prep_time_mins": 15, "cook_time_mins": 25, "rating": 4.8, "review_count": 1422, "yield_servings": 4
| # | recipe_id | title | author_name | author_id | publish_date | description |
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Complete list of extractable fields for Ingredients & Nutrition objects from food.com. All fields typed and schema-versioned.
"recipe_id": "45129", "calories": 450, "fat_g": 22.5, "protein_g": 35.2, "carbs_g": 12.0, "sodium_mg": 850, "dietary_flags": "['High Protein', 'Contains Dairy']"
| # | recipe_id | ingredients_raw | ingredients_parsed | calories | fat_g | saturated_fat_g |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Directions & Steps objects from food.com. All fields typed and schema-versioned.
"recipe_id": "45129", "step_number": 1, "instruction_text": "Heat olive oil in a large skillet over medium-high heat.", "temperature_celsius": "None", "duration_mins": 5, "equipment_needed": "['large skillet']"
| # | recipe_id | step_number | instruction_text | equipment_needed | temperature_celsius | duration_mins |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Reviews & Tweaks objects from food.com. All fields typed and schema-versioned.
"review_id": "R89210", "recipe_id": "45129", "user_name": "BakingQueen", "rating": 5, "review_text": "Excellent recipe, very easy to follow.", "tweak_text": "Added mushrooms instead of peas.", "helpful_votes": 14, "date_posted": "2025-01-14"
| # | review_id | recipe_id | user_id | user_name | rating | review_text |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for User Profiles objects from food.com. All fields typed and schema-versioned.
"user_id": "U10948", "username": "BakingQueen", "join_date": "2018-04-12", "recipes_submitted": 42, "total_reviews": 156, "followers_count": 1024, "location": "Chicago, IL"
| # | user_id | username | join_date | location | bio | recipes_submitted |
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Our Food.com scraper handles every layer of the platform: recipe metadata, complex ingredient lists, nutritional macros, user tweaks, and review pagination.
Title, prep time, cook time, yield, and category tags scraped at the recipe level.
Extract raw ingredient strings and map them into structured quantities, units, and food items.
Capture calories, fats, proteins, carbohydrates, sodium, and vitamin data per serving.
Full review text, star ratings, helpful vote counts, and specific user tweaks paginated across all review pages.
Ordered instruction arrays including cooking durations and equipment mentions.
Extract keto, vegan, gluten-free, and allergen flags associated with each recipe.
Scrape metrics on recipe creators including follower counts, total submissions, and location data.
Extract taxonomy data to map recipes into their correct hierarchical collections.
Run one-off bulk exports or configure continuous pipelines at defined cadences with change detection.
Brief in. Clean data out.
Provide recipe categories, keyword sets, or author IDs. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, and session management for food.com.
Schema validation, null-rate checks, and sample data reviews before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Recipe platforms utilise aggressive caching and dynamic rendering. Here is how we extract clean data reliably.
Food.com employs rate limiting and bot detection on high-volume endpoints. Our crawlers use residential ISP proxies with realistic browser fingerprints and randomised request timing to maintain access.
User reviews and infinite scroll pages are heavily JavaScript-rendered. We run full Playwright browser sessions to trigger lazy loading and capture dynamic content that headless HTTP clients miss entirely.
We extract structured LD+JSON metadata where available and fall back to resilient DOM selectors for user tweaks and comments, ensuring a layout change does not break your data pipeline.
Ingredient strings are notoriously messy. We extract the raw strings and apply parsing rules to separate quantities, units, and base ingredients into structured fields.
For large recipe catalogues, we maintain a hash index of last-seen values. Subsequent runs only push diffs, reducing compute cost and downstream processing load.
Grocery delivery platforms integrate structured ingredient lists to build automated cart-building and shoppable recipe features.
Health and fitness applications ingest macro profiles and dietary tags to power meal planning and calorie tracking.
Machine learning teams use recipe structures, instructions, and ingredient pairings to train culinary recommendation engines and LLMs.
FMCG brands track trending ingredients, popular user tweaks, and dietary shifts to identify product development opportunities.
Food publishers analyse rating distributions, review volumes, and content structures to optimise their own editorial strategies.
Retailers build recommendation systems based on user preferences, dietary restrictions, and popular recipe modifications.
"Food.com contains decades of culinary experimentation and user modifications, but extracting precise nutritional macros and ingredient structures requires dedicated infrastructure."
Most teams underestimate the complexity of recipe scraping. Unstructured ingredient strings, dynamic review pagination, and aggressive rate limiting break naive scripts. DataFlirt manages the residential proxies, JavaScript rendering, and schema normalisation so your data science team can focus on modelling, not maintenance.
Everything supported by our food.com scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
Scrapy handles crawl orchestration, deduplication, and retry logic. Playwright handles JavaScript rendering, cookie sessions, and interaction flows. Combined via scrapy-playwright middleware.
We maintain pools of residential ISP proxies. Rotation happens per-request with sticky sessions where required. IP score monitoring prevents blacklisted pool contamination.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, dependency management, and SLA alerting. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About food.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Food.com is generally permissible under applicable law, reinforced by the hiQ v. LinkedIn ruling. DataFlirt targets only public, non-authenticated recipe, nutrition, and review data. We do not extract personal data or circumvent authentication walls.
We extract the raw ingredient strings and apply parsing logic to separate quantities, units of measurement, and the core ingredient item into structured JSON fields.
Yes. We paginate through all available reviews for a given recipe, capturing the star rating, full text, helpful votes, and specific user modifications or tweaks.
Yes. Where provided on the recipe page, we extract the full nutritional profile including calories, fats, proteins, carbohydrates, and sodium.
Pipelines can be configured for daily or weekly refreshes depending on your requirements. Change detection ensures only updated recipes or new reviews are processed.
Our packages start at a defined category or keyword list with weekly delivery. Contact us with your specific use case for a scoped quote.
Absolutely. We provide a sample run of up to 500 recipes as part of the pre-engagement scoping process so you can validate schema fit and data quality.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a specific category extraction or a continuous feed of user reviews and tweaks across the platform, we scope, build, and operate the pipeline. Tell us what you need.
