We extract store inventories, dynamic grocery pricing, delivery fees, brand catalogues, and stock availability from Instacart. 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 Store Catalogues objects from instacart.com. All fields typed and schema-versioned.
"store_id": "st_18492", "chain_name": "Wegmans", "department": "Produce", "aisle": "Fresh Vegetables", "product_id": "pr_849201", "upc": "0000000004011", "title": "Organic Bananas", "brand": "Wegmans Organic", "price": 2.49
| # | store_id | chain_name | department | aisle | product_id | upc |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Promos objects from instacart.com. All fields typed and schema-versioned.
"product_id": "pr_849201", "store_id": "st_18492", "base_price": 3.99, "current_price": 2.99, "discount_pct": 25, "promo_type": "SALE", "bogo_eligible": false, "scraped_at": "2026-05-12T09:14:00Z"
| # | product_id | store_id | base_price | current_price | discount_pct | promo_type |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Search & SERP objects from instacart.com. All fields typed and schema-versioned.
"keyword": "almond milk", "zip_code": "10001", "store_id": "st_18492", "position": 1, "product_id": "pr_59210", "sponsored": true, "sponsored_brand": "Almond Breeze", "price": 4.49
| # | keyword | zip_code | store_id | position | product_id | title |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Store Locations objects from instacart.com. All fields typed and schema-versioned.
"store_id": "st_18492", "chain_name": "Wegmans", "address": "Astor Place", "city": "New York", "state": "NY", "zip_code": "10003", "pickup_available": true, "delivery_fee_base": 3.99
| # | store_id | chain_name | address | city | state | zip_code |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Product Metadata objects from instacart.com. All fields typed and schema-versioned.
"product_id": "pr_59210", "upc": "041570056114", "title": "Unsweetened Vanilla Almond Milk", "ingredients": "Almondmilk (Filtered Water, Almonds), Calcium Carbonate...", "dietary_tags": "['Vegan', 'Gluten-Free', 'Dairy-Free']", "allergens": "['Tree Nuts']", "weight_volume": "64 fl oz", "manufacturer": "Blue Diamond Growers"
| # | product_id | upc | title | description | ingredients | nutrition_facts |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Instacart scraper handles every layer of the platform: location-bound store catalogues, dynamic pricing, nutritional metadata, and sponsored placements - with ZIP code session management and anti-bot circumvention built in.
Extract full inventory lists bound to specific ZIP codes and store IDs. Capture departments, aisles, and stock availability.
Track Instacart-specific pricing, base prices, and discounts. Monitor retailer markups applied on the platform versus in-store pricing.
Capture deal badges, Buy-One-Get-One (BOGO) eligibility, and temporary price reductions timestamped per crawl.
Track organic versus sponsored position for any keyword and ZIP code. Identify which brands are winning retail media placements.
Extract standard UPCs and internal product IDs to map Instacart catalogues directly to your internal product databases.
Pull full ingredient lists, nutritional facts panels, dietary tags, and allergen warnings from product detail pages.
Monitor base delivery fees, service fee percentages, and small basket fees across different chains and geographic zones.
Compare pricing and availability for identical UPCs across multiple retail chains operating in the same ZIP code.
Run one-off bulk exports or configure continuous pipelines at daily or real-time cadences with change-detection diffing.
Brief in. Clean data out.
Provide ZIP codes, store chains, or keyword sets. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and CAPTCHA handling for instacart.com.
Schema validation, null-rate checks, price-outlier detection, and sample catalogues before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Instacart invests heavily in scraping detection and location-bound sessions. Here's how we stay resilient - and why teams choose managed infrastructure over DIY.
Instacart uses advanced anti-bot systems like Datadome. Our crawlers use residential ISP proxies with realistic browser fingerprints, randomised request timing, and full cookie session management to bypass perimeter security.
Instacart data is entirely location-dependent. We maintain persistent, isolated browser sessions bound to specific ZIP codes and store IDs, ensuring the pricing and availability data reflects the exact local reality.
Instead of fragile DOM scraping, we intercept and parse Instacart's internal GraphQL API responses. This yields cleaner data, faster extraction, and lower bandwidth overhead while maintaining session validity.
For large grocery catalogues, we maintain a hash index of last-seen values per field. Subsequent runs only push diffs - reducing compute cost, storage bloat, and downstream processing load.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, price outliers, schema drift, and coverage drops. SLA uptime is contractual, not aspirational.
FMCG brands track their product availability, shelf share, and out-of-stock rates across regional retail chains.
Retailers and analysts monitor Instacart's platform markups versus in-store pricing to optimise their own delivery pricing strategies.
Marketing teams audit sponsored search placements to ensure ad spend translates to top-of-page visibility for target keywords.
Financial analysts use high-frequency grocery pricing data to model local inflation trends ahead of official CPI releases.
Competing delivery platforms track service fees, delivery minimums, and surge pricing dynamically across different ZIP codes.
Health and wellness applications ingest vast catalogues of ingredient lists and nutritional facts to train dietary recommendation models.
"Instacart holds the definitive graph of local grocery availability and real-time retail pricing - but accessing it requires solving complex location-bound session management."
Most teams underestimate the investment required: reliable Instacart scraping requires maintaining persistent ZIP code sessions, handling complex GraphQL payloads, bypassing anti-bot protection, and managing residential proxy rotation. DataFlirt absorbs that complexity so your engineers can focus on the analysis - not the infrastructure.
Everything supported by our instacart.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 across US regions. Rotation happens per-request with sticky sessions where required. IP score monitoring prevents blacklisted pool contamination.
Pipelines run on AWS Lambda (burst) and ECS (sustained). 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 instacart.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Instacart is generally permissible under applicable law. DataFlirt targets only public, non-authenticated product, pricing, and store data. We do not extract personal data, circumvent authentication walls, or violate GDPR/CCPA. Clients should review Instacart's ToS and consult legal counsel for specific use cases.
We use residential ISP proxies, full Playwright browser sessions with realistic fingerprints, and request timing modelled on human behaviour. We monitor for CAPTCHA rate spikes in real time and trigger pool rotation or solver queues automatically.
Yes. Instacart pricing is highly localised. We bind extraction sessions to specific ZIP codes and store IDs, allowing you to track geographic price variations and delivery fee differences accurately.
Real-time streaming pipelines achieve sub-60-minute latency for price and availability signals on a defined product set. Full store catalogue refreshes at daily cadence complete within a 6-12 hour window depending on size.
Our smallest packages start at a defined list of stores or ZIP codes with weekly delivery. For national-level tracking or custom schema requirements, we price based on volume and delivery frequency. Contact us with your use case for a scoped quote.
Yes. We extract all available metadata on product detail pages, including full ingredient lists, nutritional facts panels, dietary tags, allergen warnings, and manufacturer details.
Every pipeline run produces timestamped snapshots. We maintain a time-series table per UPC/store combination for price and availability from the date your pipeline starts.
Absolutely. We provide a sample run of up to 5 stores or 500 products as part of the pre-engagement scoping process - so you can validate schema fit, field completeness, and data quality before signing any contract.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off catalogue dump or a continuous price-monitoring feed across 5,000 stores - we scope, build, and operate the pipeline. Tell us what you need.
