Scrape Swiggy Instamart & Zepto Daily Report 2026 - Tracking JB & UB Category Prices, Products, and Inventory.jpg
                     Scrape Swiggy Instamart & Zepto Daily Report 2026 - 
Tracking JB & UB Category Prices, Products, and Inventory
Introduction
India's quick-commerce market has moved from a convenience-led 
experiment into an important retail channel for groceries, household 
essentials, personal care, beverages, snacks, and increasingly non-
grocery categories. The evolution has also changed the way brands 
monitor digital shelves. Instead of relying on weekly or monthly 
snapshots, businesses increasingly need frequent observations of 
product availability, prices, discounts, assortment, and SKU-level 
changes. At the same time, Global quick commerce data provides 
broader visibility into how rapid-delivery retail models are evolving 
across markets, helping businesses contextualize local pricing, 
assortment, fulfillment, and consumer-demand trends.
This research report examines Scrape Swiggy Instamart & Zepto Daily 
Report 2026 as a structured approach to monitoring two beverage-
focused category groups across major quick-commerce platforms: JB, 
meaning Juices & Beverages, and UB, meaning Uncategorized Beverages.
Here, Juices & Beverages (JB) refers to products that are clearly classified 
within beverage-related categories, including juices, fruit beverages, soft 
drinks, energy drinks, sports drinks, flavored beverages, and other 
packaged beverage products where applicable. Uncategorized Beverages 
(UB) refers to beverage products that are identifiable as beverages but 
are not assigned to a specific beverage subcategory in the source 
platform taxonomy.
This distinction is important because quick-commerce platforms can 
organize similar products differently. A beverage may appear under a 
clearly defined category on one platform while being placed under an 
uncategorized or broader classification on another. Separating JB and UB 
therefore helps businesses analyze both standardized beverage 
categories and products whose classification requires additional 
normalization.
Swiggy Instamart has expanded considerably since its launch in August 
2020. Swiggy reported more than 125 cities and millions of orders across 
128+ cities in its 2025 annual analysis. By Q3 FY26, the company 
reported 1,136 active dark stores across 131 cities.
The scale of these networks creates a large volume of product-level 
observations. A structured dataset can therefore support pricing 
comparisons, availability tracking, assortment analysis, competitor 
monitoring, and category-level benchmarking specifically across Juices & 
Beverages and Uncategorized 
Beverages.
How Has Quick Commerce Expanded and Why Do Beverage 
Categories 
Matter?
The development of quick commerce between 2020 and 2026 
demonstrates why category-level intelligence has become increasingly 
relevant. In 2020, the model was largely associated with urgent grocery 
and daily-need purchases. As platforms expanded their fulfillment 
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The JB category, or Juices & Beverages, can include recognizable 
beverage 
groups such as:
• Fruit juices
• Fruit drinks
• Soft drinks
• Energy drinks
• Sports beverages
• Flavored beverages
• Packaged drinking beverages
• Other clearly classified beverage products
The UB category, or Uncategorized Beverages, can capture products that 
are identifiable as beverages but do not have a specific beverage 
subcategory assigned in the source data. This classification can be useful 
when different platforms use different category structures or when 
product taxonomy is incomplete.
By 2025, Swiggy Instamart stated that its service had expanded to 100 
cities and offered more than 30,000 products. Its larger "megapod" dark 
stores were designed to accommodate up to 50,000 SKUs, illustrating 
how fulfillment infrastructure can directly influence assortment breadth 
and the growing importance of JB UB Quick Commerce Category 
Intelligence 2026 for tracking beverage assortment at scale.
By July 2026, an independent public-location mapping project counted 
5,625 dark stores across five major Indian quick-commerce platforms, 
including mapped Zepto and Swiggy Instamart stores. The source 
describes these figures as a verified public snapshot rather than internal 
platform data.
This expansion makes beverage-category monitoring relevant for brands 
that need to understand product presence across different fulfillment 
zones. A daily dataset can reveal whether a JB product remains listed, 
whether a UB product moves into a more specific classification, whether 
a beverage disappears temporarily, changes price, receives a discount, 
or is replaced by another SKU.
Market Snapshot
Indicator Reported figure Period
Instamart launch August 2020 2020
Instamart cities 100 2025
Instamart products 30,000+ 2025
Instamart active dark 1,136 Q3 FY26
stores
Instamart cities 131 Q3 FY26
Quick-commerce dark 
stores mapped across 5 5,625 July 2026
platforms
Sources: Swiggy corporate disclosures and QuickCommerceMap.
How Can Daily Monitoring Identify Beverage Availability 
Gaps?
Inventory visibility is one of the most dynamic elements of quick 
commerce. A product can be available in one location and unavailable 
in another, while its status may change several times during the day. 
This makes periodic monitoring less suitable for businesses that need 
granular visibility into fulfillment-level availability.
Swiggy Instamart analytics can help businesses track these changes more 
consistently, enabling deeper analysis of beverage availability, stock 
movements, and location-level inventory patterns.
For JB products, monitoring can reveal whether specific juices, soft drinks, 
energy beverages, or other beverage products remain consistently 
available. For UB products, monitoring can additionally identify whether 
products remain uncategorized or whether their classification changes 
over time.
Between 2020 and 2022, quick-commerce operations concentrated 
heavily on metro markets and high-frequency grocery missions. During 
2023 and 2024, assortment and geographic coverage expanded, creating 
more opportunities for brands to compare availability across locations. In 
2025, Swiggy reported expansion to 100 cities, while its 2025 order 
analysis covered 128+ cities.
By 2026, the operational footprint had expanded further. Swiggy reported 
1,136 active dark stores at the end of Q3 FY26, while its average active 
dark-store size had increased to more than 4,200 square feet.
For businesses, Track JB UB Category Data on Swiggy Instamart can mean 
capturing SKU status, displayed price, discount, pack size, seller or 
fulfillment information where available, and location-level availability.
The distinction between JB and UB also adds a taxonomy layer to 
availability monitoring. A business can determine whether a product is 
unavailable because it is genuinely out of stock or whether it is simply 
difficult to locate because of inconsistent category classification.
Availability Metrics
Metric Business Use
In-stock rate Measures product availability
Stockout frequency Identifies recurring availability gaps
Availability by location Compares fulfillment zones
SKU activation/deactivation Tracks assortment changes
Price during availability Connects stock status with pricing
Replenishment interval Identifies recurring inventory patterns
JB/UB classification Separates defined and uncategorized beverage products
Daily collection creates a time series rather than a single snapshot, 
allowing businesses to distinguish persistent availability problems from 
short-lived stockouts.
How Can Businesses Compare Beverage Products at SKU Level 
Across Platforms?
Quick-commerce competition increasingly occurs at the individual SKU 
level. Two platforms may carry the same brand but differ in pack size, 
assortment depth, promotional pricing, classification, or availability. 
Consequently, comparing only category-level prices can hide meaningful 
differences.
For beverage analysis, SKU normalization is especially important. A 250 
ml juice should not automatically be compared with a 1-litre juice simply 
because both belong to the same JB category. Likewise, a UB product 
should be matched according to its actual product identity, pack size, 
brand, and other relevant attributes before price comparisons are 
performed.
From 2020 to 2022, competitive monitoring primarily focused on basic 
grocery products and broad category comparisons. By 2023 and 2024, 
platforms had expanded into more categories, increasing the importance 
of SKU normalization. In 2025, Swiggy reported a broad assortment 
covering groceries, electronics, fashion, makeup, toys, and other 
categories.
In 2026, the competitive environment has become even more data-
intensive. Zepto's FY26 regulatory filing reported 1,139 dark stores at 
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cstoomrep apreer  iddaeyn tdicuariln ogr  FcYlo2s6e.ly matching beverage products using attributes 
such as brand, product name, beverage type, pack size, MRP, selling 
price, discount, availability, category, and location.
SKU Comparison Framework
Data Point Instamart Zepto Comparison 
Purpose
Product name ✓ ✓ Product matching
Brand ✓ ✓ Brand comparison
Beverage type ✓ ✓ Category matching
✓ ✓ Taxonomy JB/UB classification comparison
Pack size ✓ ✓ Like-for-like analysis
Selling price ✓ ✓ Price 
benchmarking
Discount ✓ ✓ Promotion analysis
Availability ✓ ✓ Stock comparison
Category ✓ ✓ Category mapping
Location ✓ ✓ Geographic 
comparison
A normalized SKU database can reduce duplicate records and make daily 
platform comparisons easier. It can also support historical price tracking 
and identification of assortment gaps between competing platforms.
For JB products, the dataset can retain the specific beverage category. 
For UB products, the dataset can preserve the original platform 
classification while applying a standardized internal label of 
"Uncategorized Beverages." This prevents uncategorized products from 
being incorrectly excluded from beverage intelligence.
How Should Businesses Standardize Beverage Data for Market 
Comparison?
A reliable benchmark requires consistent collection rules. Comparing 
products on different dates, locations, pack sizes, or category definitions 
can produce misleading results. For this reason, businesses need 
standardized product identifiers, category mappings, timestamps, and 
geographic fields.
Between 2020 and 2022, quick-commerce benchmarking was often 
limited by smaller geographic footprints and narrower assortments. As 
platforms expanded during 2023 and 2024, businesses increasingly 
needed structured datasets to compare pricing and availability at greater 
scale.
By 2025, Swiggy's expansion to 100 cities and its reported 30,000+ 
product assortment illustrated the increasing breadth of observations 
available for analysis.
The 2026 environment adds another layer: dark-store density. A July 2026 
public mapping dataset identified thousands of dark stores across multiple 
platforms and cities, demonstrating the geographic scale that market 
researchers may need to account for.
A JB UB Category Intelligence Benchmark can standardize metrics such as 
average selling price, median price, discount percentage, SKU availability, 
assortment count, platform overlap, and beverage-category distribution.
Benchmark Metrics
Benchmark Measurement
Average selling price Mean observed price
Median selling price Midpoint of price observations
Discount rate Discount relative to MRP
SKU availability Available SKUs / monitored SKUs
Platform overlap Shared products across platforms
Assortment depth Number of listed SKUs
Price gap Difference between platform prices
Promotion frequency Frequency of observed offers
JB share Proportion of defined beverage products
UB share Proportion of uncategorized beverage products
The addition of JB and UB classification makes the benchmark more 
useful because businesses can determine whether changes are caused 
by actual assortment movement or by differences in platform 
categorization.
For example, if one platform places a beverage in a clearly defined juice 
category while another leaves it under Uncategorized Beverages, the 
underlying product should still be matched using product identity, brand, 
pack size, and other attributes.
A benchmark built from repeated observations becomes more useful over 
time because businesses can compare current conditions with historical 
patterns rather than relying on isolated screenshots or manual checks.
How Can Zepto Data Reveal Beverage Assortment and Pricing 
Patterns?
Zepto has developed a substantial dark-store network, making its 
assortment and pricing data relevant for brands monitoring quick-
commerce competition. Its FY26 regulatory filing reported 1,139 dark 
stores at the end of the fiscal year, compared with 1,029 at the end of 
FY25. The filing also reported higher orders per dark store during FY26.
The platform's development between 2021 and 2023 coincided with rapid 
adoption of the 10-minute delivery model. During 2024 and 2025, quick-
commerce platforms increasingly broadened their assortments beyond 
conventional grocery missions. By 2026, businesses monitoring the 
channel may need to examine both core grocery and emerging categories, 
including beverage segments.
JB UB Category Intelligence on Zepto can capture product-level 
observations such as title, brand, beverage type, pack size, listed price, 
promotional price, availability, category, and location where these 
attributes are publicly visible.
The JB/UB framework allows analysts to separate two types of 
observations:
• JB – Juices & Beverages: Products that have a clearly identifiable 
beverage classification.
• UB – Uncategorized Beverages: Products that are recognizable as 
beverages but are not assigned to a specific beverage subcategory.
Zepto Monitoring Indicators
Indicator Monitoring Value
SKU count Measures assortment breadth
Price movement Identifies daily changes
Discount movement Tracks promotional activity
Stock status Highlights availability
New listings Detects assortment expansion
Removed listings Identifies assortment contraction
Location coverage Compares geographic availability
JB assortment Measures classified beverage breadth
UB assortment Measures uncategorized beverage presence
A Zepto grocery dataset can also provide a structured historical layer for 
analyzing recurring products, category changes, price movements, and 
location-specific availability. With daily timestamps, businesses can 
separate temporary promotional activity from sustained pricing or 
assortment changes.
The same framework can be used to determine whether a beverage 
remains within UB over time or is later assigned to a more specific 
category.
How Can Frequent Collection Become Actionable Retail 
Intelligence?
The final stage is transforming raw observations into an analytics-ready 
dataset. Quick-commerce pages can change frequently, and collecting 
information at scale requires appropriate scheduling, normalization, 
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narrow because platforms and assortments were smaller. During 2023 
and 2024, expansion created larger volumes of product-level records. In 
2025, Swiggy reported 30,000+ products in its 100-city network, while 
its larger megapods could accommodate up to 50,000 SKUs.
By 2026, the scale of dark-store operations had increased further. 
Swiggy reported 1,136 active dark stores in Q3 FY26, while Zepto 
reported 1,139 dark stores at the end of FY26.
This makes Q-Commerce data scraping useful for building recurring 
datasets that capture product, pricing, inventory, category, and 
location signals.
For JB and UB monitoring, the data pipeline should preserve both the 
source classification and the standardized classification. This is 
important because an original source category may contain valuable 
information about how the platform organizes its digital shelf.
Data Pipeline
Stage Output
Source discovery Platform/product URLs
Data extraction Raw product observations
Normalization Standardized attributes
Category mapping JB / UB classification
Validation Quality-checked records
Deduplication Clean SKU universe
Timestamping Historical tracking
Storage Structured database
Analytics Dashboards and reports
A recurring pipeline enables businesses to monitor market changes 
continuously rather than rebuilding datasets manually for every 
reporting cycle.
How Can Product Data Scrape Support Quick-Commerce 
Intelligence?
Product Data Scrape can support businesses that require structured, 
recurring e-commerce and quick-commerce datasets for research, 
competitive monitoring, pricing intelligence, and assortment analysis.
The value of a specialized data workflow lies in combining extraction 
with data quality processes. Product records can be normalized around 
common fields such as SKU, product title, brand, category, beverage 
type, pack size, price, discount, availability, location, and timestamp.
For JB and UB analysis, an additional taxonomy layer can be applied:
JB — Juices & Beverages: clearly categorized beverage products.
UB — Uncategorized Beverages: identifiable beverage products that are 
not assigned to a specific beverage subcategory in the source 
taxonomy.
This structure prevents beverage products from being lost simply 
because the source platform uses inconsistent or incomplete category 
labels.
A daily monitoring framework can also support historical comparisons. 
Instead of looking at a product's price only once, businesses can 
examine how it changes across days, locations, promotional periods, 
and competing platforms.
The workflow can be designed around specific business requirements, 
including:
• Daily or scheduled data collection
• Product and SKU-level monitoring
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• Availability and stockout 
identification
• Beverage assortment analysis
• Platform-to-platform comparisons
• Location-level monitoring
• Data normalization and validation
• Historical datasets for trend 
analysis
• Analytics-ready structured delivery
For brands and retailers, the resulting datasets can support pricing 
teams, category managers, marketplace teams, business analysts, and 
market researchers. The same data can also feed dashboards or internal 
analytics environments for recurring decision support.
Conclusion
Quick commerce has evolved substantially since 2020, moving from a 
narrow grocery-delivery proposition toward a broader digital retail 
environment. The expansion of dark stores, product assortment, 
geographic coverage, and order volumes means that pricing and 
availability can change rapidly across locations. Scrape Swiggy 
Instamart & Zepto Daily Report 2026 provides a structured approach to 
tracking these daily changes across products, prices, inventory, and 
assortment.
For businesses monitoring beverage categories, it is important to 
distinguish between JB, meaning Juices & Beverages, and UB, meaning 
Uncategorized Beverages. JB captures products that are clearly assigned 
to beverage-related categories, while UB captures identifiable beverage 
products that remain outside a defined beverage subcategory in the 
source taxonomy.
This distinction makes the dataset more useful for cross-platform 
analysis. A beverage may be classified differently on Swiggy Instamart 
and Zepto even when the underlying product is identical. By retaining 
both the original category and a standardized JB/UB classification, 
businesses can perform more consistent product matching, price 
benchmarking, assortment analysis, and availability monitoring.
Swiggy Instamart's reported 1,136 active dark stores in Q3 FY26 and 
Zepto's 1,139 dark stores at FY26 year-end illustrate the scale of the 
fulfillment networks involved.
A structured Scrape 10-Minute Delivery Assortment workflow can help 
convert these fast-changing retail signals into historical datasets. 
Combined with standardized SKU matching, JB/UB classification, 
location-level monitoring, price benchmarking, and availability analysis, 
businesses can build a consistent foundation for quick-commerce 
intelligence.
The 2026 market requires more than occasional product checks. 
Recurring datasets can help organizations understand how beverage 
products move across platforms, how prices change, where availability 
gaps occur, support Stockout Monitoring, how promotions evolve, and 
how JB and UB assortments change over time.
Build a structured daily quick-commerce dataset with 
Product Data Scrape to monitor Juices & Beverages and Uncategorized 
Beverages, along with products, prices, availability, and assortment 
changes across leading delivery platforms!