Indonesia Food Delivery Landscape Data Intelligence - GrabFood vs GoFood vs ShopeeFood — Market Share, Growth, and Competitive Trends

Introduction

The Indonesia Food Delivery Landscape Data Intelligence market offers valuable opportunities for food brands, restaurant chains, delivery platforms, and market researchers seeking to understand competition, pricing, and consumer demand. Through Food Data Scraping Services, businesses can organize publicly accessible restaurant menus, delivery charges, promotional offers, product availability, and platform listings into structured datasets. These datasets help researchers evaluate differences between GrabFood, GoFood, and ShopeeFood while keeping product-level observations separate from broader market estimates.

Indonesia is Southeast Asia’s largest online food delivery market by gross merchandise value (GMV). According to Momentum Works estimates reported by Katadata, Indonesia’s food delivery GMV reached approximately US$5.4 billion in 2024, representing around 17% growth over 2023. In 2025, the market grew by a further 18% to approximately US$6.4 billion. These estimates include the value of orders received by restaurant partners, including canceled orders, so they should not be interpreted as net revenue earned by platforms. Sources: Katadata’s 2024 market report and 2025 market report.

This report examines platform competition, menu information, delivery fees, promotions, pricing intelligence, and restaurant-level data. It uses published market estimates and official platform information, while identifying gaps where historical menu-level prices or delivery fees are not publicly verified.

How does restaurant menu information reveal platform positioning?

GrabFood restaurant Menu Data Scraping can help researchers organize publicly displayed restaurant names, menu items, item descriptions, prices, restaurant categories, and availability indicators. Comparing these records across locations and collection dates can reveal how restaurant assortments and displayed prices differ between listings. Researchers must match the same restaurant branch and equivalent menu item before drawing conclusions, because portion sizes, optional add-ons, and location-specific menus can affect comparability.

The Indonesia Food Delivery Landscape Data Intelligence framework should distinguish restaurant coverage from transaction value. A platform may display many restaurants in a particular city, but listing counts alone do not establish its market share or sales performance. Likewise, a higher displayed menu price does not necessarily mean a customer pays more after applying an eligible voucher or accounting for delivery and service fees.

Momentum Works estimates reported by Katadata show that GrabFood remained the largest of the three platforms in Indonesia by GMV in 2024. GrabFood’s estimated GMV reached US$2.54 billion, while GoFood recorded US$1.89 billion and ShopeeFood US$0.97 billion. These figures are useful for understanding market scale, but they are not menu-price measurements. Source: Katadata’s platform comparison.

Verified market milestones, 2020–2026

Sources: Katadata’s historical platform data, 2024 market estimates, and 2025 market estimates. Figures are rounded estimates and are not menu-price data.

What should businesses know about delivery fees?

GoFood Food Delivery fee Data API describes a structured data requirement for collecting delivery-fee observations; it does not imply that GoFood offers an unrestricted public API for obtaining all delivery charges. Delivery costs may vary according to delivery address, restaurant location, distance, time, promotions, membership benefits, and other conditions presented during ordering. A reliable comparison must capture the conditions under which each fee appears.

GoFood fee research should record the restaurant, selected branch, customer location or delivery zone, displayed fee, collection time, promotion eligibility, and any relevant order conditions. If the fee is not visible until checkout, researchers should not infer it from another restaurant or a different address. Similarly, a free-delivery voucher should be recorded separately from the platform’s standard displayed fee.

The same principle applies when comparing the three platforms. A menu item with a lower listed price may not produce the lowest total order cost if its delivery charge or service fee is higher. Conversely, a promotional offer can reduce the final amount without changing the listed menu price.

Delivery-cost comparison fields

GoFood’s parent company, GoTo, publishes financial and operational reporting through its investor-relations website. Such reports provide company-level context, but they do not constitute a historical public database of individual GoFood delivery charges. Therefore, this report does not assign unsupported annual averages to delivery fees from 2020 to 2026.

How can promotional offers be evaluated consistently?

ShopeeFood Deals & Discount Data Collection focuses on capturing publicly visible vouchers, discounts, promotional banners, eligible menu items, minimum-order requirements, and validity dates. Promotions are central to price comparisons because the displayed menu price and the price paid after an eligible discount may differ. However, the presence of a promotion does not mean every customer or every restaurant qualifies for it.

The Indonesia Food Delivery Landscape Data Intelligence approach should classify each offer according to its actual terms. Useful categories include item-level discounts, restaurant-wide promotions, minimum-spend vouchers, delivery-fee reductions, and customer-specific offers when those terms are explicitly disclosed. Researchers should preserve the original wording or source reference so that the eligibility conditions remain auditable.

Momentum Works’ February 2025 analysis found that ShopeeFood’s estimated Indonesian GMV reached US$970 million in 2024, up 76% year over year. This was a substantially faster growth rate than the reported growth of GrabFood and GoFood in that period. The result indicates a shift in competitive momentum, but it does not prove that discounts alone caused the growth. Source: Katadata’s 2024 platform estimates.

Platform GMV comparison

Source: Katadata, drawing on Momentum Works estimates. The 2023 values are approximate and derived from the reported 2024 figures and growth rates; they are included only as rounded context, not as independently measured restaurant-level sales.

For promotion research, each offer should be connected to its restaurant, menu item or eligible category, applicable conditions, and collection date. This enables analysts to separate genuine price reductions from marketing messages that require a minimum purchase or apply only to selected customers.

How can menu prices be compared across the three platforms?

A GrabFood vs GoFood vs ShopeeFood price comparison should use matched restaurants, equivalent menu items, consistent portion sizes, and comparable delivery conditions. Comparing a regular meal with a larger combo, or comparing different branches, can create a misleading impression of price differences. Where the same restaurant lists different products across platforms, the researcher should document the mismatch instead of treating the records as equivalent.

A robust comparison distinguishes menu price, discounted item price, delivery fee, service fee, voucher eligibility, and final payable total when all are visible. It should also record whether the displayed amount is available to all customers or depends on membership, payment method, or a minimum spend. These fields help restaurants assess price presentation and help researchers understand the difference between headline prices and the complete ordering cost.

The market-level figures show that GrabFood led the three platforms by Indonesian GMV in 2024, while ShopeeFood recorded the fastest annual growth among them. In 2025, estimates reported by Katadata placed GrabFood at US$2.94 billion, GoFood at US$1.98 billion, and ShopeeFood at US$1.47 billion. Source: Katadata’s 2025 platform report.

What a valid comparison needs

These steps support an evidence-based comparison without claiming that one platform is always cheapest. The actual result can vary by restaurant, city, time, and promotional eligibility. Any published ranking should identify the sample, collection period, and limitations.

How can market-level pricing intelligence support decisions?

Food delivery pricing Data intelligence in Indonesia combines menu prices, fee observations, promotional conditions, restaurant coverage, and market estimates into a structured view of competitive activity. It can help restaurants investigate whether their listed prices differ across platforms, assess the frequency of observed promotions, and identify products that merit further review. Researchers can also study whether menu availability changes alongside displayed prices, provided they have sufficiently frequent observations.

The Indonesia Food Delivery Landscape Data Intelligence model must keep market GMV separate from product-level data. GMV measures the estimated value of orders transacted through platforms; it does not directly measure restaurant profitability, platform revenue, menu inflation, or the average price paid for a meal. The reported 2025 Indonesian market total of US$6.4 billion is therefore evidence of market scale, not evidence that menu prices rose by 18%. The 18% figure refers to GMV growth. Source: Katadata’s 2025 market report.

Evidence-backed trend summary

Sources: Historical platform data, 2024 estimates, and 2025 estimates.

For operational use, teams should define consistent metrics such as median listed price for matched items, proportion of observed listings with an offer, displayed delivery-fee ranges, and restaurant coverage by location. These measures should be calculated from collected records rather than inferred from GMV statistics. Results should include sample size, timestamps, and missing-data notes so that decisions reflect the limits of the evidence.

What should a restaurant-level dataset include?

An Indonesia food delivery restaurant menu and pricing dataset should link each observed menu item to its restaurant, platform, location, collection date, and source. Core fields can include restaurant name, branch identifier when available, cuisine category, item name, description, listed price, promotional price if explicitly displayed, availability, delivery fee, and offer conditions. The exact fields should reflect what the source actually makes visible; researchers should not fill missing values by guessing.

Restaurant matching is essential when a business appears on multiple platforms. Names can vary in spelling, branches may have different menus, and the same restaurant can list differently sized products. A reliable dataset should retain platform-specific listing URLs and use documented matching rules. Where a match cannot be confirmed, the records should remain separate until more evidence is available.

The historical GMV figures offer useful context for prioritizing research. For example, the three platforms together represented an estimated US$5.4 billion in Indonesian food delivery GMV in 2024, rising to about US$6.4 billion in 2025. Those market totals establish the sector’s scale but cannot tell a restaurant which menu item was discounted or what a customer paid for delivery. Sources: 2024 market estimates and 2025 market estimates.

A useful dataset also includes collection status, timestamps, currency, and validation flags. These support repeatable analysis, help identify stale records, and make it possible to distinguish real price changes from product substitutions or changes in promotional eligibility. Collection should respect platform terms, access restrictions, privacy requirements, and applicable law.

Actowiz Solutions can help businesses design a structured workflow for collecting, validating, and analyzing publicly available food delivery data, subject to applicable platform terms and permissions.

The emphasis should be on trustworthy records, consistent product matching, and transparent reporting rather than unsupported claims about competitor performance.

A Managed Grab data workflow can be designed around agreed sources and fields, such as restaurant listings, menu prices, promotions, and visible availability. The scope should specify the target cities, restaurant categories, collection frequency, permitted data sources, and quality checks. These details are important because a dataset from selected locations should not be presented as a complete national census.

The Indonesia Food Delivery Landscape Data Intelligence approach can then connect validated observations with published market research. Useful deliverables may include structured files, historical snapshots, source references, coverage summaries, and dashboards showing the data that was actually collected.

Potential service benefits include:

• Consistent collection: Standardize the fields captured from approved sources.
• Data quality controls: Identify duplicates, missing prices, and inconsistent product attributes.
• Historical traceability: Preserve timestamps and source URLs for later verification.
• Comparable reporting: Separate menu prices, promotions, delivery fees, and total order costs.
• Evidence-based insights: Clearly distinguish observed data from third-party market estimates.

This approach gives restaurants, food brands, and researchers a defensible basis for competitive analysis while preserving the limitations of the underlying data.

Conclusion

Indonesia’s online food delivery market has continued to expand, with estimated GMV increasing from approximately US$5.4 billion in 2024 to US$6.4 billion in 2025. GrabFood remained the largest of the three platforms by estimated Indonesian GMV, while ShopeeFood recorded strong growth. These market figures help explain the competitive landscape, but menu prices, delivery fees, and promotions require separate, dated observations.

Businesses can gain more useful insights by matching equivalent restaurant items, capturing promotional conditions, recording delivery costs under consistent scenarios, and documenting collection coverage. Historical claims should be made only where supporting records exist; unavailable 2026 full-year results and unverified menu-price histories should not be presented as facts.

Contact Actowiz Solutions to discuss a compliant data collection and analysis workflow using a GoFood Scraping API approach, a tailored Web Crawling service, and Web Data Mining to turn validated restaurant and pricing observations into actionable market intelligence!