Track AJIO vs Myntra India Fashion Pricing (2026 Guide)

Thirdwatch's AJIO Scraper and Myntra Scraper feed a cross-platform India fashion-pricing tracker — daily snapshot of the same brand or keyword watchlist on both platforms, compute price and discount deltas, surface where private-label vs third-party brands diverge. Built for India fashion ops, brand teams managing dual marketplace listings, growth analysts optimising marketplace spend, and price-comparison product builders.

Why track AJIO vs Myntra India fashion pricing

India online fashion is a near-duopoly with Nykaa-Fashion as the close third. According to the RedSeer 2024 fashion ecom report, Myntra and AJIO together capture the majority of the roughly $9–10B online fashion GMV — and they price the same brand catalog differently. The same Levi's 511 slim jean retails at ₹2,799 on Myntra and ₹2,499 on AJIO over one weekend, and inverts the next. AJIO leans on Reliance Retail's house brands (AJIO Own, Netplay, Performax) for aggressive private-label discounting; Myntra leans on first-party HRX, Roadster and a deeper third-party catalog. The delta is structural, not noise.

For brand ops teams, growth marketers, and analysts, that delta is operational signal. The job-to-be-done is structured: a brand ops team manages 300 SKUs across both marketplaces and needs daily price-parity alerts. A growth team optimising marketplace ad spend wants to flag the cheaper-priced marketplace per SKU before it bids. A market analyst builds a longitudinal dataset of India fashion EOSS rhythms. A consumer price-comparison app ingests both as data layers. All reduce to dual-platform snapshots, SKU matching, and delta computation.

How does this compare to the alternatives?

Indian price-comparison SaaS is priced for consumer-app operators bundling affiliate revenue. The Thirdwatch AJIO Scraper plus Myntra Scraper give you raw structured data at competitively priced pay-per-result, with no vendor lock-in and no use-case gating.

How to track AJIO vs Myntra prices in 5 steps

Step 1: How do I authenticate against Apify?

export APIFY_TOKEN="apify_api_xxxxxxxxxxxxxxxx"

Step 2: How do I pull both platforms in parallel?

Spawn one run for AJIO and one for Myntra against the same brand watchlist.

import os, json, datetime, pathlib
from apify_client import ApifyClient

client = ApifyClient(os.environ["APIFY_TOKEN"])

BRAND_WATCH = ["levis 511 slim jean", "allen solly mens shirt",
               "biba kurta set", "hrx tshirt", "puma running shoes",
               "tommy hilfiger polo", "fossil mens watch"]

ajio_run = client.actor("thirdwatch/ajio-scraper").call(run_input={
    "queries": BRAND_WATCH, "sortBy": "popularity", "maxResults": 60,
}, wait_secs=600)

myntra_run = client.actor("thirdwatch/myntra-scraper").call(run_input={
    "queries": BRAND_WATCH, "maxResults": 60,
}, wait_secs=600)

ajio = list(client.dataset(ajio_run["defaultDatasetId"]).iterate_items())
myntra = list(client.dataset(myntra_run["defaultDatasetId"]).iterate_items())

today = datetime.date.today().isoformat()
pathlib.Path("snapshots").mkdir(exist_ok=True)
pathlib.Path(f"snapshots/ajio-{today}.json").write_text(json.dumps(ajio))
pathlib.Path(f"snapshots/myntra-{today}.json").write_text(json.dumps(myntra))
print(f"{today}: AJIO {len(ajio)}, Myntra {len(myntra)}")

Step 3: How do I match the same SKU across AJIO and Myntra?

There is no shared product ID. Use brand as a hard filter and rapidfuzz for product-name similarity.

import pandas as pd
from rapidfuzz import fuzz, process

a = pd.DataFrame(ajio)[["sku", "brand", "product_name", "price", "original_price", "discount_percent", "url"]]
m = pd.DataFrame(myntra)[["product_id", "brand", "title", "price", "mrp", "discount_percent", "url"]]
m = m.rename(columns={"title": "product_name"})

a["brand_key"] = a.brand.str.lower().str.strip()
m["brand_key"] = m.brand.str.lower().str.strip()

pairs = []
for brand_key, ag in a.groupby("brand_key"):
    mg = m[m.brand_key == brand_key]
    if mg.empty: continue
    for _, ar in ag.iterrows():
        best = process.extractOne(
            ar.product_name, mg.product_name.tolist(),
            scorer=fuzz.token_set_ratio, score_cutoff=85,
        )
        if best:
            mr = mg.iloc[best[2]]
            pairs.append({
                "brand": ar.brand, "name_ajio": ar.product_name, "name_myntra": mr.product_name,
                "ajio_price": ar.price, "myntra_price": mr.price,
                "ajio_mrp": ar.original_price, "myntra_mrp": mr.mrp,
                "match_score": best[1],
            })
matched = pd.DataFrame(pairs)
print(f"{len(matched)} matched SKU pairs")

token_set_ratio at 85 is the production-tested cutoff for India fashion — high enough to avoid cross-style confusion ("slim fit" vs "regular fit"), low enough to absorb the platforms' inconsistent title formatting.

Step 4: How do I compute the price delta and flag arbitrage?

matched["delta"] = matched.ajio_price - matched.myntra_price
matched["delta_pct"] = matched.delta / matched[["ajio_price", "myntra_price"]].min(axis=1) * 100

arbitrage = matched[matched.delta_pct.abs() >= 10].copy()
arbitrage["cheaper_on"] = arbitrage.delta.apply(lambda d: "myntra" if d > 0 else "ajio")
print(arbitrage[["brand", "name_ajio", "ajio_price", "myntra_price",
                 "delta_pct", "cheaper_on"]].head(20))

A 10%+ gap with both products in stock is the canonical arbitrage signal. Gaps below 10% are usually coupon-stack or shipping-fee noise and not worth alerting on.

Step 5: How do I post a daily delta digest to Slack?

import requests as r

def fmt(row):
    sign = "+" if row.delta > 0 else ""
    return (f"*{row.brand} — {row.name_ajio[:40]}*: "
            f"AJIO ₹{int(row.ajio_price):,} vs Myntra ₹{int(row.myntra_price):,} "
            f"({sign}{row.delta_pct:.1f}% on {row.cheaper_on})")

top = arbitrage.sort_values("delta_pct", key=abs, ascending=False).head(10)
r.post("https://hooks.slack.com/services/.../...",
       json={"text": "AJIO vs Myntra deltas today:\n" + "\n".join(top.apply(fmt, axis=1))},
       timeout=10)

For an active ops team a 10-row daily digest in Slack with the largest deltas is the right cadence — enough signal to act on without alert fatigue.

Sample output

A matched-pair record after running the joining logic above:

[
  {
    "brand": "Levi's",
    "name_ajio": "Men's 511 Slim Fit Jean",
    "name_myntra": "Men 511 Slim Fit Stretchable Jeans",
    "ajio_price": 2499,
    "myntra_price": 2799,
    "ajio_mrp": 4999,
    "myntra_mrp": 4999,
    "ajio_discount": 50,
    "myntra_discount": 44,
    "delta_pct": -12.0,
    "cheaper_on": "ajio",
    "match_score": 91
  },
  {
    "brand": "Biba",
    "name_ajio": "Printed Anarkali Kurta Set with Dupatta",
    "name_myntra": "Women Printed Anarkali Kurta with Dupatta",
    "ajio_price": 2199,
    "myntra_price": 2099,
    "ajio_mrp": 3999,
    "myntra_mrp": 3999,
    "ajio_discount": 45,
    "myntra_discount": 47,
    "delta_pct": 4.6,
    "cheaper_on": "myntra",
    "match_score": 88
  }
]

The match_score field is your trust signal — scores above 90 are safe for automated alerting; 85–90 should be human-reviewed before pricing decisions; below 85 is filtered out by the pipeline.

Common pitfalls

Three things go wrong in production AJIO-vs-Myntra trackers. Brand-string normalisation — AJIO writes "Levi's" while Myntra sometimes writes "Levis"; lower-case + strip-apostrophes before joining or you'll miss half the pairs. Coupon-stack price drift — both platforms layer additional coupons at checkout that aren't reflected in the listing price; for headline-discount marketing comparison this is fine, but for effective-cost arbitrage you'll under-call the gap. Sale-window divergence — AJIO EOSS and Myntra EORS run on overlapping but not identical calendars; some "deltas" are just one platform being mid-sale and the other not. Flag sale windows in your downstream analytics and don't treat them as steady-state signal.

Thirdwatch handles AJIO's and Myntra's anti-bot defences transparently. Both actors emit consistent, dedupable rows from a single keyword input — small enough to run hourly during EOSS, cheap enough to run daily year-round on a 300-SKU watchlist.

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