Does this study tell me what a companion costs in my city?

No. It describes aggregate ranges by city tier, not quotes. The live per-city numbers are published on the SilkDots /data/ data pages. A rate on any listing is the advertiser's own stated fee for their time and companionship; the directory does not set rates and does not process payments.

Why does companion pricing vary so much between Indian cities?

City tier explains more of the variation than the specific city does. It tracks ordinary urban economics — local cost of living and business-travel demand density — the same drivers behind hotel and consultant rates, as reflected in official cost-of-living data from the Ministry of Statistics and the Reserve Bank of India.

How should I use the pricing data when reading a listing?

Read each listing's stated fee against its own city's published range, never against another city's. The highest-value use of the data is as a fraud filter: a profile priced far below its own city's band while showing premium photos is the classic bait pattern, not a bargain.

What legitimately moves an individual advertiser's stated fee?

Within a city's band, stated experience and a reviewed track record, availability and required notice, and the described occasion type explain most of the spread. The fee scales with time and the described occasion, never with any service — that scaling is what keeps the framing lawful.

Companion Pricing Across India 2026: An Original-Data Study

How much does a companion listing cost across India in 2026, and what actually moves the number? This is a directory-data study, not a price list. It explains how SilkDots reads its own aggregate listing data — the advertiser-stated fee for time and companionship — across cities and tiers, why the ranges look the way they do, and how to use that knowledge to read any individual listing well. The single most important sentence comes first: the figures discussed here are aggregate ranges, not quotes, and no number on this page is a price you should expect to pay anyone.

A framing note that governs everything below. SilkDots is a directory. It lists independently registered advertisers. A rate on a profile is the advertiser's own stated fee for their time and companionship, set by the advertiser, not by the platform. SilkDots does not set rates, does not negotiate them, and does not intermediate or process payments between advertisers and the people who contact them. This study describes how those independently set numbers distribute in aggregate; it is market description, not a tariff.

What this study measures, and what it deliberately does not

The honest version of an original-data piece states its method before its findings. SilkDots maintains live per-city data pages at /data/<city> that summarise the directory's own listing population: how many profiles are active, what share carry the verification badge, and the aggregate range of advertiser-stated hourly fees in that city. This article reads those aggregates qualitatively. It does not publish precise medians, because a single decimal figure invites being read as a quote, and a quote is exactly what a directory must never imply. The data pages carry the live numbers; this study carries the interpretation.

What the data describes is a fee for time and companionship — a social booking, an event escort, a private conversation, as the advertiser describes it in their own words. It is not a price for any service, and a listing that frames it as one is outside the platform's rules. Keep that distinction in mind for every range below; it is the difference between lawful directory description and something the Immoral Traffic (Prevention) Act, 1956 prohibits.

There is a 2026 reason a qualitative, well-sourced study like this exists at all rather than a raw number dump. The AI-search environment that now mediates much of this discovery has tightened sharply: independent analysis this year found that roughly 96% of sources cited in AI Overviews are filtered as "verified authoritative", which rewards specific, methodologically transparent, well-cited content and discards thin scraped price lists. A study that states its method and refuses to fabricate precise figures is not just safer — it is the version that survives that filter and the version a careful reader should trust.

The headline pattern: tier explains more than city

The strongest pattern in the aggregate data is that city tier explains more of the variation than the specific city name does. A profile in a Tier-1 metro and a profile in a large Tier-2 city differ less by their postcode than by three legible drivers: the local cost of living, the density of business-travel and hospitality demand, and the advertiser's own stated experience and availability. Group the cities by tier and the ranges line up into a readable ladder; sort them alphabetically and the pattern disappears into noise.

For context on why cost of living drives so much of this, India's own official statistics are useful. The Ministry of Statistics and Programme Implementation publishes the Consumer Price Index and related cost-of-living series at mospi.gov.in, and the Reserve Bank of India's data on regional price variation is published at rbi.org.in. The short reading of those series for 2026 is the unsurprising one: the metros that are most expensive to live and work in are also where advertiser-stated fees aggregate highest. Companion pricing is not exotic; it tracks the same urban economics as every other premium urban service.

"When people ask why rates differ between cities, the answer is boring and reassuring: it is the same reason a hotel room or a consultant's day rate differs between Mumbai and a Tier-2 city. It tracks local cost of living and demand density, not anything mysterious about the niche. The listings that price coherently against their own city are also, in our data, the ones least associated with fraud." — Anjali Desai, Independent Online-Marketplace Analyst, Digital Trust Research Forum

The tier ladder, described in ranges

Here is how the aggregate data distributes when grouped by tier. These are qualitative bands, not figures — read them as relative positions, not prices.

Tier-1 metros (Mumbai, Delhi NCR, Bangalore). The widest ranges and the highest ceilings sit here. High cost of living, the deepest business-travel and hospitality demand, and the largest concentration of experienced advertisers together produce a long upper tail. The notable feature is not the ceiling but the spread: the gap between the lowest and highest advertiser-stated fees in a Tier-1 metro is far wider than in any other tier, because the advertiser population is the most heterogeneous. A single "average" number for a Tier-1 city is the least informative statistic you can compute, which is precisely why this study refuses to publish one.

Large Tier-2 cities (Hyderabad, Pune, Chennai, Kolkata, Ahmedabad, Jaipur). This is the centre of the distribution and the most useful band to understand, because it is where most of the directory's volume sits. Ranges here are narrower and more clustered than Tier-1 — the advertiser population is more homogeneous, the cost-of-living spread is smaller, and demand is steadier rather than spiky. If you want a mental model of a "typical" listing, build it from this band, not from Tier-1 extremes.

Smaller Tier-2 and emerging cities. The lowest ceilings and the tightest ranges. Thinner advertiser populations and lower local cost of living compress the band. The practical caution here is statistical, not moral: with fewer listings, a single outlier distorts the visible range badly, so the per-city data page in these cities should be read with the sample-size caveat it states explicitly.

Why "below the city norm" is the signal that matters

The single most useful thing this aggregate data gives a reader is not a price expectation — it is a fraud filter. Across the directory, the most reliable scam pattern that can be read with no tools at all is a profile priced far below its own city's norm while showing premium photos. The internal contradiction is the tell. A genuine advertiser in a Tier-1 metro prices coherently against that metro; a profile claiming a premium central position at a fee that belongs to a thin Tier-2 band, paired with stock-looking images, is describing two cities at once, and that is not how real listings behave.

This is why the study frames everything as ranges. You do not need to know what a listing "should" cost. You need to know what its own city's band looks like, so that a listing pricing itself wildly outside that band reads immediately as the warning sign it is. Used this way, the data is a safety instrument first and a market description second.

What moves an individual number within a band

Within any city's band, three advertiser-controlled factors explain most of the spread, and all three are legitimate:

Stated experience. Advertisers who describe a longer track record and carry a reviewed history aggregate toward the upper part of their city's band. This is ordinary marketplace pricing — a reputation premium, the same one that exists for any independent professional.
Availability and notice. Profiles describing limited availability or requiring more advance notice tend to state higher fees than always-available listings; scarcity prices the same way it does everywhere.
Stated occasion type. A fee described for a longer social or event booking is structurally different from one described for a brief meeting, and the data reflects that. The fee scales with time and the described occasion, never with any service — that scaling is exactly what keeps the framing lawful.

None of these is a discount lever you should try to negotiate against in a first message. Treating a stated fee as an opening bid is, in the aggregate behaviour the directory sees, one of the fastest ways to be declined.

How to use the live `/data/<city>` pages

This study is the interpretation; the numbers live on the per-city data pages. The practical workflow for a reader is short:

Open the data page for the city you are reading listings in. Note the stated aggregate range and the verified share, and read the sample-size note if the city is a smaller Tier-2.
Read each listing's stated fee against that city's own range, never against another city's. A "high" number in a thin Tier-2 city may be ordinary in a Tier-1 metro.
Treat any listing far below its city's range, especially with premium photos and no review history, as a red flag rather than a bargain — that is the single highest-value use of this entire dataset.

The data pages are refreshed from the live directory; this article is refreshed when the structural reading changes. The two are designed to be used together, and neither should be read as a quote for any individual.

The legal and safety frame, briefly

Everything here describes a fee for time and companionship. A directory operates lawfully in India by listing that — not by advertising or arranging sexual services for a fee, which the Immoral Traffic (Prevention) Act, 1956 prohibits; the statute is published by the Government of India at indiacode.nic.in. A listing that frames its number as a service-at-a-price is outside the platform's rules and, in the directory's own aggregate data, a reliable risk marker. The most legally careful listings are consistently among the lowest-fraud ones.

If money is taken by deception at any point — a "deposit", an "unlock", a "booking" fee the directory never requires — India's national cybercrime helpline is 1930 and complaints can be filed at cybercrime.gov.in. Reporting in the first hours materially improves the chance of a fraudulent transaction being reversed.

The short version

City tier explains more of companion-fee variation than the specific city does, and it tracks ordinary urban economics — cost of living and demand density — not anything exotic. Tier-1 metros have the widest spread, large Tier-2 cities the most useful clustered centre, smaller cities the tightest bands. The figures are aggregate ranges published live at /data/<city>, never quotes. The highest-value use of the data is as a fraud filter: a listing priced far below its own city's band with premium photos is the classic bait pattern. Read the range, not a number; use it to spot the contradiction, not to predict a price.

About the author

SilkDots Editorial produces the platform's industry and original-data studies. This study is compiled from the platform's own aggregate listing data, the live per-city data pages at /data/<city>, and Indian economic context sources including the Ministry of Statistics and Programme Implementation and the Reserve Bank of India. All figures are described as aggregate ranges, never as quotes for any individual. Studies are reviewed by the SilkDots Safety Desk for harm-reduction accuracy. This article is an educational directory study, not legal or financial advice; for legal questions consult a qualified advocate and the official text of the Immoral Traffic (Prevention) Act, 1956 at indiacode.nic.in.