| Month | Free signups | Pro | Premium | Ultimate | Paid total | Free per paid |
|---|---|---|---|---|---|---|
| January | 4,772 | 2 | 26 | 5 | 33 | 145 : 1 |
| February | 4,114 | 4 | 16 | 2 | 22 | 187 : 1 |
| March | 3,599 | 11 | 20 | 2 | 33 | 109 : 1 |
| April | 5,312 | 5 | 13 | 7 | 25 | 212 : 1 |
| May | 4,063 | 8 | 9 | 6 | 23 | 177 : 1 |
| June | 4,102 | 12 | 14 | 8 | 34 | 121 : 1 |
| July | 4,494 | 5 | 6 | 8 | 19 | 237 : 1 |
| August (to 28th) | 4,606 | 4 | 2 | 4 | 10 | 461 : 1 |
| Total | 35,062 | 51 | 106 | 42 | 199 | 176 : 1 |
Free acquisition is healthy and steady at ~4,400 a month. Paid conversion has fallen 70% — from 33 new paid accounts in January to 10 in August. Premium is the worst hit: 26 in January, 2 in August. The free-to-paid ratio has widened from 145:1 to 461:1. Nothing about the free tier changed; what changed is the rate at which we convert off it.
| Month | Active users | Total calls | Paid-user calls | Free-user calls | Free share | Talk hrs | Total cost | Free-user cost |
|---|---|---|---|---|---|---|---|---|
| January | 218 | 386 | 21 | 365 | 94.6% | 20 | $125 | $113 |
| February | 292 | 699 | 324 | 375 | 53.6% | 26 | $141 | $82 |
| March | 557 | 3,333 | 300 | 3,033 | 91.0% | 71 | $413 | $368 |
| April | 908 | 5,428 | 319 | 5,109 | 94.1% | 114 | $632 | $591 |
| May | 1,563 | 15,708 | 482 | 15,226 | 96.9% | 154 | $908 | $839 |
| June | 1,758 | 26,336 | 369 | 25,967 | 98.6% | 166 | $984 | $920 |
| July | 1,910 | 18,335 | 293 | 18,042 | 98.4% | 207 | $1,315 | $1,251 |
| August (to 28th) | 1,470 | 10,372 | 118 | 10,254 | 98.9% | 157 | $1,062 | $1,041 |
| Total | — | 80,597 | 2,226 | 78,371 | 97.2% | 915 | $5,580 | $5,205 |
Free users take 97.2% of AI calls and 93.3% of the AI spend, and the free share has climbed every month since February. But keep the magnitude honest: the entire eight-month AI voice bill is about $5,580 — roughly ₹4.9 lakh, against ₹94.5L of sales in the same period. This is a ~5% cost line, not an existential one.
| Calls taken in 30 days | Users | Calls | Talk hrs | Cost | Cost per user |
|---|---|---|---|---|---|
| 1 | 272 | 272 | 30 | $167 | $0.62 |
| 2–3 | 338 | 815 | 39 | $228 | $0.67 |
| 4–7 | 388 | 2,166 | 49 | $331 | $0.85 |
| 8–15 | 626 | 7,138 | 53 | $407 | $0.65 |
| 16–31 | 99 | 1,787 | 11 | $88 | $0.89 |
There is no abuse problem. The heaviest free user in the last 30 days took 29 calls and cost $1.26. The single most expensive free user cost $4.20. Cost per active user is flat at roughly $0.65–0.89 across every usage band, so no gating threshold would recover meaningful money. All 25 of the top consumers are on the FREE plan — but that is unremarkable when 99.8% of accounts are free.
Note the direction of causation: these are outbound calls we place to them, not a service they pull on demand. Heavy AI usage is us dialling repeatedly, not a user consuming a free resource. A usage cap would be capping our own outreach.
| Month | Jan | Feb | Mar | Apr | May | Jun | Jul | Aug |
|---|---|---|---|---|---|---|---|---|
| Assistant messages | 79,725 | 63,539 | 60,794 | 93,136 | 66,580 | 59,277 | 46,217 | 40,500 |
509,768 assistant messages since January, and the trend is falling — down 49% from the April peak. Whatever the free tier is costing in LLM inference, it is shrinking, not growing. Per-message cost is not stored, so this cannot be converted to rupees from the database. That number has to come from the provider bill.
This replaces the estimate I refused to make. The provider export gives 31 days of actual organisation-level LLM spend, so the text side of the platform now has a real number attached to it.
| Measure | Value | Note |
|---|---|---|
| Total, 31 days | $747.25 | 29 Jul – 28 Aug 2026, organisation-wide |
| Daily average | $24.10 | range $5.33 – $65.74 |
| 30-day run rate | $723 | ≈ ₹63,600 per month |
| Annualised at this rate | $8,798 | ≈ ₹7.7 lakh a year |
| Assistant turns in window | 27,180 | plus 18,887 user turns |
| Cost per assistant reply | $0.0275 | ≈ ₹2.42 per Anika message |
| Cost per user message answered | $0.0396 | ≈ ₹3.48 per inbound text handled |
| Week | Spend | Per day |
|---|---|---|
| 29 Jul – 4 Aug | $230.09 | $32.87 |
| 5 – 11 Aug | $241.36 | $34.48 |
| 12 – 18 Aug | $113.78 | $16.25 |
| 19 – 25 Aug | $123.90 | $17.70 |
| 26 – 28 Aug | $38.13 | $12.71 |
Daily chat spend has fallen 61% inside a single month — $34.48/day in early August to $12.71/day by the 28th. That tracks the message-volume decline on the row above (Anika's replies are down ~45% since April). Whatever is driving it is worth understanding before it is treated as a saving: a quieter assistant is cheaper, but the Spaces engagement data says text is one of the few things paying users actually do.
| AI cost line | Per month | Per month ₹ | Basis |
|---|---|---|---|
| Voice (all segments, all directions) | $698 | ₹61,400 | $5,580 over 8 months, measured per call |
| Chat / LLM | $723 | ₹63,600 | provider export, 31 days |
| Total AI | $1,421 | ₹1.25 lakh | ≈ ₹15 lakh a year at this rate |
| Segment | New threads in window | Share | Allocated chat cost |
|---|---|---|---|
| Free | 3,064 | 97.1% | $726 · ₹63,900 |
| Individual services | 79 | 2.5% | $19 |
| Pro | 6 | 0.2% | $1.4 |
| Premium | 3 | 0.1% | $0.7 |
| Ultimate | 4 | 0.1% | $0.9 |
| Total | 3,156 | 100% | $747 |
Treat this split as an upper bound on free, not a precise allocation. The export carries no
user_id, project_id or line_item — every row is organisation-total — so cost has to be
allocated by an activity proxy. The proxy used here is threads created in the window, which is the only
per-segment signal the indexes support. It overstates free: a paying user typically continues an existing
thread rather than opening a new one, so their ongoing message volume is invisible to this measure.
A proper split still needs the single-field index on assistant_thread.gameplayerId, or an export
with user_id populated.
| Segment | Definition | Users | Share |
|---|---|---|---|
| Free | No entitlement of any kind. Signed up, never bought anything. | 302,442 | 99.66% |
| Individual services | Bought a one-off service — ₹299 consultation, SoP review, visa filing — but no plan. | 438 | 0.14% |
| Pro | ₹2,499 plan | 341 | 0.11% |
| Premium | ₹34,999 plan | 200 | 0.07% |
| Ultimate | ₹69,999 plan | 55 | 0.02% |
| Total | — | 303,476 | 100% |
Plan segment from entitlements.activePlan.planName; individual-service buyers are entitlement holders
with no plan. Free is the remainder of platform_subscriptions. A user is counted in their highest tier only.
| Direction | Calls | Share | Talk hrs | Avg call | Cost | Share of cost |
|---|---|---|---|---|---|---|
| Outbound — we dial them | 75,143 | 93.2% | 688 | 33 s | $4,354 | 78.0% |
| Inbound — they call us (WhatsApp voice) | 4,488 | 5.6% | 180 | 145 s | $940 | 16.8% |
| Other / untagged | 969 | 1.2% | 47 | 174 s | $286 | 5.2% |
| Total | 80,600 | 100% | 915 | 41 s | $5,580 | 100% |
An inbound call lasts 4.4× longer than an outbound one — 145 seconds against 33. Inbound is 5.6% of call volume but 16.8% of the cost, because when someone rings us they actually talk. Outbound is cheap per call precisely because most of it never connects.
| Segment | Users | Outbound calls | Outbound $ | Inbound calls | Inbound $ | Other $ | Total $ | $ per user |
|---|---|---|---|---|---|---|---|---|
| Free | 302,442 | 74,033 | $4,204 | 2,612 | $684 | $163 | $5,051 | $0.017 |
| Individual | 438 | 727 | $72 | 255 | $54 | $29 | $154 | $0.35 |
| Pro | 341 | 20 | $4 | 142 | $20 | $28 | $51 | $0.15 |
| Premium | 200 | 220 | $42 | 1,172 | $152 | $49 | $243 | $1.21 |
| Ultimate | 55 | 143 | $33 | 307 | $30 | $18 | $81 | $1.48 |
| Total | 303,476 | 75,143 | $4,354 | 4,488 | $940 | $286 | $5,580 | $0.018 |
The direction of the call tells you which segment it is. Free users take 98.5% of outbound — we chase them. Paying users generate 41.8% of inbound from 0.34% of the accounts — they ring us.
| Inbound calls per user, over eight months | Rate | vs Free |
|---|---|---|
| Free | 0.009 | — |
| Pro | 0.42 | 48× |
| Individual services | 0.58 | 67× |
| Ultimate | 5.58 | 646× |
| Premium | 5.86 | 678× |
A Premium user calls in 678 times more often than a free user. That is the clearest engagement signal in the entire dataset — and it costs $1.21 per user across eight months against ₹34,999 of revenue. Inbound volume is a retention asset, not a cost problem. Premium alone accounts for 26% of all inbound calls from 200 accounts.
| Direction | Calls | Connected | Talk hours | Share of hours |
|---|---|---|---|---|
| Outbound | 29,601 | 14,140 | 951 | 94.3% |
| Inbound | 5,228 | 1,580 | 58 | 5.7% |
| Total | 34,829 | 15,720 | 1,009 | 100% |
1,009 counsellor hours in eight months — this is the real cost line, and it dwarfs the AI. At a fully loaded ₹400/hour that is roughly ₹4.0 lakh of human time, against ₹4.9 lakh of AI voice. Comparable in rupees, but the AI number covers 80,600 calls and the human number covers 34,829. Per connected conversation, a counsellor costs roughly 20× what the AI does.
Only 45% of coach dials connect. Inbound coach calls connect at 30% — worse than outbound at 48% — which
suggests missed inbound calls are being logged. That is a service-level problem worth its own look.
Per-segment attribution of coach hours is not computed here: argonaut_counsellors_call_history keys on
lead_id while segments key on gamePlayerId, and the bridge is not indexed for a platform-wide join.
| Month | Jan | Feb | Mar | Apr | May | Jun | Jul | Aug | Total |
|---|---|---|---|---|---|---|---|---|---|
| Space messages — human & system | 50,098 | 43,021 | 45,493 | 65,999 | 64,838 | 81,558 | 70,175 | 52,414 | 473,596 |
| Anika replies | 30,831 | 23,805 | 27,421 | 36,514 | 14,336 | 19,677 | 18,255 | 16,812 | 187,651 |
| Assistant LLM turns | 79,725 | 63,539 | 60,794 | 93,136 | 66,580 | 59,277 | 46,217 | 40,500 | 509,768 |
Anika wrote 187,651 replies in 2026 — 28% of everything in the Spaces. Volume peaked in April and has roughly halved since; Anika's share of the conversation dropped sharply after April, which looks like a deliberate change worth confirming with engineering.
assistant_conversation stores the message text but no token counts and no cost.
Only the voice collection carries a metadata.cost field. Estimating from character length would
undercount badly, because the billed prompt re-sends the system context and full history on every turn —
none of which is stored per call. Any number produced that way would be wrong by a multiple, so I have not produced one.
Per-segment text volume also could not be computed.
assistant_thread.gameplayerId is only indexed compound with createdAt, so a segment-wide join
times out. A single-field index on gameplayerId, or a one-off export, unblocks it.
| Segment | Users | Voice cost / user 8 months | Plan price | Voice cost as % of revenue |
|---|---|---|---|---|
| Free | 302,442 | $0.017 | ₹0 | undefined — no revenue |
| Individual | 438 | $0.35 | ₹299–15,000 | < 10% |
| Pro | 341 | $0.15 | ₹2,499 | 0.5% |
| Premium | 200 | $1.21 | ₹34,999 | 0.3% |
| Ultimate | 55 | $1.48 | ₹69,999 | 0.2% |
Voice cost is a rounding error against every paid tier — under 0.5% of revenue in all four. The heaviest users of the platform are also the most profitable ones by a wide margin. Nothing here supports throttling a paying segment, and the free segment costs $0.017 a head over eight months.
The one genuine exposure is free outbound: $4,204 spent dialling people who have never paid, producing 78% of total voice spend for the segment with no revenue attached. That is the line to manage — not by closing the platform, but by dialling fewer, better-qualified people, which is exactly what the ₹299 cohort analysis argues for.
The premise does not survive the data. "We can't keep the platform too open for too long" assumes open access is costing us. Three findings say otherwise:
The real finding on this page is the conversion collapse — 33 new paid accounts in January against 10 in August, with Premium down from 26 to 2. That is a monetisation problem sitting on top of a healthy and stable top of funnel, and closing the platform would treat the symptom we do not have while making the one we do have worse.
If the goal is to gate something, gate the expensive human, not the cheap software. Counsellor time and G-Meets are where real money goes, and the companion ₹299 analysis shows they are already being rationed — just arbitrarily rather than deliberately.
user_id, project_id and line_item columns are all empty, so chat cannot be separated from the voice agent's own LLM calls, program-finder, or SoP generation — nor split by user. Ask for the same export with those dimensions populated.assistant_thread.gameplayerId (single-field). Today it exists only compound with createdAt, which makes per-segment text attribution time out. This is a five-minute change that unlocks the text-cost analysis.gamePlayerId on argonaut_counsellors_call_history, or a maintained lead↔player bridge table. Without it, counsellor hours — the largest cost line on this page — cannot be split by segment.
Sources — platform_subscriptions (plan and signup month), ai_voice_conversation
(metadata.cost, durationSeconds; paid users identified by joining gamePlayerId),
assistant_conversation (message volume). Costs are provider-reported USD for the AI voice stack only and
exclude telephony, infrastructure and LLM chat. Months are IST; August runs to the 28th. Pulled 28 Aug 2026 —
a snapshot, not live.