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Sector data
  • Monthly card spendCard spend by month, January to September, in $K. September (month to date) is 40K; the highest month is July at 71K.48KJAN52KFEB61KMAR57KAPR66KMAY59KJUN71KJUL68KAUG40KSEPMONTH TO DATECLOSED MONTHS
    Monthly card spend
    MonthSpend
    JAN$48K
    FEB$52K
    MAR$61K
    APR$57K
    MAY$66K
    JUN$59K
    JUL$71K
    AUG$68K
    SEP$40K
    Monthly card spendCard spend by month, January to September, in $K. September (month to date) is 40K; the highest month is July at 71K.48KJAN52K61KMAR57K66KMAY59K71KJUL68K40KSEPMONTH TO DATECLOSED MONTHS
    Monthly card spend
    MonthSpend
    JAN$48K
    FEB$52K
    MAR$61K
    APR$57K
    MAY$66K
    JUN$59K
    JUL$71K
    AUG$68K
    SEP$40K

    001 · BarsCompare items · Fintech data

  • Signups by planNew paid accounts per month by plan, July to December 2026: Starter 2,980, Team 1,960, Business 880; December has Starter 570, Team 410 and Business 190.6004002000JULAUGSEPOCTNOVDECSTARTERTEAMBUSINESS
    Signups by plan
    MonthStarterTeamBusiness
    JUL420260110
    AUG460300120
    SEP510290150
    OCT480340140
    NOV540360170
    DEC570410190
    Signups by planNew paid accounts per month by plan, July to December 2026: Starter 2,980, Team 1,960, Business 880; December has Starter 570, Team 410 and Business 190.6004002000JULAUGSEPOCTNOVDECSTARTERTEAMBUSINESS
    Signups by plan
    MonthStarterTeamBusiness
    JUL420260110
    AUG460300120
    SEP510290150
    OCT480340140
    NOV540360170
    DEC570410190

    002 · Grouped barsCompare items · SaaS data

  • Failed payments by reasonFailed payments per week by reason, W27 to W40: 552 in W27, 214 in W40 (118 card declined, 64 insufficient funds, 32 fraud block).6004002000W27W30W33W36W40CARD DECLINEDINSUFFICIENT FUNDSFRAUD BLOCK
    Failed payments by reason
    WeekCard declinedInsufficient fundsFraud blockTotal
    W2730416682552
    W2829616181538
    W2929816281541
    W3027915276507
    W3126914773489
    W3226214371476
    W3324913667452
    W3423712965431
    W3521911960398
    W3618810351342
    W371719347311
    W381528341276
    W391367437247
    W401186432214
    Failed payments by reasonFailed payments per week by reason, W27 to W40: 552 in W27, 214 in W40 (118 card declined, 64 insufficient funds, 32 fraud block).6004002000W27W30W33W36W40CARD DECLINEDINSUFFICIENT FUNDSFRAUD BLOCK
    Failed payments by reason
    WeekCard declinedInsufficient fundsFraud blockTotal
    W2730416682552
    W2829616181538
    W2929816281541
    W3027915276507
    W3126914773489
    W3226214371476
    W3324913667452
    W3423712965431
    W3521911960398
    W3618810351342
    W371719347311
    W381528341276
    W391367437247
    W401186432214

    003 · Stacked barsPart of a whole · Fintech data

  • Net cash flowWeekly inflow (up) and outflow (down) in $K, W29 to W40: inflow totals $3,660K, outflow $2,790K, net $870K.400K0−400KW29W33W37W40INFLOWOUTFLOW
    Net cash flow
    WeekInflow ($K)Outflow ($K)
    W29220180
    W30260210
    W31240230
    W32300200
    W33280240
    W34330220
    W35310260
    W36290250
    W37360230
    W38340270
    W39380240
    W40350260
    Net cash flowWeekly inflow (up) and outflow (down) in $K, W29 to W40: inflow totals $3,660K, outflow $2,790K, net $870K.400K0−400KW29W33W37W40INFLOWOUTFLOW
    Net cash flow
    WeekInflow ($K)Outflow ($K)
    W29220180
    W30260210
    W31240230
    W32300200
    W33280240
    W34330220
    W35310260
    W36290250
    W37360230
    W38340270
    W39380240
    W40350260

    004 · Diverging barsCompare items · Fintech data

  • Delivery windows by routeDelivery window per route (earliest to latest arrival, with the median), 8 routes; 2 routes start at or after the 10:30 SLA; R8 runs 12:00 to 17:00.18:0014:0010:0006:00R1R2R3R4R5R6R7R8WITHIN SLAOVER SLA
    Delivery windows by route
    RouteEarliestLatestMedianSLA
    R108:0011:0009:30Within
    R209:0013:0011:00Within
    R307:3010:0008:30Within
    R410:0015:0012:30Within
    R508:0012:0010:00Within
    R611:0016:0013:00Over
    R709:0012:0010:30Within
    R812:0017:0014:00Over
    Delivery windows by routeDelivery window per route (earliest to latest arrival, with the median), 8 routes; 2 routes start at or after the 10:30 SLA; R8 runs 12:00 to 17:00.18:0014:0010:0006:00R1R3R5R8WITHIN SLAOVER SLA
    Delivery windows by route
    RouteEarliestLatestMedianSLA
    R108:0011:0009:30Within
    R209:0013:0011:00Within
    R307:3010:0008:30Within
    R410:0015:0012:30Within
    R508:0012:0010:00Within
    R611:0016:0013:00Over
    R709:0012:0010:30Within
    R812:0017:0014:00Over

    005 · Floating range barsDistribution · Logistics data

  • Response latency distribution96,224 requests in 25 ms latency bins from 0 to 900 ms; p50 is 250 ms, p95 is 475 ms.15K10K5K0P50 250 MSP95 475 MS0225 MS450 MS900 MSWITHIN P95TAIL ABOVE P95
    Response latency distribution
    LatencyRequests
    0–25 ms60
    25–50 ms60
    50–75 ms297
    75–100 ms1,845
    100–125 ms4,755
    125–150 ms7,829
    150–175 ms9,963
    175–200 ms10,800
    200–225 ms10,537
    225–250 ms9,569
    250–275 ms8,264
    275–300 ms6,884
    300–325 ms5,588
    325–350 ms4,451
    350–375 ms3,497
    375–400 ms2,720
    400–425 ms2,100
    425–450 ms1,614
    450–475 ms1,236
    475–500 ms944
    500–525 ms721
    525–550 ms550
    550–575 ms420
    575–600 ms321
    600–625 ms245
    625–650 ms188
    650–675 ms144
    675–700 ms111
    700–725 ms85
    725–750 ms66
    750–775 ms60
    775–800 ms60
    800–825 ms60
    825–850 ms60
    850–875 ms60
    875–900 ms60
    Response latency distribution96,224 requests in 25 ms latency bins from 0 to 900 ms; p50 is 250 ms, p95 is 475 ms.15K10K5K0P50 250 MSP95 475 MS0225 MS450 MS900 MSWITHIN P95TAIL ABOVE P95
    Response latency distribution
    LatencyRequests
    0–25 ms60
    25–50 ms60
    50–75 ms297
    75–100 ms1,845
    100–125 ms4,755
    125–150 ms7,829
    150–175 ms9,963
    175–200 ms10,800
    200–225 ms10,537
    225–250 ms9,569
    250–275 ms8,264
    275–300 ms6,884
    300–325 ms5,588
    325–350 ms4,451
    350–375 ms3,497
    375–400 ms2,720
    400–425 ms2,100
    425–450 ms1,614
    450–475 ms1,236
    475–500 ms944
    500–525 ms721
    525–550 ms550
    550–575 ms420
    575–600 ms321
    600–625 ms245
    625–650 ms188
    650–675 ms144
    675–700 ms111
    700–725 ms85
    725–750 ms66
    750–775 ms60
    775–800 ms60
    800–825 ms60
    825–850 ms60
    850–875 ms60
    875–900 ms60

    006 · HistogramDistribution · AI data

  • Deliveries by hubParcels delivered today of those scheduled, across 5 hubs: 4,645 of 5,440; Rotterdam delivered the most at 1,312 of 1,480.04008001.2K1.6KROTTERDAM1,312HAMBURG1,034ANTWERP1,046LYON598MILAN655DELIVEREDSCHEDULED
    Deliveries by hub
    HubDeliveredScheduled
    Rotterdam1,3121,480
    Hamburg1,0341,260
    Antwerp1,0461,120
    Lyon598860
    Milan655720
    Deliveries by hubParcels delivered today of those scheduled, across 5 hubs: 4,645 of 5,440; Rotterdam delivered the most at 1,312 of 1,480.04008001.2K1.6KROTTERDAM1,312HAMBURG1,034ANTWERP1,046LYON598MILAN655DELIVEREDSCHEDULED
    Deliveries by hub
    HubDeliveredScheduled
    Rotterdam1,3121,480
    Hamburg1,0341,260
    Antwerp1,0461,120
    Lyon598860
    Milan655720

    007 · Horizontal barsCompare items · Logistics data

  • Q3 goalsQ3 goals, actual against target: revenue $428K of $500K, orders 6,120 of 7,000, avg order $70 of $68, repeat buyers 31% of 35%.RevenueUSD$428KOF $500KOrdersCOUNT6,120OF 7,000Avg orderUSD$70OF $68Repeat buyersSHARE31%OF 35%ACTUALTARGETRANGES
    Q3 goals
    GoalUnitActualTarget
    RevenueUsd$428K$500K
    OrdersCount6,1207,000
    Avg orderUsd$70$68
    Repeat buyersShare31%35%
    Q3 goalsQ3 goals, actual against target: revenue $428K of $500K, orders 6,120 of 7,000, avg order $70 of $68, repeat buyers 31% of 35%.RevenueUSD$428KOF $500KOrdersCOUNT6,120OF 7,000Avg orderUSD$70OF $68Repeat buyersSHARE31%OF 35%ACTUALTARGETRANGES
    Q3 goals
    GoalUnitActualTarget
    RevenueUsd$428K$500K
    OrdersCount6,1207,000
    Avg orderUsd$70$68
    Repeat buyersShare31%35%

    008 · BulletCompare items · E-commerce data

  • Deposits by regionNew deposits in October by region, $1634K in total: East $478K, Midwest $386K, South $596K, West $174K.$478K$386K$596K$174KEASTMIDWESTSOUTHWEST
    Deposits by region
    RegionDeposits
    East$478K
    Midwest$386K
    South$596K
    West$174K
    Deposits by regionNew deposits in October by region, $1634K in total: East $478K, Midwest $386K, South $596K, West $174K.$478K$386K$596K$174KEASTMIDWESTSOUTHWEST
    Deposits by region
    RegionDeposits
    East$478K
    Midwest$386K
    South$596K
    West$174K

    009 · LollipopCompare items · Fintech data

  • P95 latency, us-eastP95 time to first token in us-east, hourly over the last 24 hours: 1.30 s now, peak 1.45 s, low 0.85 s.1.8 S1.2 S0.6 S000:0006:0012:0018:00NOW
    P95 latency, us-east
    HourP95
    00:000.90 s
    01:001.10 s
    02:000.95 s
    03:001.20 s
    04:000.85 s
    05:001.00 s
    06:001.30 s
    07:001.05 s
    08:001.15 s
    09:000.90 s
    10:001.25 s
    11:001.40 s
    12:001.10 s
    13:001.00 s
    14:001.35 s
    15:001.20 s
    16:000.95 s
    17:001.10 s
    18:001.45 s
    19:001.15 s
    20:001.05 s
    21:001.30 s
    22:001.20 s
    Now1.30 s
    P95 latency, us-eastP95 time to first token in us-east, hourly over the last 24 hours: 1.30 s now, peak 1.45 s, low 0.85 s.1.8 S1.2 S0.6 S000:0006:0012:0018:00NOW
    P95 latency, us-east
    HourP95
    00:000.90 s
    01:001.10 s
    02:000.95 s
    03:001.20 s
    04:000.85 s
    05:001.00 s
    06:001.30 s
    07:001.05 s
    08:001.15 s
    09:000.90 s
    10:001.25 s
    11:001.40 s
    12:001.10 s
    13:001.00 s
    14:001.35 s
    15:001.20 s
    16:000.95 s
    17:001.10 s
    18:001.45 s
    19:001.15 s
    20:001.05 s
    21:001.30 s
    22:001.20 s
    Now1.30 s

    010 · LineChange over time · AI data

  • Daily closeDaily close from Sep 10 to Oct 9: from 3,211 to 3,412.3.5K3.4K3.3K3.2KSEP 10SEP 17SEP 24OCT 1OCT 9
    Daily close
    DayClose
    Sep 103,211
    Sep 113,231
    Sep 123,224
    Sep 133,214
    Sep 143,226
    Sep 153,229
    Sep 163,224
    Sep 173,237
    Sep 183,250
    Sep 193,254
    Sep 203,263
    Sep 213,271
    Sep 223,287
    Sep 233,278
    Sep 243,275
    Sep 253,286
    Sep 263,296
    Sep 273,279
    Sep 283,289
    Sep 293,290
    Sep 303,310
    Oct 13,325
    Oct 23,311
    Oct 33,314
    Oct 43,334
    Oct 53,344
    Oct 63,352
    Oct 73,357
    Oct 83,376
    Oct 93,412
    Daily closeDaily close from Sep 10 to Oct 9: from 3,211 to 3,412.3.5K3.4K3.3K3.2KSEP 10SEP 24OCT 1OCT 9
    Daily close
    DayClose
    Sep 103,211
    Sep 113,231
    Sep 123,224
    Sep 133,214
    Sep 143,226
    Sep 153,229
    Sep 163,224
    Sep 173,237
    Sep 183,250
    Sep 193,254
    Sep 203,263
    Sep 213,271
    Sep 223,287
    Sep 233,278
    Sep 243,275
    Sep 253,286
    Sep 263,296
    Sep 273,279
    Sep 283,289
    Sep 293,290
    Sep 303,310
    Oct 13,325
    Oct 23,311
    Oct 33,314
    Oct 43,334
    Oct 53,344
    Oct 63,352
    Oct 73,357
    Oct 83,376
    Oct 93,412

    011 · AreaChange over time · Crypto data

  • Latency by regionP95 time to first token per hour over the last 24 hours, 4 regions; now: US-East 360 ms, EU-West 394 ms, AP-South 594 ms, SA-East 518 ms.900600300000:0006:0012:0018:00NOWUS-EASTEU-WESTAP-SOUTHSA-EAST
    Latency by region
    HourUS-East (ms)EU-West (ms)AP-South (ms)SA-East (ms)
    00:00352297663515
    01:00368326699493
    02:00358341658479
    03:00362326685479
    04:00337315696506
    05:00362340713469
    06:00343352718495
    07:00365332724533
    08:00390344726508
    09:00419330705530
    10:00430338697540
    11:00396346657526
    12:00391370625517
    13:00369394653552
    14:00354420639516
    15:00326403655539
    16:00323420647513
    17:00303394670533
    18:00317379662506
    19:00349380638497
    20:00359364618516
    21:00345393583484
    22:00370403575521
    23:00360394594518
    Latency by regionP95 time to first token per hour over the last 24 hours, 4 regions; now: US-East 360 ms, EU-West 394 ms, AP-South 594 ms, SA-East 518 ms.900600300000:0006:0012:0018:00NOWUS-EASTEU-WESTAP-SOUTHSA-EAST
    Latency by region
    HourUS-East (ms)EU-West (ms)AP-South (ms)SA-East (ms)
    00:00352297663515
    01:00368326699493
    02:00358341658479
    03:00362326685479
    04:00337315696506
    05:00362340713469
    06:00343352718495
    07:00365332724533
    08:00390344726508
    09:00419330705530
    10:00430338697540
    11:00396346657526
    12:00391370625517
    13:00369394653552
    14:00354420639516
    15:00326403655539
    16:00323420647513
    17:00303394670533
    18:00317379662506
    19:00349380638497
    20:00359364618516
    21:00345393583484
    22:00370403575521
    23:00360394594518

    012 · Multi-lineChange over time · AI data

  • Volume by venueDaily spot volume per venue in $M, Sep 10 to Oct 9; on Oct 9: Northgate $455M, Tidepool $326M, Ardent DEX $108M.600M400M200M0SEP 10SEP 20SEP 30OCT 9NORTHGATETIDEPOOLARDENT DEX
    Volume by venue
    DayNorthgate ($M)Tidepool ($M)Ardent DEX ($M)
    Sep 10384273118
    Sep 11404250137
    Sep 12392264128
    Sep 13397248138
    Sep 14365257133
    Sep 15396266148
    Sep 16372295143
    Sep 17400322127
    Sep 18432353114
    Sep 1947033391
    Sep 2048535685
    Sep 2144132698
    Sep 2243430994
    Sep 2340631077
    Sep 2438729167
    Sep 2535032567
    Sep 2634733687
    Sep 2732132660
    Sep 2833934480
    Sep 29380372108
    Sep 3039333989
    Oct 1375360106
    Oct 2407369114
    Oct 3395380103
    Oct 439038096
    Oct 5434380122
    Oct 645638095
    Oct 7433364112
    Oct 841735793
    Oct 9455326108
    Volume by venueDaily spot volume per venue in $M, Sep 10 to Oct 9; on Oct 9: Northgate $455M, Tidepool $326M, Ardent DEX $108M.600M400M200M0SEP 10SEP 20SEP 30OCT 9NORTHGATETIDEPOOLARDENT DEX
    Volume by venue
    DayNorthgate ($M)Tidepool ($M)Ardent DEX ($M)
    Sep 10384273118
    Sep 11404250137
    Sep 12392264128
    Sep 13397248138
    Sep 14365257133
    Sep 15396266148
    Sep 16372295143
    Sep 17400322127
    Sep 18432353114
    Sep 1947033391
    Sep 2048535685
    Sep 2144132698
    Sep 2243430994
    Sep 2340631077
    Sep 2438729167
    Sep 2535032567
    Sep 2634733687
    Sep 2732132660
    Sep 2833934480
    Sep 29380372108
    Sep 3039333989
    Oct 1375360106
    Oct 2407369114
    Oct 3395380103
    Oct 439038096
    Oct 5434380122
    Oct 645638095
    Oct 7433364112
    Oct 841735793
    Oct 9455326108

    013 · Overlapping areasChange over time · Crypto data

  • Shipments by modeMonthly shipments in K by mode, 2026, stacked around a centre line; October: 47K in total (Road 23K, Sea 10K, Rail 8K, Air 6K).ROAD46%SEA28%RAIL16%OCT · 47KJANAPRJULOCTDEC
    Shipments by mode
    MonthRoad (K)Sea (K)Rail (K)Air (K)
    Jan14962
    Feb13862
    Mar161073
    Apr191283
    May221584
    Jun241774
    Jul231964
    Aug211864
    Sep221585
    Oct231086
    Nov3010910
    Dec279812
    Shipments by modeMonthly shipments in K by mode, 2026, stacked around a centre line; October: 47K in total (Road 23K, Sea 10K, Rail 8K, Air 6K).ROAD46%SEA28%RAIL16%OCT · 47KJANAPRJULOCTDEC
    Shipments by mode
    MonthRoad (K)Sea (K)Rail (K)Air (K)
    Jan14962
    Feb13862
    Mar161073
    Apr191283
    May221584
    Jun241774
    Jul231964
    Aug211864
    Sep221585
    Oct231086
    Nov3010910
    Dec279812

    014 · StreamgraphChange over time · Logistics data

  • Cash balance forecastCash balance from May to October (actual, $2.41M today) and forecast to April: $2.66M, with a p10–p90 band of $2.24M to $3.08M.3.2M2.7M2.3M1.8MMAYOCTAPRACTUALFORECAST
    Cash balance forecast
    MonthBalanceP10P90Kind
    May$2.10M$2.08M$2.12MActual
    Jun$2.20M$2.18M$2.22MActual
    Jul$2.15M$2.13M$2.17MActual
    Aug$2.30M$2.28M$2.32MActual
    Sep$2.35M$2.33M$2.37MActual
    Oct$2.41M$2.39M$2.43MActual
    Nov$2.46M$2.39M$2.53MForecast
    Dec$2.50M$2.36M$2.64MForecast
    Jan$2.52M$2.31M$2.73MForecast
    Feb$2.58M$2.30M$2.86MForecast
    Mar$2.60M$2.25M$2.95MForecast
    Apr$2.66M$2.24M$3.08MForecast
    Cash balance forecastCash balance from May to October (actual, $2.41M today) and forecast to April: $2.66M, with a p10–p90 band of $2.24M to $3.08M.3.2M2.7M2.3M1.8MMAYOCTAPRACTUALFORECAST
    Cash balance forecast
    MonthBalanceP10P90Kind
    May$2.10M$2.08M$2.12MActual
    Jun$2.20M$2.18M$2.22MActual
    Jul$2.15M$2.13M$2.17MActual
    Aug$2.30M$2.28M$2.32MActual
    Sep$2.35M$2.33M$2.37MActual
    Oct$2.41M$2.39M$2.43MActual
    Nov$2.46M$2.39M$2.53MForecast
    Dec$2.50M$2.36M$2.64MForecast
    Jan$2.52M$2.31M$2.73MForecast
    Feb$2.58M$2.30M$2.86MForecast
    Mar$2.60M$2.25M$2.95MForecast
    Apr$2.66M$2.24M$3.08MForecast

    015 · Forecast bandChange over time · Fintech data

  • Accuracy by context lengthNeedle recall by context length: Atlas 3 falls from 97% at 4K to 77% at 128K; Kite 2 Pro from 93% to 84%.4K16K32K64K128K60%70%80%90%100%ATLAS 3KITE 2 PRO
    Accuracy by context length
    ContextAtlas 3Kite 2 Pro
    4K97%93%
    16K95%92%
    32K92%90%
    64K86%88%
    128K77%84%
    Accuracy by context lengthNeedle recall by context length: Atlas 3 falls from 97% at 4K to 77% at 128K; Kite 2 Pro from 93% to 84%.4K16K32K64K128K60%70%80%90%100%ATLAS 3KITE 2 PRO
    Accuracy by context length
    ContextAtlas 3Kite 2 Pro
    4K97%93%
    16K95%92%
    32K92%90%
    64K86%88%
    128K77%84%

    016 · Vertical profileDistribution · AI data

  • MRR by planMonthly recurring revenue of $184K by plan: Enterprise $82K (45%), Business $54K (29%), Team $31K (17%), Starter $17K (9%).Enterprise$82KBusiness$54KTeam$31KStarter$17K
    MRR by plan
    PlanMRRShare
    Enterprise$82K45%
    Business$54K29%
    Team$31K17%
    Starter$17K9%
    MRR by planMonthly recurring revenue of $184K by plan: Enterprise $82K (45%), Business $54K (29%), Team $31K (17%), Starter $17K (9%).Enterprise$82KBusiness$54KTeam$31KStarter$17K
    MRR by plan
    PlanMRRShare
    Enterprise$82K45%
    Business$54K29%
    Team$31K17%
    Starter$17K9%

    017 · DonutPart of a whole · SaaS data

  • Orders by channel3,412 orders by channel: Web 52%, App 28%, Market 14%, Retail 7%.ORDERS3,412WEB 52%APP 28%MARKET 14%RETAIL 7%
    Orders by channel
    ChannelShare
    Web52%
    App28%
    Market14%
    Retail7%
    Orders by channel3,412 orders by channel: Web 52%, App 28%, Market 14%, Retail 7%.ORDERS3,412WEB 52%APP 28%MARKET 14%RETAIL 7%
    Orders by channel
    ChannelShare
    Web52%
    App28%
    Market14%
    Retail7%

    018 · Semi donutPart of a whole · E-commerce data

  • Signups by channel13.8K new trial signups in September across six channels: Organic leads with 4.2K (30%), Events is smallest with 0.6K (4%).Organic4.2KPaid search3.4KReferral2.6KSocial1.8KPartners1.2KEvents0.6K
    Signups by channel
    ChannelSignupsShare
    Organic420030%
    Paid search340025%
    Referral260019%
    Social180013%
    Partners12009%
    Events6004%
    Signups by channel13.8K new trial signups in September across six channels: Organic leads with 4.2K (30%), Events is smallest with 0.6K (4%).Organic4.2KPaid search3.4KReferral2.6KSocial1.8KPartners1.2KEvents0.6K
    Signups by channel
    ChannelSignupsShare
    Organic420030%
    Paid search340025%
    Referral260019%
    Social180013%
    Partners12009%
    Events6004%

    019 · Polar areaPart of a whole · SaaS data

  • Token budget7.2M of a 10M token budget used (72%), 2.8M remaining; the forecast at reset is 10.4M, and the budget resets in 9 days.72%USEDUsed7.2MRemaining2.8MForecast at reset10.4MResets in9 days
    Token budget
    ItemValue
    Used7.2M
    Remaining2.8M
    Forecast at reset10.4M
    Resets in9 days
    Token budget7.2M of a 10M token budget used (72%), 2.8M remaining; the forecast at reset is 10.4M, and the budget resets in 9 days.72%USEDUsed7.2MRemaining2.8MForecast at reset10.4MResets in9 days
    Token budget
    ItemValue
    Used7.2M
    Remaining2.8M
    Forecast at reset10.4M
    Resets in9 days

    020 · Progress ringPart of a whole · AI data

FAQ

Picking a chart.

Grouped the way people ask them: about choosing a type, about the Figma file and about the code.

Something else?

hello@usechartory.com
  • Start from the question your data answers. The eight chips above sort the 100+ types by that question, and each type page says when to use it and which type fits better when it does not.