costs.ipynb

{
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>model</th>\n",
       "      <th>solution</th>\n",
       "      <th>prompt_tokens</th>\n",
       "      <th>completion_tokens</th>\n",
       "      <th>prompt_cost</th>\n",
       "      <th>completion_cost</th>\n",
       "      <th>total_cost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1697102416</td>\n",
       "      <td>gpt-4</td>\n",
       "      <td>solution_1</td>\n",
       "      <td>275</td>\n",
       "      <td>32</td>\n",
       "      <td>0.007700</td>\n",
       "      <td>0.001792</td>\n",
       "      <td>0.009492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1697109825</td>\n",
       "      <td>gpt-4</td>\n",
       "      <td>solution_1</td>\n",
       "      <td>275</td>\n",
       "      <td>29</td>\n",
       "      <td>0.007700</td>\n",
       "      <td>0.001624</td>\n",
       "      <td>0.009324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1697109853</td>\n",
       "      <td>gpt-4</td>\n",
       "      <td>solution_1</td>\n",
       "      <td>319</td>\n",
       "      <td>41</td>\n",
       "      <td>0.008932</td>\n",
       "      <td>0.002296</td>\n",
       "      <td>0.011228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1697109880</td>\n",
       "      <td>gpt-4</td>\n",
       "      <td>solution_1</td>\n",
       "      <td>375</td>\n",
       "      <td>39</td>\n",
       "      <td>0.010500</td>\n",
       "      <td>0.002184</td>\n",
       "      <td>0.012684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1697109891</td>\n",
       "      <td>gpt-4</td>\n",
       "      <td>solution_1</td>\n",
       "      <td>427</td>\n",
       "      <td>48</td>\n",
       "      <td>0.011956</td>\n",
       "      <td>0.002688</td>\n",
       "      <td>0.014644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1697109903</td>\n",
       "      <td>gpt-4</td>\n",
       "      <td>solution_1</td>\n",
       "      <td>484</td>\n",
       "      <td>37</td>\n",
       "      <td>0.013552</td>\n",
       "      <td>0.002072</td>\n",
       "      <td>0.015624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1697109950</td>\n",
       "      <td>gpt-4</td>\n",
       "      <td>solution_1</td>\n",
       "      <td>535</td>\n",
       "      <td>36</td>\n",
       "      <td>0.014980</td>\n",
       "      <td>0.002016</td>\n",
       "      <td>0.016996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1697109968</td>\n",
       "      <td>gpt-4</td>\n",
       "      <td>solution_1</td>\n",
       "      <td>590</td>\n",
       "      <td>47</td>\n",
       "      <td>0.016520</td>\n",
       "      <td>0.002632</td>\n",
       "      <td>0.019152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1697109978</td>\n",
       "      <td>gpt-4</td>\n",
       "      <td>solution_1</td>\n",
       "      <td>646</td>\n",
       "      <td>34</td>\n",
       "      <td>0.018088</td>\n",
       "      <td>0.001904</td>\n",
       "      <td>0.019992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1697110318</td>\n",
       "      <td>gpt-35-turbo</td>\n",
       "      <td>solution_2</td>\n",
       "      <td>160</td>\n",
       "      <td>27</td>\n",
       "      <td>0.000224</td>\n",
       "      <td>0.000051</td>\n",
       "      <td>0.000275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1697110341</td>\n",
       "      <td>gpt-35-turbo</td>\n",
       "      <td>solution_2</td>\n",
       "      <td>257</td>\n",
       "      <td>47</td>\n",
       "      <td>0.000360</td>\n",
       "      <td>0.000089</td>\n",
       "      <td>0.000449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1697110358</td>\n",
       "      <td>gpt-35-turbo</td>\n",
       "      <td>solution_2</td>\n",
       "      <td>380</td>\n",
       "      <td>48</td>\n",
       "      <td>0.000532</td>\n",
       "      <td>0.000091</td>\n",
       "      <td>0.000623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1697110398</td>\n",
       "      <td>gpt-35-turbo</td>\n",
       "      <td>solution_2</td>\n",
       "      <td>488</td>\n",
       "      <td>30</td>\n",
       "      <td>0.000683</td>\n",
       "      <td>0.000057</td>\n",
       "      <td>0.000740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1697110407</td>\n",
       "      <td>gpt-35-turbo</td>\n",
       "      <td>solution_2</td>\n",
       "      <td>592</td>\n",
       "      <td>47</td>\n",
       "      <td>0.000829</td>\n",
       "      <td>0.000089</td>\n",
       "      <td>0.000918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1697110414</td>\n",
       "      <td>gpt-35-turbo</td>\n",
       "      <td>solution_2</td>\n",
       "      <td>693</td>\n",
       "      <td>32</td>\n",
       "      <td>0.000970</td>\n",
       "      <td>0.000061</td>\n",
       "      <td>0.001031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1697110415</td>\n",
       "      <td>gpt-35-turbo</td>\n",
       "      <td>solution_2</td>\n",
       "      <td>160</td>\n",
       "      <td>28</td>\n",
       "      <td>0.000224</td>\n",
       "      <td>0.000053</td>\n",
       "      <td>0.000277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1697110430</td>\n",
       "      <td>gpt-35-turbo</td>\n",
       "      <td>solution_2</td>\n",
       "      <td>259</td>\n",
       "      <td>29</td>\n",
       "      <td>0.000363</td>\n",
       "      <td>0.000055</td>\n",
       "      <td>0.000418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1697110441</td>\n",
       "      <td>gpt-35-turbo</td>\n",
       "      <td>solution_2</td>\n",
       "      <td>358</td>\n",
       "      <td>38</td>\n",
       "      <td>0.000501</td>\n",
       "      <td>0.000072</td>\n",
       "      <td>0.000573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1697110449</td>\n",
       "      <td>gpt-35-turbo</td>\n",
       "      <td>solution_2</td>\n",
       "      <td>450</td>\n",
       "      <td>31</td>\n",
       "      <td>0.000630</td>\n",
       "      <td>0.000059</td>\n",
       "      <td>0.000689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1697110450</td>\n",
       "      <td>gpt-35-turbo</td>\n",
       "      <td>solution_2</td>\n",
       "      <td>160</td>\n",
       "      <td>28</td>\n",
       "      <td>0.000224</td>\n",
       "      <td>0.000053</td>\n",
       "      <td>0.000277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1697110834</td>\n",
       "      <td>gpt-4</td>\n",
       "      <td>solution_3</td>\n",
       "      <td>393</td>\n",
       "      <td>75</td>\n",
       "      <td>0.011004</td>\n",
       "      <td>0.004200</td>\n",
       "      <td>0.015204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1697110874</td>\n",
       "      <td>gpt-4</td>\n",
       "      <td>solution_3</td>\n",
       "      <td>962</td>\n",
       "      <td>83</td>\n",
       "      <td>0.026936</td>\n",
       "      <td>0.004648</td>\n",
       "      <td>0.031584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1697110906</td>\n",
       "      <td>gpt-4</td>\n",
       "      <td>solution_3</td>\n",
       "      <td>1023</td>\n",
       "      <td>106</td>\n",
       "      <td>0.028644</td>\n",
       "      <td>0.005936</td>\n",
       "      <td>0.034580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1697110926</td>\n",
       "      <td>gpt-4</td>\n",
       "      <td>solution_3</td>\n",
       "      <td>1018</td>\n",
       "      <td>87</td>\n",
       "      <td>0.028504</td>\n",
       "      <td>0.004872</td>\n",
       "      <td>0.033376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1697110963</td>\n",
       "      <td>gpt-4</td>\n",
       "      <td>solution_3</td>\n",
       "      <td>1745</td>\n",
       "      <td>145</td>\n",
       "      <td>0.048860</td>\n",
       "      <td>0.008120</td>\n",
       "      <td>0.056980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1697110988</td>\n",
       "      <td>gpt-4</td>\n",
       "      <td>solution_3</td>\n",
       "      <td>1880</td>\n",
       "      <td>152</td>\n",
       "      <td>0.052640</td>\n",
       "      <td>0.008512</td>\n",
       "      <td>0.061152</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "     timestamp         model    solution  prompt_tokens  completion_tokens  \\\n",
       "0   1697102416         gpt-4  solution_1            275                 32   \n",
       "1   1697109825         gpt-4  solution_1            275                 29   \n",
       "2   1697109853         gpt-4  solution_1            319                 41   \n",
       "3   1697109880         gpt-4  solution_1            375                 39   \n",
       "4   1697109891         gpt-4  solution_1            427                 48   \n",
       "5   1697109903         gpt-4  solution_1            484                 37   \n",
       "6   1697109950         gpt-4  solution_1            535                 36   \n",
       "7   1697109968         gpt-4  solution_1            590                 47   \n",
       "8   1697109978         gpt-4  solution_1            646                 34   \n",
       "9   1697110318  gpt-35-turbo  solution_2            160                 27   \n",
       "10  1697110341  gpt-35-turbo  solution_2            257                 47   \n",
       "11  1697110358  gpt-35-turbo  solution_2            380                 48   \n",
       "12  1697110398  gpt-35-turbo  solution_2            488                 30   \n",
       "13  1697110407  gpt-35-turbo  solution_2            592                 47   \n",
       "14  1697110414  gpt-35-turbo  solution_2            693                 32   \n",
       "15  1697110415  gpt-35-turbo  solution_2            160                 28   \n",
       "16  1697110430  gpt-35-turbo  solution_2            259                 29   \n",
       "17  1697110441  gpt-35-turbo  solution_2            358                 38   \n",
       "18  1697110449  gpt-35-turbo  solution_2            450                 31   \n",
       "19  1697110450  gpt-35-turbo  solution_2            160                 28   \n",
       "20  1697110834         gpt-4  solution_3            393                 75   \n",
       "21  1697110874         gpt-4  solution_3            962                 83   \n",
       "22  1697110906         gpt-4  solution_3           1023                106   \n",
       "23  1697110926         gpt-4  solution_3           1018                 87   \n",
       "24  1697110963         gpt-4  solution_3           1745                145   \n",
       "25  1697110988         gpt-4  solution_3           1880                152   \n",
       "\n",
       "    prompt_cost  completion_cost  total_cost  \n",
       "0      0.007700         0.001792    0.009492  \n",
       "1      0.007700         0.001624    0.009324  \n",
       "2      0.008932         0.002296    0.011228  \n",
       "3      0.010500         0.002184    0.012684  \n",
       "4      0.011956         0.002688    0.014644  \n",
       "5      0.013552         0.002072    0.015624  \n",
       "6      0.014980         0.002016    0.016996  \n",
       "7      0.016520         0.002632    0.019152  \n",
       "8      0.018088         0.001904    0.019992  \n",
       "9      0.000224         0.000051    0.000275  \n",
       "10     0.000360         0.000089    0.000449  \n",
       "11     0.000532         0.000091    0.000623  \n",
       "12     0.000683         0.000057    0.000740  \n",
       "13     0.000829         0.000089    0.000918  \n",
       "14     0.000970         0.000061    0.001031  \n",
       "15     0.000224         0.000053    0.000277  \n",
       "16     0.000363         0.000055    0.000418  \n",
       "17     0.000501         0.000072    0.000573  \n",
       "18     0.000630         0.000059    0.000689  \n",
       "19     0.000224         0.000053    0.000277  \n",
       "20     0.011004         0.004200    0.015204  \n",
       "21     0.026936         0.004648    0.031584  \n",
       "22     0.028644         0.005936    0.034580  \n",
       "23     0.028504         0.004872    0.033376  \n",
       "24     0.048860         0.008120    0.056980  \n",
       "25     0.052640         0.008512    0.061152  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
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    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Load monitoring from csv\n",
    "monitoring = pd.read_csv('monitoring.csv', header=None)\n",
    "\n",
    "# Add columns\n",
    "monitoring.columns = ['timestamp', 'model', 'solution', 'prompt_tokens', 'completion_tokens']\n",
    "prompt_pricing={'gpt-35-turbo':0.0000014, 'gpt-4':0.000028}\n",
    "completion_pricing={'gpt-35-turbo':0.0000019, 'gpt-4':0.000056}\n",
    "# Add cost column for each row\n",
    "monitoring['prompt_cost'] = monitoring['prompt_tokens'] * monitoring['model'].map(prompt_pricing)\n",
    "monitoring['completion_cost'] = monitoring['completion_tokens'] * monitoring['model'].map(completion_pricing)\n",
    "monitoring['total_cost'] = monitoring['prompt_cost'] + monitoring['completion_cost']\n",
    "\n",
    "#Display\n",
    "monitoring\n"
   ]
  },
  {
   "cell_type": "code",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>prompt_cost</th>\n",
       "      <th>completion_cost</th>\n",
       "      <th>total_cost</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>solution</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>solution_1</th>\n",
       "      <td>0.109928</td>\n",
       "      <td>0.019208</td>\n",
       "      <td>0.129136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>solution_2</th>\n",
       "      <td>0.005540</td>\n",
       "      <td>0.000732</td>\n",
       "      <td>0.006271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>solution_3</th>\n",
       "      <td>0.196588</td>\n",
       "      <td>0.036288</td>\n",
       "      <td>0.232876</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "            prompt_cost  completion_cost  total_cost\n",
       "solution                                            \n",
       "solution_1     0.109928         0.019208    0.129136\n",
       "solution_2     0.005540         0.000732    0.006271\n",
       "solution_3     0.196588         0.036288    0.232876"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cost per solution\n",
    "monitoring.groupby('solution').sum()[['prompt_cost', 'completion_cost', 'total_cost']]"
   ]
  }
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