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This page covers credit consumption for the ADE v1 APIs. For plans and billing, see Plans & Billing.

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Credit Costs

The credit consumption is determined by the specific API endpoint called.

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Credit Costs for the Parse APIs

This section explains credit use for these APIs:

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Documents

This pricing applies when using the parsing model. For pricing, see DPT-2 mini. The number of credits used to parse a document is based on the number of pages and the features used in parsing, as shown in the following table.
FeatureCredit CostNotes
Parsing3 credits/pageEach page processed includes parsing.
ZDR (HIPAA/BAA)+1 credit/pageAdditional charge when enabled.
Available on Team and Enterprise plans.

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Spreadsheets

This pricing applies when using the parsing model. For pricing, see DPT-2 mini. The number of credits used to parse a spreadsheet is based on the number of sheets and embedded images. For supported spreadsheet formats, see Supported File Types.
FeatureCredit CostNotes
Parsing1 credit/sheetEach sheet processed includes parsing.
Embedded images3 credits/imageCost per embedded image, chart, or logo.
ZDR (HIPAA/BAA)+1 credit/sheet
+1 credit/image		Additional charge when enabled.
Available on Team and Enterprise plans.
Example: A spreadsheet with 1 sheet and 2 embedded images:
  • Base cost: 1 credit (sheet) + 6 credits (2 images) = 7 credits
  • With ZDR enabled: 7 credits (base) + 1 credit (sheet) + 2 credits (2 images) = 10 credits

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DPT-2 mini

is a lightweight model that consumes fewer credits than other parsing models. The number of credits used to parse a document is based on the number of pages and the features used in parsing, and is rounded up to the nearest tenth decimal place.
FeatureCredit CostNotes
Parsing1.5 credits/pageEach page processed includes parsing.
ZDR (HIPAA/BAA)+1 credit/pageAdditional charge when enabled.
Available on Team and Enterprise plans.

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Credit Costs for the Extract API

This section explains credit use for these APIs: The number of credits used to extract data is based on both the number of input characters and output characters, and is rounded up to the nearest tenth decimal place. The same credit costs apply whether you extract data synchronously with the API or asynchronously with the ADE Extract Jobs API.
FactorCredit CostNotes
5,000 input characters1 creditInput characters are the number of characters in the Markdown file that is passed to the API.
1,000 output characters1 creditOutput characters are the number of characters in the extraction object returned by the API, excluding whitespace used for indentation. (Other spaces, like the spaces after colons in key-value pairs, are not removed.)
Formula Here is the formula used to calculate the credit cost: 
credits = (input characters ÷ 5,000) + (output characters ÷ 1,000)
Result is rounded up to the nearest tenth decimal place.

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Sample Cost for the Extract API

Input Characters Let’s say you run the API on a Markdown file with the following content. The file has 2,270 characters. Therefore, the number of input characters is 2,270. 
<a id='2800ae73-bd32-4c54-910d-755f25541b15'></a>\n\nAROMA CAFE\n1211 Green Street\nNew York, NY 10005\n\n<a id='b7c067c1-4c96-4f07-8a2e-f3557aeb9b95'></a>\n\n12/27/2019\n\n<a id='9d2262d5-9319-41f1-9c69-e7ba0b726f9a'></a>\n\n08:26 PM\n\n<a id='8dfee4e4-c1a1-4fc8-9525-8c0352697e90'></a>\n\nTAB 8          HOST MAGGIE\n\n<a id='25f864af-7281-4c83-95bd-2e499dd8ee0d'></a>\n\nAMEX ##########19883\n\n<a id='4486463b-f5e3-487b-b9df-4959587c5091'></a>\n\n<table>\n  <tr>\n    <th>QTY</th>\n    <th>DESC</th>\n    <th>AMT</th>\n  </tr>\n  <tr>\n    <td>1</td>\n    <td>Americano</td>\n    <td>$3.19</td>\n  </tr>\n  <tr>\n    <td>1</td>\n    <td>Almond Scone</td>\n    <td>$1.99</td>\n  </tr>\n  <tr>\n    <td>1</td>\n    <td>16oz Bottle Water</td>\n    <td>$2.99</td>\n  </tr>\n</table>\n\n<a id='91b1d921-163e-406b-bf7f-a1731f706e1c'></a>\n\nAMT: $8.70\n\nSUBTOTAL: $8.17\nTAX: $0.53\n\nBALANCE: $8.70\n\n<a id='7bcc9a3e-c688-4ff2-a5b0-bfea57d25f3c'></a>\n\nSummary : This image displays a barcode consisting of a series of vertical black and white lines, used for encoding information in a machine-readable format.\n\nbarcode:\n  Barcode Details :\n    • The barcode is composed of alternating black and white vertical bars of varying thickness.\n    • No human-readable numeric or alphanumeric string is visible below or above the barcode.\n    • Barcode type is not explicitly indicated; the pattern resembles a standard linear barcode (e.g., Code 128 or EAN-13), but cannot be definitively identified without further context.\n    • The barcode contains approximately 60–70 modules (individual bars and spaces).\n    • Quiet zones (blank margins) are present on both the left and right sides of the barcode.\n    • The barcode is horizontally oriented and centered on a white background.\n    • No additional text, logo, or annotation is present.\n\nAnalysis :\n  • The barcode is designed for machine scanning and does not convey any human-readable information in this image.\n  • The absence of a numeric string or label means the encoded data cannot be determined visually.\n  • The presence of quiet zones and consistent bar height suggests it is formatted for standard retail or inventory use.\n\n<a id='d06c76e1-fb44-4efb-89b7-f7eedd976062'></a>\n\nSAMPLE RECEIPT
Output Characters Let’s say you extract multiple fields from the Markdown file, and the following is the content in the extraction object returned by the API. The response has 475 characters. Therefore, the number of output characters is 475. 
{'merchant': {'name': 'AROMA CAFE', 'address': '1211 Green Street', 'city_state_zip': 'New York, NY 10005'}, 'transaction': {'date': '2019-12-27', 'time': '20:26', 'payment_method': 'AMEX ##########19883', 'tab_host': 'TAB 8 HOST MAGGIE'}, 'items': [{'qty': '1', 'desc': 'Americano', 'amt': '3.19'}, {'qty': '1', 'desc': 'Almond Scone', 'amt': '1.99'}, {'qty': '1', 'desc': '16oz Bottle Water', 'amt': '2.99'}], 'totals': {'subtotal': '8.17', 'tax': '0.53', 'total': '8.70'}}
Calculate the Total Now that you have your input and output character counts, you can calculate the credit cost:
StepCalculationResult
Input credits2,270 ÷ 5,0000.454
Output credits475 ÷ 1,0000.475
Total (before rounding)0.454 + 0.4750.929
Final costRound up to nearest tenth decimal place1.0 credit

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Credit Costs for the Build Extract Schema API

This section explains credit use for this API: The number of credits used to build an extraction schema is based on both the number of input characters and output characters, and is rounded up to the nearest tenth decimal place.
FactorCredit CostNotes
5,000 input characters1 creditInput characters are the combined total number of characters in the markdowns, markdown_urls, prompt, and schema parameters.
1,000 output characters1 creditOutput characters are the number of characters in the schema returned by the API.
Formula Here is the formula used to calculate the credit cost: 
credits = (input characters ÷ 5,000) + (output characters ÷ 1,000)
Result is rounded up to the nearest tenth decimal place.

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Sample Cost for the Build Extract Schema API

Let’s say you have an existing schema built from two bank statements. You want to add and remove some fields in the schema, so you call the API to refine the schema. Input Characters The API includes the following parameters.
ParameterContentCharacters
markdownsThe parsed output of the first bank statement9,116
markdownsThe parsed output of the second bank statement7,314
schemaThe existing extraction schema that you want to refine9,089
promptInstructions to add and remove specific fields214
Total25,733
Output Characters Running the API call generates the following updated schema.
ContentCharacters
Updated schema8,585
Calculate the Total Now that you have your input and output character counts, you can calculate the credit cost.
StepCalculationResult
Input credits25,733 ÷ 5,0005.1466
Output credits8,585 ÷ 1,0008.585
Total (before rounding)5.1466 + 8.58513.7316
Final costRound up to nearest tenth decimal place13.8 credits

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Credit Costs for the Classify API

This section explains credit use for this API: The number of credits used to classify a document is based on the number of pages, and is rounded up to the nearest tenth decimal place.
FactorCredit CostNotes
Per page0.5 credits/pageEach page processed by the Classify API consumes 0.5 credits.
Formula Here is the formula used to calculate the credit cost: 
credits = number of pages × 0.5
Result is rounded up to the nearest tenth decimal place.
Example: A 10-page document costs 10 × 0.5 = 5.0 credits.

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Credit Costs for the Section API

This section explains credit use for this API: The number of credits used to section a document is based on both the number of input characters and output characters, and is rounded up to the nearest tenth decimal place.
FactorCredit CostNotes
5,000 input characters1 creditInput characters are the number of characters in the Markdown file passed to the API.
1,000 output characters1 creditOutput characters are the number of characters in the table_of_contents field returned by the API.
Formula Here is the formula used to calculate the credit cost: 
credits = (input characters ÷ 5,000) + (output characters ÷ 1,000)
Result is rounded up to the nearest tenth decimal place.

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Credit Costs for the Split API

This section explains credit use for this API: The number of credits used to split documents is based on the number of input characters, and is rounded up to the nearest tenth decimal place.
FactorCredit CostNotes
5,000 input characters1 creditInput characters are the number of characters in the Markdown file that is passed to the API.
Formula Here is the formula used to calculate the credit cost: 
credits = (input characters ÷ 5,000) 
Result is rounded up to the nearest tenth decimal place.