Claude (Anthropic) provides usage reporting and cost breakdowns for your organizations and API keys. This connector extracts those reports from the Anthropic Admin API and lands them in your Nekt Catalog as queryable tables.

This connector is not available for individual Claude accounts, only organization ones.

Configuring Claude as a Source

In the Sources tab, click on the “Add source” button located on the top right of your screen. Then, select the Claude option from the list of connectors. Click Next and you’ll be prompted to add your access.

1. Add account access

You’ll need an Anthropic Admin API key with access to usage and cost reporting for your organization. Once you have it, add the account access, then click Next. The following configurations are available:

Admin API Key: Anthropic Admin API key (starts with sk-ant-admin...).
Start Date: Select the earliest date you want to extract data from.

Make sure the API key you use has the required Admin permissions for usage and cost reporting. Without it, the connector cannot extract data from the Anthropic Admin API.

2. Select streams

Choose which data streams you want to sync:

Messages Usage: Token usage report (grouped by model, workspace, API key, service tier, and context window).
Cost Report: Cost breakdown report (grouped by workspace and cost-item description).

Select the streams and click Next.

3. Configure data streams

Customize how you want your data to appear in your catalog. Select the desired layer where the data will be placed, a folder to organize it inside the layer, a name for each table and the type of sync.

Layer: choose between the existing layers on your catalog.
Folder: a folder can be created inside the selected layer to group all tables being created from this new data source.
Table name: we suggest a name, but feel free to customize it. You can add a prefix to all tables at once.
Sync Type: you can choose between INCREMENTAL and FULL_TABLE.
- Incremental: only new/updated buckets since the last successful run are extracted.
- Full table: every run fetches the full current state of the selected stream.

Once you are done configuring, click Next.

4. Configure data source

Describe your data source for easy identification within your organization, not exceeding 140 characters. To define your Trigger, consider how often you want data to be extracted from this source. This decision usually depends on how frequently you need the new table data updated (every day, once a week, or only at specific times). Optionally, you can define some additional settings:

Configure Delta Log Retention and determine for how long we should store old states of this table as it gets updated.
Determine when to execute an Additional Full Sync.

Once you are ready, click Next to finalize the setup.

5. Check your new source

You can view your new source on the Sources page. If needed, manually trigger the source extraction by clicking on the arrow button. Once executed, your data will appear in your Catalog.

For you to be able to see it on your Catalog, you need at least one successful source run.

Streams and Fields

Below you’ll find all available data streams from Claude and their corresponding fields:

Messages Usage

Token usage report grouped by model, workspace, API key, service tier, and context window.

Field	Type	Description
`starting_at`	DateTime	Start of the time bucket.
`ending_at`	DateTime	End of the time bucket.
`model`	String	Model used.
`workspace_id`	String	Workspace ID.
`api_key_id`	String	API key ID.
`service_tier`	String	Service tier (for example, standard, batch, priority).
`context_window`	String	Context window bucket (for example, `0-200k` or `200k-1M`).
`uncached_input_tokens`	Integer	Uncached input tokens.
`cache_read_input_tokens`	Integer	Tokens read from cache.
`cache_creation_ephemeral_5m_input_tokens`	Integer	Tokens for 5-minute cache creation.
`cache_creation_ephemeral_1h_input_tokens`	Integer	Tokens for 1-hour cache creation.
`output_tokens`	Integer	Output tokens generated.
`web_search_requests`	Integer	Number of web search requests.

Cost Report

Cost breakdown grouped by workspace and cost-item description (which includes model and token type).

Field	Type	Description
`starting_at`	DateTime	Start of the time bucket.
`ending_at`	DateTime	End of the time bucket.
`workspace_id`	String	Workspace ID.
`description`	String	Cost item description.
`amount`	String	Cost amount in cents (USD).
`currency`	String	Currency code (USD).
`cost_type`	String	Cost type (tokens, web_search, or code_execution).
`model`	String	Model name.
`token_type`	String	Token type (uncached_input_tokens, output_tokens, etc.).
`service_tier`	String	Service tier (standard or batch).
`context_window`	String	Context window bucket (for example, `0-200k` or `200k-1M`).
`inference_geo`	String	Inference geography.

The connector uses daily time buckets (bucket_width = 1d) and uses starting_at as the replication key for incremental sync.

Use Cases for Data Analysis

This guide outlines a couple of common business intelligence use cases when consolidating Claude usage and cost data in your Catalog.

1. Token usage by model

Analyze how input/output tokens and web search activity vary by model over time.

SQL query

SELECT
  model,
  context_window,
  SUM(uncached_input_tokens) AS uncached_input_tokens,
  SUM(cache_read_input_tokens) AS cache_read_input_tokens,
  SUM(cache_creation_ephemeral_5m_input_tokens) AS cache_creation_ephemeral_5m_input_tokens,
  SUM(cache_creation_ephemeral_1h_input_tokens) AS cache_creation_ephemeral_1h_input_tokens,
  SUM(output_tokens) AS output_tokens,
  SUM(web_search_requests) AS web_search_requests
FROM
  nekt_raw.claude_messages_usage
WHERE
  starting_at >= current_timestamp - interval '30' day
GROUP BY
  model,
  context_window
ORDER BY
  output_tokens DESC

SELECT
  model,
  context_window,
  SUM(uncached_input_tokens) AS uncached_input_tokens,
  SUM(cache_read_input_tokens) AS cache_read_input_tokens,
  SUM(cache_creation_ephemeral_5m_input_tokens) AS cache_creation_ephemeral_5m_input_tokens,
  SUM(cache_creation_ephemeral_1h_input_tokens) AS cache_creation_ephemeral_1h_input_tokens,
  SUM(output_tokens) AS output_tokens,
  SUM(web_search_requests) AS web_search_requests
FROM
  `nekt_raw.claude_messages_usage`
WHERE
  starting_at >= TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 30 DAY)
GROUP BY
  model,
  context_window
ORDER BY
  output_tokens DESC

Sample Result

model	context_window	uncached_input_tokens	cache_read_input_tokens	output_tokens	web_search_requests
claude-3-5-sonnet	0-200k	12,450,000	3,210,000	8,930,000	42,810
claude-3-opus	200k-1M	7,110,000	1,905,000	4,620,000	18,120

2. Cost breakdown by token type

Understand which token categories dominate spend.

SQL query

SELECT
  cost_type,
  model,
  token_type,
  SUM(CAST(amount AS DOUBLE)) / 100.0 AS cost_usd
FROM
  nekt_raw.claude_cost_report
WHERE
  starting_at >= current_timestamp - interval '30' day
  AND cost_type = 'tokens'
GROUP BY
  cost_type,
  model,
  token_type
ORDER BY
  cost_usd DESC

SELECT
  cost_type,
  model,
  token_type,
  SUM(CAST(amount AS FLOAT64)) / 100.0 AS cost_usd
FROM
  `nekt_raw.claude_cost_report`
WHERE
  starting_at >= TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 30 DAY)
  AND cost_type = 'tokens'
GROUP BY
  cost_type,
  model,
  token_type
ORDER BY
  cost_usd DESC

Sample Result

cost_type	model	token_type	cost_usd
tokens	claude-3-5-sonnet	output_tokens	124,531.20
tokens	claude-3-5-sonnet	uncached_input_tokens	88,102.77
tokens	claude-3-opus	output_tokens	41,009.65

Implementation Notes

Authentication

The connector authenticates against the Anthropic Admin API using admin_api_key as the x-api-key request header.

Pagination and daily buckets

Reports are fetched using daily buckets (bucket_width = 1d) and paginated through the Admin API using a page cursor when the response indicates more results.

Rate limiting and retries

Anthropic Admin API calls are rate-limited conservatively and retried using exponential backoff. This helps prevent transient failures and HTTP 429 responses during large backfills.

Interpreting `amount` in Cost Report

cost_report.amount is returned as a string representing cents (USD). Divide by 100 to get the value in USD.

Skills for agents

Download Claude skills file

Claude connector documentation as plain markdown, for use in AI agent contexts.

Documentation

Get started

Using Nekt

Workspace

Resources

Claude as a data source

Configuring Claude as a Source

1. Add account access

2. Select streams

3. Configure data streams

4. Configure data source

5. Check your new source

Streams and Fields

Use Cases for Data Analysis

1. Token usage by model

2. Cost breakdown by token type

Implementation Notes

Skills for agents

Download Claude skills file

Documentation

Get started

Using Nekt

Workspace

Resources

​Configuring Claude as a Source

​1. Add account access

​2. Select streams

​3. Configure data streams

​4. Configure data source

​5. Check your new source

​Streams and Fields

​Use Cases for Data Analysis

​1. Token usage by model

​2. Cost breakdown by token type

​Implementation Notes

​Skills for agents

Download Claude skills file

Configuring Claude as a Source

1. Add account access

2. Select streams

3. Configure data streams

4. Configure data source

5. Check your new source

Streams and Fields

Use Cases for Data Analysis

1. Token usage by model

2. Cost breakdown by token type

Implementation Notes

Skills for agents