Which Anthropic API key does the connector need?

An organization **Admin API key** that starts with sk-ant-admin. You can create one in the Anthropic Console under **Settings** > **Admin keys**. Regular API keys are rejected by the Admin API reporting endpoints, so they will not work.

Why do cost amounts look about a hundred times larger than my bill?

The Anthropic API returns cost amounts in USD **cents**: 17.0289 in the Amount column means $0.170289. Divide by 100 to get dollars. The value arrives as a decimal string and is stored as a text column, so cast it to a numeric type in your queries.

Do I need a separate connection for each workspace?

No. The Admin API key is organization-scoped, so one connection covers every workspace. Use the Workspace ID column to separate them in the destination table; the default workspace appears as null.

Anthropic

The Anthropic Admin API describes Claude API consumption at the organization level in two reports. The usage report counts tokens split by prompt cache behavior: uncached input, 5-minute and 1-hour cache writes, and cache reads. The cost report tracks daily spend per line item in USD cents.

Renta syncs both to your data warehouse as time-bucketed statistics broken down by workspace, API key, model, service tier, and context window.

Authentication

Renta authenticates with an organization Admin API key. Create one in the Anthropic Console under Settings > Admin keys: the key starts with sk-ant-admin. Regular API keys are rejected by the Admin API reporting endpoints.

When you save the connection, Renta validates the key against the Anthropic Admin API, so an invalid or non-admin key returns an error immediately instead of failing on the first pipeline run.

Connect Anthropic source

Follow these steps to add Anthropic as a source in Renta using an Admin API key.

Go to the CatalogStep 1

Navigate to the Connections > Catalog section in the left sidebar.
Open the AI category and click the Anthropic card.

Anthropic card in the AI category of the Renta catalog

Enter the Admin API key and saveStep 2

Fill in the connection form:

Source name.
Specify a name for this data source. This is used exclusively in the Renta interface.
Admin API key.
Paste your Anthropic Admin API key (starts with sk-ant-admin).

Click Save to finish.

Add a new Anthropic connection page with source name and Admin API key fields

Source createdStep 3

The new source appears in the Sources list with the Active status. You can now use it in pipelines.

Sources list with the new Anthropic source

Create a pipeline

Once the source is connected, create a pipeline to sync your Anthropic data into your destination.

Create an Anthropic pipeline

Link your Anthropic source to a destination and configure the pipeline.

Select Anthropic as the sourceStep 1

In the left sidebar, click New pipeline. On the Source step, select Anthropic under Already connected and click Next step.

Select a source step with Anthropic under Already connected

Select your Anthropic connectionStep 2

Choose the Anthropic connection you created earlier and click Next step.

Select a destinationStep 3

On the Destination step, select your destination (for example, Google BigQuery) and click Next step.

Select a destination step with Google BigQuery

Select the destination connectionStep 4

Choose the destination connection you want to load data into and click Next step.

Select your Google BigQuery connection step

Pipeline configuration

On the final step, choose which Anthropic report to export and how Renta loads it.

Report type and granularity

Pick the report and the size of its time buckets.

Report Types Choose one of the two reports. Each pipeline exports one report into one destination table.

Report type	Data
Usage Report	Token counts for Claude API requests: uncached input tokens, prompt cache writes (5-minute and 1-hour), cache reads, output tokens, and server-side web search request counts.
Cost Report	Daily costs in USD cents per line item, attributed to workspace, model, token type, and cost type.

Granularity Choose the time bucket size: Minute, Hour, or Day. Each row in the destination table is one time bucket for one combination of breakdown values. For Cost Report the only option is Day: the Anthropic cost endpoint supports daily buckets only.

A day contains 1,440 minute buckets, so Minute granularity over a long date range produces a large table and a noticeably longer initial load. For historical analysis, Day is usually enough.

Parameters

Select the fields to include in the destination table. The available set depends on the report type: each report combines breakdown columns, its own metrics, and time bucket fields.

Check the fields in the picker or click Select All. The Selected tab shows everything you picked, and the Selected Parameters list below lets you remove a field. In the destination table, column names use the API names shown under each field: Workspace ID becomes workspace_id.

Report type	Breakdowns	Metrics
Usage Report	API Key ID, Workspace ID, Account ID, Service Account ID, Model, Service Tier, Context Window, Inference Geo	Uncached Input Tokens, Cache Creation Ephemeral 5m Input Tokens, Cache Creation Ephemeral 1h Input Tokens, Cache Read Input Tokens, Output Tokens, Web Search Requests
Cost Report	Workspace ID, Description, Cost Type, Model, Token Type, Context Window, Service Tier, Inference Geo	Amount, Currency

Every report also includes three time fields: Bucket Starting At and Bucket Ending At (bucket boundaries in UTC) and Bucket Starting Date, the day-level date column the destination table is partitioned by. Bucket Starting Date is pre-selected and cannot be removed.

Amount is in USD cents, not dollars: 17.0289 means $0.170289. The value arrives as a decimal string and is stored as a text column, so cast it to a numeric type and divide by 100 in your queries.

Breakdown columns can be null: requests made from the Workbench carry no API key, and the default workspace has no workspace ID. The totals in such rows are still correct.

Table name and date range

Name the pipeline and set the historical period to load.

Anthropic integration name and table name fields

Integration name.
The name of the pipeline. It is used exclusively in the Renta interface.
Table name.
The destination table name. You can keep the auto-generated name or enter your own.

Anthropic date range with date start and date end fields

Date start.
The first date of the extraction period.
Date end.
The last date of the extraction period. Leave it as Today to keep collecting new data on every run.

Overwrite period

The overwrite period defines how many recent days Renta reloads on every run.

On each run, Renta re-fetches the report starting from the date of the last successful run minus the overwrite period. The affected days are deleted from the destination table and inserted again, while older data stays untouched. The default is Last 1 day.

Schedule

Choose how often the pipeline runs.

Schedule type	Description
Manual	The pipeline runs only when triggered manually through the interface or via API.
Daily	The pipeline runs once a day at the specified update time.
Interval	The pipeline runs at specified time intervals.
Cron Expression	The pipeline runs on a schedule defined by a cron expression.

For Daily schedules, set the Update time, the exact time each run starts, then click Create pipeline.

Update time selection and the Create pipeline button

Renta also supports API triggers and integration with external orchestrators such as Apache Airflow, Dagster, and Prefect. See the full scheduling documentation for details.

The new pipeline appears in the Pipelines section under the ETL tab, where you can track its runs, progress, and the destination table name.

Pipelines list with the new Anthropic integration

Sync strategy

Both Anthropic reports are time-bucketed statistics, so they share one synchronization strategy.

Strategy	Supported report types	Description
Incremental	Usage Report, Cost Report	Each destination table is partitioned by the `bucket_starting_date` column. On every run Renta reloads the window from the last successful run minus the overwrite period to the end of the date range: those daily partitions are deleted and inserted again, and older partitions stay untouched.

Changing the selected parameters updates the table schema, so the next run after the change reloads the entire date range.

Frequently asked questions

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