Paddle Python API Docs | dltHub

Paddle is a merchant‑of‑record billing platform and REST API for managing products, customers, transactions, subscriptions, reports and related billing entities. The REST API base URL is `` and All requests require an API key (HTTP header) with scoped permissions.

dlt is an open-source Python library that handles authentication, pagination, and schema evolution automatically. dlthub provides AI context files that enable code assistants to generate production-ready pipelines. Install with uv pip install "dlt[workspace]" and start loading Paddle data in under 10 minutes.

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What data can I load from Paddle?

Here are some of the endpoints you can load from Paddle:

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How do I authenticate with the Paddle API?

Paddle uses per‑account API keys. Provide the API key in the Authorization header as a Bearer token or via the developer dashboard‑created API key mechanism; the API version is set via a request header. Requests must be over HTTPS.

1. Get your credentials

1. Log into the Paddle dashboard. 2) Navigate to Developer Tools → Authentication (or Manage API keys). 3) Create a new API key (choose sandbox or live and set scopes/permissions). 4) Copy the API key value and store it securely; use it in requests or in your dlt secrets.toml.

2. Add them to .dlt/secrets.toml

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[sources.paddle_payments_source]
api_key = "your_paddle_api_key_here"

dlt reads this automatically at runtime — never hardcode tokens in your pipeline script. For production environments, see setting up credentials with dlt for environment variable and vault-based options.

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How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

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uv venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI Workbench:

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dlt ai init --agent <your-agent> # <agent>: claude | cursor | codex

This installs project rules, a secrets management skill, appropriate ignore files, and configures the dlt MCP server for your agent. Learn more →

2. Install the rest-api-pipeline toolkit:

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dlt ai toolkit rest-api-pipeline install

This loads the skills and context about dlt the agent uses to build the pipeline iteratively, efficiently, and safely. The agent uses MCP tools to inspect credentials — it never needs to read your secrets.toml directly. Learn more →

3. Start LLM-assisted coding:

Use /find-source to load data from the Paddle API into DuckDB.

The rest-api-pipeline toolkit takes over from here — it reads relevant API documentation, presents you with options for which endpoints to load, and follows a structured workflow to scaffold, debug, and validate the pipeline step by step.

4. Run the pipeline:

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python paddle_payments_pipeline.py

If everything is configured correctly, you'll see output like this:

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Pipeline paddle_payments_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset paddle_payments_data
The duckdb destination used duckdb:/paddle_payments.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:

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dlt pipeline paddle_payments_pipeline show

This opens the Pipeline Dashboard where you can verify pipeline state, load metrics, schema (tables, columns, types), and query the loaded data directly.

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Python pipeline example

This example loads products and customers from the Paddle API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

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import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def paddle_payments_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "products", "endpoint": {"path": "products", "data_selector": "data"}},
            {"name": "customers", "endpoint": {"path": "customers", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="paddle_payments_pipeline",
        destination="duckdb",
        dataset_name="paddle_payments_data",
    )
    load_info = pipeline.run(paddle_payments_source())
    print(load_info)

To add more endpoints, append entries from the resource table to the "resources" list using the same name, path, and data_selector pattern.

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How do I query the loaded data?

Once the pipeline runs, dlt creates one table per resource. You can query with Python or SQL.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("paddle_payments_pipeline").dataset()
sessions_df = data.products.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM paddle_payments_data.products LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("paddle_payments_pipeline").dataset()
data.products.df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

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What destinations can I load Paddle data to?

dlt supports loading into any of these destinations — only the destination parameter changes:

Change the destination in dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. See the full destinations list.

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Troubleshooting

Authentication failures

If you receive 401 or permission‑related 403 errors, verify the API key used (live vs sandbox) and that it has the required scopes in the Paddle dashboard. Include the request_id from the response when contacting Paddle support.

Rate limits and quota

Paddle enforces rate limits. If you receive 429 Too Many Requests, implement exponential backoff and retry using the pagination cursors (meta.pagination.next / after parameter) rather than increasing per_page beyond documented limits.

Pagination and cursors

List endpoints are paginated. Responses include meta.pagination with keys such as next and per_page. Use the meta.pagination.next URL or the after parameter to page through results. Default and maximum per_page values are documented per endpoint (commonly default 50, max 200 for some endpoints, transactions default 30 max 30).

Ensure that the API key is valid to avoid 401 Unauthorized errors. Also, verify endpoint paths and parameters to avoid 404 Not Found errors.

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Next steps

Continue your data engineering journey with the other toolkits of the dltHub AI Workbench:

• data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
• dlthub-runtime — Deploy, schedule, and monitor your pipeline in production.

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dlt ai toolkit data-exploration install
dlt ai toolkit dlthub-runtime install

• How to deploy a pipeline
• How to explore your data in marimo Notebooks
• How to query your data in Python with dataset
• Join the dlt community on Slack