Spot by Flexera Python API Docs | dltHub

Spot by Flexera is a cloud optimization platform that provides REST APIs to manage Spotinst resources. The REST API base URL is https://api.spotinst.io and All requests require a Bearer token for authentication.

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 Spot by Flexera data in under 10 minutes.

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What data can I load from Spot by Flexera?

Here are some of the endpoints you can load from Spot by Flexera:

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How do I authenticate with the Spot by Flexera API?

The API uses Bearer Token authentication. Include the header Authorization: Bearer <token> in every request.

1. Get your credentials

1. Log in to the Spot dashboard.
2. Navigate to Connect Settings → API Keys.
3. Click Create API Key (or Create Programmatic User).
4. Give the key a name and optional permissions, then Save.
5. Copy the generated token; it will be shown only once.
6. Store the token securely for use in API calls.

2. Add them to .dlt/secrets.toml

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[sources.spot_by_flexera_source]
token = "your_spot_token_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 Spot by Flexera 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 spot_by_flexera_pipeline.py

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

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Pipeline spot_by_flexera_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset spot_by_flexera_data
The duckdb destination used duckdb:/spot_by_flexera.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 spot_by_flexera_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 account and apikeys from the Spot by Flexera 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 spot_by_flexera_source(token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.spotinst.io",
            "auth": {
                "type": "bearer",
                "token": token,
            },
        },
        "resources": [
            {"name": "account", "endpoint": {"path": "setup/account"}},
            {"name": "apikeys", "endpoint": {"path": "connect/settings/apikeys", "data_selector": "items"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="spot_by_flexera_pipeline",
        destination="duckdb",
        dataset_name="spot_by_flexera_data",
    )
    load_info = pipeline.run(spot_by_flexera_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("spot_by_flexera_pipeline").dataset()
sessions_df = data.apikeys.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM spot_by_flexera_data.apikeys LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("spot_by_flexera_pipeline").dataset()
data.apikeys.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 Spot by Flexera 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 errors

• 401 Unauthorized – occurs when the Bearer token is missing, invalid, or expired. Ensure the Authorization: Bearer <token> header is present and the token has not been revoked.

Rate limiting

• Spot APIs enforce request limits per account. A 429 Too Many Requests response indicates the limit has been exceeded. The response body contains an error object with code and message. Implement exponential backoff and respect the Retry-After header if provided.

Pagination quirks

• Many list endpoints return a paginated payload with nextKey / previousKey or a paginationInfo object. Use the nextKey value in the subsequent request's query parameter to retrieve the next page. Some endpoints use limit and offset instead.

Generic error format

• Errors are returned as JSON with an error field, e.g.:

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"error" : {
  "code" : "string",
  "message" : "string"
}

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