---
id: polars-cloud
version: "0.10.0"
license: unclear
license_treatment: permissive
maintenance: active
---

# polars-cloud — Run Polars remotely, either in the Cloud or On-Prem

License: permissive · Maintenance: active · Downloads: 117.1K/mo

## What it is and what it does

Polars Cloud is a client library that bridges the open-source Polars DataFrame engine to a managed distributed compute service. It lets you write Polars queries using the familiar local API, then transparently execute them on remote infrastructure by calling `.remote()` on a query and providing a compute context. The library depends on polars and typing-extensions, and handles the serialization and orchestration needed to run your DataFrame operations at scale.

The package targets data engineers and analysts who want to scale beyond single-machine memory and CPU without rewriting their Polars code or learning a new API. It supports both CPU and GPU compute, works from notebooks, Airflow DAGs, Lambda functions, or any Python environment, and lets you monitor query progress through a dashboard. As of version 0.10.0, it is very early in its lifecycle (released 11 days ago), so production readiness and feature completeness should be validated before adoption.

Use it for:

- Scale Polars queries on large datasets stored in S3 or other cloud storage without rewriting code.
- Run compute-intensive aggregations and transformations from serverless environments like AWS Lambda.
- Execute parallel DataFrame operations from Airflow DAGs without managing Spark or Dask clusters.
- Prototype locally with Polars, then run the same query at scale on remote infrastructure.
- Combine multiple data sources and perform complex joins on datasets larger than local machine memory.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Polars Cloud extends the Polars DataFrame library to run queries on distributed remote infrastructure, allowing you to scale computations beyond a single machine while using the same Polars API.

Yes, with conditions. The package is permissively licensed and has no known vulnerabilities, making it safe to try. However, it is extremely new (0.10.0, released 11 days ago) with a 'Planning' development status, so treat it as early-stage software. Install if you want to experiment with distributed Polars execution and are willing to accept potential API changes; avoid if you need production-grade stability or have strict SLAs. Verify pricing, feature coverage, and performance characteristics before committing to it for critical workloads.

## Install

pip install polars-cloud
uv add polars-cloud
poetry add polars-cloud

## Installing polars-cloud

Before you install: Medium install friction: compiled wheels available for Python 3.10+ across macOS, Linux, and Windows architectures. Very recent release (11 days old) with active maintenance status, so long-term stability and community patterns remain unproven.

License in practice: MIT license (permissive) imposes no restrictions on use, modification, or distribution in commercial or private projects.

Quickstart:

pip install polars_cloud

import polars as pl
import polars_cloud as pc

ctx = pc.ComputeContext(cpus=16, memory=64)
query = pl.scan_parquet("s3://data/").group_by("col").agg(pl.mean("val"))
query.remote(ctx).distributed().sink_parquet("s3://out/")

Requires Python 3.10 or later; remote execution requires a Polars Cloud account and compute context configuration.

Verify before relying:

- Actual performance and cost characteristics of remote execution compared to local Polars or other distributed engines.
- Whether the distributed engine supports all Polars operations or has limitations on certain query patterns.
- Pricing model and free tier details for Polars Cloud compute resources.
- Integration maturity with common data sources and cloud storage systems beyond S3.

## Package facts

- License: not declared (permissive)
- Python support: supports_current
- Install friction: medium
- Maintenance: active
- Downloads: 117.1K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

distributed polars queries, polars remote execution, scale polars dataframes, cloud dataframe processing, polars serverless compute, distributed sql engine, polars query scaling, distributed-computing, dataframe-scaling, cloud-native

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