polars-cloud
Run Polars remotely, either in the Cloud or On-Prem
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later; remote execution requires a Polars Cloud account and compute context configuration.
- 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 · maintenance · safety
permissive license (permissive) — MIT license (permissive) imposes no restrictions on use, modification, or distribution in commercial or private projects.
last release 2026-08-03 (11 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 117,069 downloads/mo, #12,183 on PyPI
Alternatives
Verify before relying
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/")- 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.
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 on this page. Verify before relying on it.
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
polars-cloud on PyPI
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.
Requires Python 3.10 or later; remote execution requires a Polars Cloud account and compute context configuration.
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/")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 | permissive license permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 2 packagespolarstyping-extensions |
| Maintenance | Actively maintained 11 days since the last release |
| First released | |
| Downloads | 117,069 / month, #12,183 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 1 - PlanningEnvironment :: ConsoleIntended Audience :: Science/ResearchLicense :: OSI Approved :: MIT LicenseOperating System :: OS IndependentProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: Scientific/Engineering |
Evidence: polars_cloud-0.10.0-cp310-abi3-macosx_10_12_x86_64.whl; polars_cloud-0.10.0-cp310-abi3-macosx_11_0_arm64.whl; polars_cloud-0.10.0-cp310-abi3-manylinux_2_28_aarch64.whl; polars_cloud-0.10.0-cp310-abi3-manylinux_2_28_x86_64.whl; polars_cloud-0.10.0-cp310-abi3-win_amd64.whl; polars_cloud-0.10.0-cp310-abi3-win_arm64.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “distributed polars queries”
- polars-cloudPolars Cloud extends the Polars DataFrame library to run queries on…
- polars-runtime-compatPolars-runtime-compat provides binary wheels for Polars, a Rust-based…
- polarsPolars is a DataFrame query engine written in Rust that executes…
Give your agent the search over MCP, or paste the wish link into any chat.
More Scientific/Engineering packages
NumPy provides an N-dimensional array object and a comprehensive suite of mathematical, linear algebra, Fourier transform, and random number functions for scientific computing in Python.
pandas provides fast, flexible data structures (Series and DataFrame) for loading, cleaning, transforming, and analyzing labeled or relational data in Python.
scipy provides numerical algorithms for mathematics, science, and engineering—including optimization, integration, linear algebra, Fourier transforms, signal and image processing, and ODE solvers—built on numpy arrays.
scikit-learn provides a comprehensive Python library for supervised and unsupervised machine learning, including classification, regression, clustering, dimensionality reduction, and model evaluation tools built on NumPy and SciPy.
Install it if you need to train, evaluate, or deploy supervised or unsupervised learning models.
dill extends Python's pickle module to serialize and deserialize a much wider range of Python objects, including functions, lambdas, classes, and interpreter sessions, to byte streams for storage or network transmission.
Multiprocess is an enhanced fork of Python's standard multiprocessing library that uses dill for better serialization, allowing you to spawn processes with a threading-like API and share complex objects between them.
Install it if you use multiprocessing and encounter pickle serialization limits with lambdas or complex objects.
See also polars · polars-runtime-32 · polars-runtime-64 · polars-runtime-compat · polars-lts-cpu · narwhals · lithops · dataframe-api-compat · itables · grizz