---
id: lithops
version: "3.7.0"
license: unclear
license_treatment: permissive
maintenance: active
---

# lithops — Lithops lets you transparently run your Python applications in the Cloud

License: permissive · Maintenance: active · Downloads: 233.1K/mo

## What it is and what it does

Lithops abstracts away cloud infrastructure management by letting you write ordinary Python functions and run them in parallel across multiple cloud providers, Kubernetes clusters, or on-premise systems without code changes. It supports both a Futures-style API (map/reduce patterns) and a Multiprocessing API (drop-in replacement for Python's multiprocessing.Pool), plus cloud-native storage backends for reading and writing data. The framework handles deployment, scaling, and orchestration transparently—you configure a compute backend (Lambda, Cloud Run, Batch, Kubernetes, etc.) and a storage backend (S3, GCS, Azure Blob, etc.), then submit your workload.

The primary use case is highly parallel workloads: Monte Carlo simulations, machine learning preprocessing, geospatial analytics, and metabolomics. You can optimize for speed using serverless functions that launch hundreds of tasks in milliseconds, or for cost by running the same code on batch systems with spot instances. It supports major cloud providers (AWS, Google Cloud, Azure, IBM, Aliyun, Oracle) and on-premise platforms (Kubernetes, OpenStack, Ceph, MinIO), making it useful for hybrid or multi-cloud scenarios where code portability matters.

Use it for:

- Run Monte Carlo simulations or financial modeling across hundreds of parallel functions on AWS Lambda without managing infrastructure.
- Process large geospatial or metabolomics datasets by distributing computation across Kubernetes pods or cloud VMs.
- Migrate existing Python multiprocessing code to serverless or cloud backends by swapping Pool() with lithops.multiprocessing.Pool().
- Execute the same data pipeline on multiple cloud providers (AWS, GCP, Azure) by changing configuration without rewriting code.
- Reduce costs for batch workloads by running them on Spot Instances via AWS Batch while keeping the same Python code.

## Worth the install?

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

Lithops is a Python framework for running unmodified code at scale across cloud providers, HPC, and on-premise platforms using serverless compute and storage backends.

Yes, if you have highly parallel workloads and want to avoid cloud-specific code. The framework is actively maintained, has no known vulnerabilities, and supports a wide range of backends. Install friction is low. The main gotcha is that you must configure a compute and storage backend before running anything; it is not a drop-in replacement for local parallelism without setup. Best for teams already using cloud or Kubernetes and wanting code portability across providers.

## Install

pip install lithops
uv add lithops
poetry add lithops

## Installing lithops

Before you install: Low friction installation with a pure-Python wheel. Active maintenance with recent releases; last commit 2026-07-17. Depends on 12 runtime packages including Click, PyYAML, cloudpickle, paramiko, and pika—all standard libraries for CLI, configuration, serialization, SSH, and messaging.

License in practice: Licensed under Apache Software License (permissive). No restrictions on commercial or proprietary use; you may modify and redistribute under the same license terms.

Quickstart:

pip install lithops

from lithops import FunctionExecutor

def double(i):
    return i * 2

with FunctionExecutor() as fexec:
    f = fexec.map(double, [1, 2, 3, 4])
    print(f.result())

Requires configuration of at least one compute backend (e.g., AWS Lambda, Google Cloud Run, Kubernetes) and corresponding storage backend before execution; localhost backends available for testing.

Verify before relying:

- Whether the 12 runtime dependencies introduce any transitive security concerns beyond the zero known vulnerabilities in lithops itself.
- Performance characteristics and cost implications when switching between backends (e.g., Lambda vs. Batch vs. on-premise).
- Maturity and stability of less-common backends (Aliyun, Oracle, OpenWhisk, Singularity) relative to AWS/GCP/Azure.

## Package facts

- License: not declared (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 233.1K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

distributed computing framework python, serverless multi-cloud execution, parallel workload orchestration, cloud-agnostic function execution, large-scale data processing, python code at scale, kubernetes and lambda abstraction, serverless, multi-cloud, parallel-computing

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