Welcome to Lithops World!

What is Lithops

Lithops Cloud is an advanced serverless computing framework designed specifically for large-scale data processing and scientific computing tasks. It operates on a serverless architecture, which means it automatically manages infrastructure provisioning and scaling based on workload demands. This ensures optimal resource utilization and cost efficiency without the need for manual intervention. It offers a versalite range of operations for efficient serverless computing, making it a robust choice for complex data processing and computing tasks. The main features that make Lithops outstand other solutions are:

Ease of Deployment and Integration

Multi-Cloud Compatibility

Scalability and Performance

Data Management and Integration

Python-Centric Development

Open Source Community and Support

Cost Efficiency and Accessibility

Lithops Cloud is also suitable for a wide range of use cases including scientific research, big data analytics, IoT data processing, and more. Its robust capabilities in managing distributed computations and handling large datasets make it an ideal choice for organizations seeking scalable and efficient cloud computing solutions.

In conclusion, Lithops Cloud combines ease of deployment, multi-cloud compatibility, scalable performance, Python-centric development, and community-driven support. It empowers developers to build and deploy serverless applications with confidence, driving innovation and efficiency in modern cloud computing environments.

Main functions of Lithops

Function Async Invocation

Batch Processing Operations

Monitoring Functions

Storage Object Management

Utility Functions

What people say about us

Dr. Alberto P. Martí (VP of Open Source Innovation at OpenNebula Systems)

"Lithops is a powerful open source tool for executing Python parallel applications at massive scale on cloud resources using a serverless computing paradigm. Its integration with OpenNebula is going to enable developers to focus on their applications and easily scale them up using resources across the multi-provider cloud-edge continuum without having to deal with the underlying infrastructure."

Theodore Alexandrov (European Molecular Biology Laboratory)

“Replacing Spark with Lithops in our cloud spatial metabolomics platform METASPACE helped us make processing tens of thousands of datasets from thousands of users easily scalable and adaptable to varying load.”

Kyungyong Lee (Kookmin University)

"Using Lithops, we could easily deploy a big-data analytics workload easily across different cloud vendors. Only a few commands using Lithops provides us extremely parallel execution environments promptly using AWS Lambda"

Aaron Call (Ph.D. at Barcelona Supercomputing Center)

"Lithops has increased our use-case data throuhgput to 3x faster ingestion compared to our previous standard version in an HPC environment."

Maciej Malawski (Cyfronet)

"We found Lithops convenient for speeding up our transcriptomics pipelines running in the cloud, thanks to support for bioinformatics data formats."

Eduard Marin (Telefonica Research)

"We tested at Telefonica how Lithops provides very fast scalability and provides automated elastic data processing."

Tom White ([C]Worthy)

"We found Lithops hit the sweet spot for scalable serverless compute. It's dependable, open source, and scales to thousands of serverless containers in seconds when processing real-world multi-dimensional array datasets with Cubed."