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Scale to any backend, on any cloud, with a single line of code

import covalent as ct # Ship your task to any device @ct.electron("awsbatch") def training(model,X,y): return model.train(X,y) # Combine them however you want ! @ct.lattice def compare(X,y): model=... c_model=training(model,X,y) return plot_performance(c_model,q_model) run_id=ct.dispatch(compare)(X_train,y_train)

Supported Clouds

How it Works


  • Decorate any python function to convert it into a task / workflow
  • Work locally and with the IDE of your choice, including Jupyter notebooks
  • Workflows can be cross-language and cross-platform
import covalent as ct @ct.electron def spacer(word): return word+" " @ct.electron def join(*a): return "".join(a) @ct.lattice def workflow(name): return join(spacer("hello"),spacer(name)) +" !!" id=ct.dispatch(workflow)(name="Shore") ct.get_result(id,wait=True)


  • Prototype your workflows locally and then run them anywhere
  • Switch between hardware types and clouds with a single line of code
  • Out of the box cloud bursting capabilities


  • Visually engage with your workflows directly with Covalent’s browser-based UI
  • Get live status updates for tasks, workflows and hardware

Collect Results

  • Automatically add checkpoints to your workflows
  • Keep track of and organize your experiments using the Covalent UI
  • Automatically package experiments that can easily be shared/reproduced

Covalent supports a growing list of languages and executors

See the full list of plugins here. Looking for something more specific? You can get started creating your own here.

Choose from Covalent’s flexible deployment options


Rapid Deployment

Seamlessly transition from experiments to production within Covalent

API for High Compute

Expose functions and solvers from generative AI to quantum using Covalent

Intuitive Monitoring

Oversee workflows across clouds with real-time tracking via a user-friendly UI

Unified Cloud Computing

Switch between cloud providers and integrate diverse computing paradigms

Adaptive Workflows

Real-time workflow adjustments with dynamic resource allocation

Seamless Orchestration

From local prototypes to multi-cloud applications, managed with ease

Industry & Vertical Agnostic

Machine Learning

Rapidly prototype computationally intensive ML models, and seamlessly scale to any hardware or cloud – all from your Python environment.

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Bio & Life Sciences

Prototype and scale workflows from image analysis to drug discovery across multiple cloud platforms effortlessly from a Jupyter notebook

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Build dynamic workflows that scale with the size of molecular systems and flexibly match software to hardware resources.

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Quantum Computing

Compare emerging quantum platforms and algorithms with ease. Covalent integrates quantum into the broader compute ecosystem and lowers the barrier to entry for quantum-curious organizations

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Scientific Computing

Rapidly iterate over models, collect and organize large datasets, and collaborate with other scientists. Covalent reduces the time from hypothesis to publication by eliminating operational overheads.

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Deploy and dynamically balance workloads across federated HPC clusters without needing to interact with individual file systems or schedulers.

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Yes. Covalent is an Open Source-first platform that anyone can use for free including self-hosting in their own cloud.

Covalent is used by data scientists and computational scientists across industries and disciplines, wherever there is a necessity for running experiments on advanced computing hardware such as GPUs, CPUs, QPUs and others.

Yes, we encourage people to host Covalent Open Source on their own cloud. Learn how here.

While most other workflow tools are focused on the “when” (when to schedule an event), Covalent is focused on the “where” (what type of hardware, and in what modality – cloud vs. on-prem).

The best places to ask questions about Covalent are on our Github Discussions board or in our Slack channel.

Join the Covalent community