Packaging & Distribution
Seamlessly deploy and maintain open source software in well-managed environments
Modern software moves fast, but production environments demand stability, reproducibility, and long-term support. Teams often prototype quickly, only to encounter failures when research code reaches production due to fragile builds, inconsistent environments, or unmanaged dependencies.
Quansight helps organizations package, distribute, and maintain Python and native software so it works reliably across platforms, architectures, and deployment contexts, combining deep technical expertise with active leadership in the open source ecosystems you depend on.
What We Do
We partner with engineering, research, and platform teams to design robust, maintainable packaging and build systems across the Python and scientific computing stack.
> Build and distribute Python and native code
C/C++, Rust, and more, for multiple operating systems and architectures, including x86 and ARM
> Package software for PyPI and conda
Using modern and legacy build backends such as autotools, CMake, and Meson
> Design and maintain long-lived build pipelines
Ensuring packaging metadata and infrastructure evolve safely over time
> Integrate supply chain security best practices
Reducing dependency risk and protect against tampering
> Evaluate and migrate package management strategies
Including transitions to emerging tools (e.g., uv, pixi)
> Collaborate with upstream package manager maintainers
Assessing new features, resolve bugs, and improve day-to-day workflows
> Prepare and submit enhancement proposals
PEPs and equivalent governance processes when ecosystem-level changes are required
Quansight for Packaging & Environment Management
At Quansight, our open source experts and decision-makers include steering committee members and maintainers of core projects. Additionally, we created conda-store, an open source tool focused on collaborative data science environments.
Conda
We have Steering Council Members & Contributors on our team. Conda provides package, dependency, and environment management for any language.
Conda-Forge
We employ Core Team Members and Contributors. Conda-forge is a community-led collection of recipes, build infrastructure, and distributions for the conda package manager.
pypackaging-native
Quansight is the creator of this resource, which addresses the unique challenges of packaging Python projects and provides key insights and references.
Meson-Python
As maintainers, we leverage the Meson build system to offer robust build backends for Python packages. Our team members successfully transitioned several key projects to use this system.
Python Packaging
We help maintain critical packages under the Python Packaging Authority (PyPA), contribute to the authorship of Python Enhancement Proposals (PEPs), and actively drive the evolution of Python packaging standards.
“Point72 and Cubist are committed to open source and to sponsoring organizations such as PyData and the Python Software Foundation. We are excited about the opportunities our partnership with Quansight may provide to solve packaging problems strategically and sustainably both for our own research teams and for conda-forge users generally.”
Proactive Reproducibility — Same Data, Same Code, Same Results
Reproducibility cannot be an afterthought. Achieving it requires clearly defined research and production environments that remain adaptable across various contexts. This proactive integration into the development process addresses challenges such as rapidly evolving libraries and complex workflows.
By setting clear objectives and identifying potential risks, we help practitioners create robust systems that enhance the reliability and credibility of their work.
Common Barriers to Reproducibility:
Diverse Environments
Practitioners often work in varied environments, making it difficult to ensure consistent results across different setups
Library Changes
Rapid library evolution can introduce breaking changes that compromise reproducibility.
Lack of Control
IT departments may enforce strict control over environments, conflicting with the flexibility needed by data scientists.
Complex Workflows
Existing workflows often do not support reproducibility, making it challenging to replicate results or share work.
Get in Touch
Ready to take the next step in your open source journey? We’d love to hear from you.
