Consistent Environments Across Your Team
No more "works on my machine." Every data scientist gets the same reproducible environment from day one. No manual setup, no dependency conflicts.
Deploy Through Code, Not Platform Constraints
Turn notebooks into production systems. APIs, scheduled jobs or applications. Hand off to engineering when needed. No templates, no rigid platforms.
Track, Collaborate, and Version Your Work
Version control built in. Branch, commit, and merge without leaving. Notebooks are reproducible and deterministic. Share work that actually runs.
Frequently Asked Questions
Yes, you can upload your existing notebooks into Zerve. Zerve understands cell dependencies, safely parallelizes execution where possible, and shows you the execution plan before running, without changing your code.
By default, your code runs on Zerve-managed cloud infrastructure. You can also run everything inside your own cloud or on-prem environment, keeping execution and data fully within your network when required.
Zerve runs in the cloud, not as a local-only app. You still get fast, interactive execution that feels like working locally, while benefiting from scalable compute and built-in collaboration. Zerve can be self-hosted inside your own cloud or on-prem environment.
You can connect your data to Zerve by uploading files, using native integrations with common databases and warehouses or connecting programmatically through code.
Zerve supports Python, R, SQL, GraphQL and PySpark, and lets you use them together in the same project. You can use multiple languages in the same project and pass results between them directly, without exporting data or writing glue code.