G2

Verified User in Pharmaceuticals

Mid-Market (51-1000 emp.)

5/30/2025

"The digital data "swiss-army knife" for your Life Sciences laboratory!"

5/5

What do you like best about Ganymede?

The agility of the Engineering and Implementation teams to deliver meaningful improvements to the capabilities of the SaaS platform. The tool covers so much of the complementary needs in our day-to-day research & development experimental work. They always welcome my feedback and often it turns into bug fixes, platform improvements, and quality of life tweaks. I'm in Ganymede every day and can monitor activities and issues across multiple instrument families and sites all in one place. I'm especially fond of the Virtualization and Modular Analysis components of the tool that allow 'insertion of a human' into an end-to-end workflow instead of making everything only have to go fully scripted from start to finish. Review collected by and hosted on G2.com.

What do you dislike about Ganymede?

There are not nearly as many 'plug-and-play' integrations based off of direct integration with a control software (e.g. CDS, Flow Cytometry software etc.) as I was hoping for. Much of the available nodes/code has been focused on file-based activities and triggers. The most API-rich integrations tend to be driven around Cloud-based platforms rather than Desktop programs that instruments are controlled with. Review collected by and hosted on G2.com.

What problems is Ganymede solving and how is that benefiting you?

Our main use for Ganymede is to provide a controlled, secure, and protected "connected cloud data platform" for our life sciences R&D experimental work. We needed a modern solution that was "future-forward" and cutting edge - rather than "just a cloud object store" with security. It needed to be user-friendly, adaptable to our constantly changing experimentation, and complement our existing suite of instrumentation and other SaaS data platforms (e.g. our ELN, Benchling). This platform (and the implementation team that enabled it) has helped us to increase the volume of data capture, with greater precision and reproducibility (independent of the researcher doing the work) -- all while reducing the overall burden on the researchers to have to manually manage their data flow through their experiment and then into our systems of record. Review collected by and hosted on G2.com.